Theoretical and Quantitative Approaches to the Study of Mortuary Practice 9781841710051, 9781407351117

Table of contents :
Front Cover
Title Page
Copyright
Table of Contents
LIST OF TABLES
ACKNOWLEDGEMENTS
Chapter 1. Theoretical Approaches to the Study of Mortuary Practice
Chapter 2. The Age Dimension in Mortuary Studies
Chapter 3. The Gender Dimension in Mortuary Studies
Chapter 4. The Horizontal Dimension in Mortuary Studies
Chapter 5. The Vertical Dimension in Mortuary Studies
Chapter 6. Multivariate Approaches to the Analysis of Burial Data
Chapter 7. Modelling Artificial Cemeteries and Objectifying Their Analysis
Chapter 8. Results of Analysis of the Model Cemeteries
Conclusion
Bibliography
Appendix 1. Tabular Results
Appendix 2. The Computer Programs: Simburial, Identclus and Identaxis
Appendix 3. Data for Model Cemeteries

Citation preview

BAR S785 1999

McHUGH

THEORETICAL AND QUANTITATIVE APPROACHES TO THE STUDY OF MORTUARY PRACTICE

B A R

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

Feldore McHugh

BAR International Series 785 1999

Published in 2016 by BAR Publishing, Oxford BAR International Series 785 Theoretical and Quantitative Approaches to the Study of Mortuary Practice © F McHugh and the Publisher 1999 The author's moral rights under the 1988 UK Copyright, Designs and Patents Act are hereby expressly asserted. All rights reserved. No part of this work may be copied, reproduced, stored, sold, distributed, scanned, saved in any form of digital format or transmitted in any form digitally, without the written permission of the Publisher.

ISBN 9781841710051 paperback ISBN 9781407351117 e-format DOI https://doi.org/10.30861/9781841710051 A catalogue record for this book is available from the British Library BAR Publishing is the trading name of British Archaeological Reports (Oxford) Ltd. British Archaeological Reports was first incorporated in 1974 to publish the BAR Series, International and British. In 1992 Hadrian Books Ltd became part of the BAR group. This volume was originally published by Archaeopress in conjunction with British Archaeological Reports (Oxford) Ltd / Hadrian Books Ltd, the Series principal publisher, in 1999. This present volume is published by BAR Publishing, 2016.

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PUBLISHING BAR titles are available from:

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TABLE OF CONTENTS LIST OF TABLES ACKNOWLEDGEMENTS

vii XI

CHAPTER 1 THEORETICAL APPROACHES TO THE STUDY OF MORTUARY PRACTICE 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12

Introduction Theoretical approaches to burials over the last 25 years The information obtained from the study of mortuary practices The work of Saxe The work of Binford The work of Tainter Other early theoretical work in mortuary analysis General criticisms of Saxe, Binford and Tainter Post-processual approaches to burial practice Problems with ideological interpretations of burial The use of ethnographic examples in study of mortuary practice Conclusion

1 1 2 4 7

8 11

12 13 16 17 18

CHAPTER 2 THE AGE DIMENSION IN MORTUARY STUDIES 2.1 2.2

2.3 2.4 2.5

Introduction Symbolic markers of age 2 .2 .1 Introduction 2.2.2 Sub-adult symbolism in burial 2.2.3 Symbolic distinctions between adult and sub-adult 2.2.4 Age symbolisms for adults 2.2.5 Symbolisms relating to marital status 2.2.6 Conclusion The significance of wealthy child burials The effect of age on wealth accumulation Conclusion

19 19 19 20 22 22 23 24

26 28

CHAPTER 3 THE GENDER DIMENSION IN MORTUARY STUDIES 3.1 3.2

3.3 3.4 3.5 3.6

Introduction Male/female symbolism 3 .2 .1 Introduction 3 .2 .2 Spatial distinctions 3.2.3 Structure and orientation of the grave 3 .2 .4 Ceremonial distinctions 3 .2 .5 Treatment/arrangement of the body 3 .2 .6 Artefactual distinctions Determining relative status of males and females Complicating factors in determining relative male/female status Post-marital residence rules Conclusion

30 30 30 30 31 31 32 32 34 35 37 39

CHAPTER 4 THE HORIZONTAL DIMENSION IN MORTUARY STUDIES 4.1 4.2 4.3 4.4

Introduction The archaeological study of horizontal differentiation Distinguishing horizontal and vertical groups Ethnographic/archaeological examples of horizontal differentiation in burial 4 .4 .1 Introduction 4.4.2 Ritual and ceremonial distinctions

40 40 41 42 42 42 iii

4.5 4.5.2 4.5.3 4.6

4.4.3 Orientation and positioning of the corpse 4.4.4 Spatial distinctions 4.4.5 Structural distinctions 4.4.6 Treatment of the corpse 4.4.7 Artefactual inclusions Other types of horizontal groups Ethnic groups Religious groups Conclusion

43 44 45 46 46 46 47 48 49

CHAPTER 5 THE VERTICAL DIMENSION IN MORTUARY STUDIES 5.1 5.2 5.3 5.4

5.5 5.6 5.7

51 51

Introduction Ceremonial/ritual distinctions Treatment/arrangement of the corpse Spatial differentiation Structural elements in burial Rank symbols Conclusion

54

55 56 57 60

CHAPTER 6 MULTIVARIATE APPROACHES TO THE ANALYSIS OF BURIAL DATA 6.1 6.2 6.3 6.4 6.5 6.6

6.7

6.8

6.9

Introduction The use of multivariate statistical methods in archaeology The utility of quantitative methods in burial analysis Methods used in the analysis of burial data The coding of burial data Cluster analysis 6.6.1 Introduction 6.6.2 Aim of cluster analysis 6.6.3 General problems in the use of cluster analysis 6 .6 .4 Uses of cluster analysis in analysis of burial data 6 .6 .5 Issues in the cluster analysis of burial data 6 .6 .6 Discussion Factor analysis 6.7 .1 Factor analysis: introduction 6.7 .2 Problems in factor analysis 6 .7.3 The use of factor analysis in burial studies 6. 7 .4 Discussion Correspondence analysis 6.8.1 Correspondence analysis: introduction 6.8.2 Interpretation of the results of correspondence analysis 6.8.3 Comparisons between principal components and correspondence analysis 6.8.4 The use of correspondence analysis in burial studies 6.8.5 Problems with correspondence analysis Conclusion

CHAPTER 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10

62 62 62 63 63 64 64 64 65 66 67 71 73 73 73 77

80 80 80 81 81 82 83 84

MODELLING ARTIFICIAL CEMETERIES AND OBJECTIFYING THEIR ANALYSIS

Introduction Modelling burial data Types of rules structuring the mortuary domain Interpretative approaches in some recent burial analyses Aim of simulated models Outline of models Outline of analysis procedure Objectifying the results of multivariate analysis Structure of the computer programs Conclusion

iv

85 85 86 87

90 92 96 97 102 104

CHAPTER 8 RESULTS OF ANALYSIS OF THE MODEL CEMETERIES 8.1 8.2 8.3 8.4

8.5

8.6

8.7

8.8

8.9

8.10

8.11

8.12 8.13 8.14

Brief synopsis of models Brief synopsis of quantitative approach Preliminary investigation 8 .3 .1 Cluster analysis results 8 .3 .2 Correspondence/ Principal components analysis results Analysis of model IA (egalitarian society) 8 .4 .1 Cluster analyses results 8.4.2 Correspondence analyses results 8.4.3 Principal components analyses results Analysis of model 2A (ranked society) 8 .5 .1 Cluster analysis results 8 .5 .2 Correspondence analysis results 8 .5 .3 Principal components analyses results Analysis of model 3A (ranked, superordinate rank has subordinate items) 8 .6 .1 Cluster analysis results 8 .6 .2 Correspondence analyses results 8 .6 .3 Principal components analyses results Analysis of model 4 (reduced probabilities) 8. 7 .1 Cluster analyses of model 4A (egalitarian, artefact probabilities reduced to 70%) 8.7 .2 Cluster analyses of model 4B (egalitarian, artefact probabilities reduced to 40%) 8 .7.3 Cluster analysis of model 4C (ranked, artefact probabilities reduced to 70%) 8 .7.4 Cluster analysis of model 4D (ranked, artefact probabilities reduced to 40%) 8 .7.5 Correspondence analyses of model 4A 8 .7 .6 Principal components analyses of model 4A 8. 7. 7 Correspondence analyses of model 4B 8 .7 .8 Principal components analyses of model 4B 8. 7 .9 Correspondence analyses of model 4C 8.7 .10 Principal components analyses of model 4C 8 .7 .11 Correspondence analyses of model 4D 8 .7 .12 Principal components analyses of model 4D Analysis of model 5A (egalitarian, artefacts shared with different social identities) 8 .8 .1 Cluster analysis results 8 .8 .2 Correspondence analyses results 8 .8 .3 Principal components analyses results Analysis of model 5B (ranked, artefacts shared with different social identities) 8 .9 .1 Cluster analysis results 8 .9 .2 Correspondence analyses results 8 .9 .3 Principal components analyses results Analysis of model 6A (three ranks) 8.10.1 Cluster analysis results 8.10.2 Correspondence analyses results 8.10.3 Principal components analyses results Analysis of model 7 (random noise) 8 .11.1 Cluster analysis of model 7A 8 .11.2 Cluster analysis of model 7B 8 .11.3 Cluster analysis of model 7C 8 .11.4 Cluster analysis of model 7D 8 .11.5 Correspondence and principal components analyses of model 7 Analysis of model 8A (egalitarian, females 25%) 8.12.1 Cluster analysis of model 8A 8 .12 .2 Correspondence/principal components analysis of model 8A Analysis of model 8B (ranked, rank 1 50%) 8.13.1 Cluster analysis of model 8B 8 .13 .2 Correspondence/principal components analysis of model 8B Analysis of model 9 (Ramsauer adult females) 8 .14 .1 Cluster analysis results

V

105 105 106 106 106 107 107 107 108 109 109 110 110 112 112 113 114 115 115 116 116 117 117 117 118 119 119 120 121 122 123 123 124 124 125 125 125 126 127 127 128 128 130 130 131 131 132 132 132 132 133 133 133 133 134 134

8 .15 8 .16

8.14.2 Correspondence analysis/PCA results Discussion Future work and Conclusion

134 135 140

CONCLUSION

142

BIBLIOGRAPHY

145

APPENDIX 1 Tabular results

157

APPENDIX 2

The computer programs: SIMBURIAL, IDENTCLUS and ID ENT AXIS

271

APPENDIX 3

Data for the model cemeteries

305

vi

LIST OF TABLES TABLE

PAGE

7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10

157 157 157 158 158 159 160 160 160 161 161 162 162 163 163 164 164 165 166 167 167 168 168 169 169 170 171 171 172 173 173 173 174 174 175 176 176 176 177 177 178 179 179

7.11 7.12 7.13 7.14 7.15 7.16 7.17 7.18 7.19 7.20 7.21 7.22 7.23 7.24 7.25 7.26 7.27 7.28 7.29 7.30 7.31 7.32 7.33 7.34 7.35 7.36 7.37 7.38 8.1 8.2 8.3 8.4 8.5 8.6 8.7

8.8 8.9 8.10

8.11 8.12 8.13

Artefacts in simulated cemeteries and analysis labels Rules for artefact distribution in model IA Summary of rule occurrence in model IA Rules for artefact distribution in model 2A Summary of rule occurrence in model 2A Rules for artefact distribution in model 3A Summary of rule occurrence in model 3A Rules for artefact distribution in model 4A Summary of rule occurrence in model 4A Rules for artefact distribution in model 4B Summary of rule occurrence in model 4B Rules for artefact distribution in model 4C Summary of rule occurrence in model 4C Rules for artefact distribution in model 4D Summary of rule occurrence in model 4D Rules for artefact distribution in model 5A Summary of rule occurrence in model 5A Rules for artefact distribution in model 5B Summary of rule occurrence in model 5B Rules for artefact distribution in model 6A Summary of rule occurrence in model 6A Rules for artefact distribution in model 7A Summary of rule occurrence in model 7A Rules for artefact distribution in model 7B Summary of rule occurrence in model 7B Rules for artefact distribution in model 7C Summary of rule occurrence in model 7C Rules for artefact distribution in model 7D Summary of rule occurrence in model 7D Rules for artefact distribution in model 8A Summary of rule occurrence in model 8A Rules for artefact distribution in model 8B Summary of rule occurrence in model 8B Rules for artefact distribution in model 9 (Ramsauer) Summary of rule occurrence in model 9 (Ramsauer, repetition 1) Highest Jaccard values in clustering of random data Highest Jaccard values in clustering of real model IA data Highest point-biserial correlations in PCA/CA of random data Rules for artefact distribution in preliminary model Summary of rule occurrence in preliminary model (50 burials, random seed 1) Summary of rule occurrence in preliminary model (100 burials, random seed 1) Summary of rule occurrence in preliminary model (200 burials, random seed 1) Summary of rule occurrence in preliminary model (400 burials, random seed 1) Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 1) Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 2) Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 3) Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 4) Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 5) Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 1) Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 2) Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 3)

vii

180 180 181 181 181 182 182 183

TABLE 8.14 8.15 8.16 8.17 8.18 8.19 8.20 8.21 8.22 8.23 8.24 8.25 8.26 8.27 8.28 8.29 8.30 8.31 8.32 8.33 8.34 8.35 8.36 8.37 8.38 8.39 8.40 8.41 8.42 8.43 8.44 8.45 8.46 8.47 8.48 8.49 8.50 8.51 8.51 8.53 8.54

PAGE

Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 4) Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 5) Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 1) Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 2) Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 3) Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 4) Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 5) Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 1) Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 2) Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 3) Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 4) Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 5) Summary of average Jaccard values for clustering of preliminary model (5 repetitions, 4 sample sizes) Summary of highest correlations in PCA/CA analyses of preliminary model (all samples) Average point-biserial correlations for social groups in PCA/CA analyses of preliminary model (4 sample sizes) Top Jaccard values for Ward's method clustering of model IA Top Jaccard values for average linkage clustering of model IA Top Jaccard values for monothetic divisive clustering of model IA Summary of top Jaccard values for clustering of model IA Highest correlations on axes for correspondence analysis of model IA Highest correlations on axes for detrended correspondence analysis of model IA Highest correlations on axes for unrotated PCA (covariance matrix) of model IA Highest correlations on axes for unrotated PCA (correlation matrix) of model IA Highest correlations on axes for Varimax-rotated PCA of model IA Highest correlations on axes for Oblimin-rotated PCA of model IA Summary of highest correlations in PCA/CA analyses of model IA Top Jaccard values for Ward's method clustering of model 2A Top Jaccard values for average linkage clustering of model 2A Top Jaccard values for monothetic divisive clustering of model 2A Summary of top Jaccard values for clustering of model 2A Highest correlations on axes for correspondence analysis of model 2A Highest correlations on axes for detrended correspondence analysis of model 2A Highest correlations on axes for unrotated PCA (covariance matrix) of model 2A Highest correlations on axes for unrotated PCA (correlation matrix) of model 2A Highest correlations on axes for V arimax -rotated PCA of model 2A Highest correlations on axes for Oblimin-rotated PCA of model 2A Summary of highest correlations in PCA/CA analyses of model 2A Top Jaccard values for Ward's method clustering of model 3A Top Jaccard values for average linkage clustering of model 3A Top Jaccard values for monothetic divisive clustering of model 3A Summary of top Jaccard values for clustering of model 3A viii

183 183 184 184 185 185 185 186 186 187 187 187 188 189 191 191 192 192 192 193 193 193 194 194 195 196 196 196 197 197 198 199 199 200 200 201 202 203 203 204 204

TABLE 8.55 8.56 8.57 8.58 8.59 8.60 8.61 8.62 8.63 8.64 8.65 8.66 8.67 8.68 8.69 8.70 8.71 8.72 8.73 8.74 8.75 8.76 8.77 8.78 8.79 8.80 8.81 8.82 8.83 8.84 8.85 8.86 8.87 8.88 8.89 8.90 8.91 8.92 8.93 8.94 8.95 8.96 8.97 8.98 8.99 8.100 8.101 8.102 8.103 8.104 8.105 8.106 8.107 8.108 8.109 8.110 8.111 8.112 8.113 8.114 8.115 8.116 8.117 8.118

PAGE

Highest correlations on axes for correspondence analysis of model 3A Highest correlations on axes for detrended correspondence analysis of model 3A Highest correlations on axes for unrotated PCA (covariance matrix) of model 3A Highest correlations on axes for unrotated PCA (correlation matrix) of model 3A Highest correlations on axes for Varimax-rotated PCA of model 3A Highest correlations on axes for Oblimin-rotated PCA of model 3A Summary of highest correlations in PCA/CA analyses of model 3A Top Jaccard values for Ward's method clustering of model 4A Top Jaccard values for average linkage clustering of model 4A Top Jaccard values for monothetic divisive clustering of model 4A Surnrnary of top Jaccard values for clustering of model 4A Top Jaccard values for Ward's method clustering of model 4B Top Jaccard values for average linkage clustering of model 4B Top Jaccard values for monothetic divisive clustering of model 4B Surnrnary of top Jaccard values for clustering of model 4B Top Jaccard values for Ward's method clustering of model 4C Top Jaccard values for average linkage clustering of model 4C Top Jaccard values for monothetic divisive clustering of model 4C Surnrnary of top Jaccard values for clustering of model 4C Top Jaccard values for Ward's method clustering of model 4D Top Jaccard values for average linkage clustering of model 4D Top Jaccard values for monothetic divisive clustering of model 4D Surnrnary of top Jaccard values for clustering of model 4D Highest correlations on axes for correspondence analysis of model 4A Highest correlations on axes for detrended correspondence analysis of model 4A Highest correlations on axes for unrotated PCA (covariance matrix) of model 4A Highest correlations on axes for unrotated PCA (correlation matrix) of model 4A Highest correlations on axes for Varimax-rotated PCA of model 4A Highest correlations on axes for Oblirnin-rotated PCA of model 4A Summary of highest correlations in PCA/CA analyses of model 4A Highest correlations on axes for correspondence analysis of model 4B Highest correlations on axes for detrended correspondence analysis of model 4B Highest correlations on axes for unrotated PCA (covariance matrix) of model 4B Highest correlations on axes for unrotated PCA (correlation matrix) of model 4B Highest correlations on axes for Varimax-rotated PCA of model 4B Highest correlations on axes for Oblirnin-rotated PCA of model 4B Summary of highest correlations in PCA/CA analyses of model 4B Highest correlations on axes for correspondence analysis of model 4C Highest correlations on axes for detrended correspondence analysis of model 4C Highest correlations on axes for unrotated PCA (covariance matrix) of model 4C Highest correlations on axes for unrotated PCA (correlation matrix) of model 4C Highest correlations on axes for Varimax-rotated PCA of model 4C Highest correlations on axes for Oblirnin-rotated PCA of model 4C Summary of highest correlations in PCA/CA analyses of model 4C Highest correlations on axes for correspondence analysis of model 4D Highest correlations on axes for detrended correspondence analysis of model 4D Highest correlations on axes for unrotated PCA (covariance matrix) of model 4D Highest correlations on axes for unrotated PCA (correlation matrix) of model 4D Highest correlations on axes for Varimax-rotated PCA of model 4D Highest correlations on axes for Oblimin-rotated PCA of model 4D Summary of highest correlations in PCA/CA analyses of model 4D Top Jaccard values for Ward's method clustering of model SA Top Jaccard values for average linkage clustering of model SA Top Jaccard values for monothetic divisive clustering of model SA Surnrnary of top Jaccard values for clustering of model SA Highest correlations on axes for correspondence analysis of model SA Highest correlations on axes for detrended correspondence analysis of model SA Highest correlations on axes for unrotated PCA (covariance matrix) of model SA Highest correlations on axes for unrotated PCA (correlation matrix) of model SA Highest correlations on axes for Varimax-rotated PCA of model SA Highest correlations on axes for Oblimin-rotated PCA of model SA Summary of highest correlations in PCA/CA analyses of model SA Top Jaccard values for Ward's method clustering of model SB Top Jaccard values for average linkage clustering of model SB ix

205 205 206 207 207 208 209 209 210 210 210 211 211 211 212 212 213 213 213 214 214 215 215 216 216 217 217 218 218 219 219 220 220 220 221 222 222 223 223 224 224 225 226 227 227 228 229 229 230 231 232 233 233 234 234 234 235 235 236 236 237 237 237 238

TABLE

PAGE

8.119 8.120 8.121 8.122 8.123 8.124 8.125 8.126 8.127 8.128 8.129 8.130 8.131 8.132 8.133 8.134 8.135 8.136 8.137 8.138 8.139 8.140 8.141 8.142 8.143 8.144 8.145 8.146 8.147 8.148 8.149 8.150 8.151 8.152 8.153 8.154 8.155 8.156 8.157 8.158 8.159 8.160 8.161 8.162 8.163 8.164 8.165 8.166 8.167 8.168 8.169 8.170 8.171a 8.171b 8.172 8.173 8.174 8.175 8.176 8.177

238 239 239 240 240 241 242 242 243 244 244 245 245 246 247 247 248 249 250 250 251 252 252 252 253 253 253 254 254 254 255 255 256 256 257 257 258 258 259 259 260 260 261 261 262 263 263 263 263 263 264 264 265 265 266 266 267 268 269 270

Top Jaccard values for monothetic divisive clustering of model SB Summary of top Jaccard values for clustering of model SB Highest correlations on axes for correspondence analysis of model SB Highest correlations on axes for detrended correspondence analysis of model SB Highest correlations on axes for unrotated PCA (covariance matrix) of model SB Highest correlations on axes for unrotated PCA (correlation matrix) of model SB Highest correlations on axes for V arimax-rotated PCA of model SB Highest correlations on axes for Oblirnin-rotated PCA of model SB Summary of highest correlations in PCA/CA analyses of model SB Top Jaccard values for Ward's method clustering of model 6A Top Jaccard values for average linkage clustering of model 6A Top Jaccard values for monothetic divisive clustering of model 6A Summary of top Jaccard values for clustering of model 6A Highest correlations on axes for correspondence analysis of model 6A Highest correlations on axes for detrended correspondence analysis of model 6A Highest correlations on axes for unrotated PCA (covariance matrix) of model 6A Highest correlations on axes for unrotated PCA (correlation matrix) of model 6A Highest correlations on axes for V arimax -rotated PCA of model 6A Highest correlations on axes for Oblirnin-rotated PCA of model 6A Summary of highest correlations in PCA/CA analyses of model 6A Top Jaccard values for Ward's method clustering of model 7A Top Jaccard values for average linkage clustering of model 7A Top Jaccard values for monothetic divisive clustering of model 7A Summary of top Jaccard values for clustering of model 7A Top Jaccard values for Ward's method clustering of model 7B Top Jaccard values for average linkage clustering of model 7B Top Jaccard values for monothetic divisive clustering of model 7B Summary of top Jaccard values for clustering of model 7B Top Jaccard values for Ward's method clustering of model 7C Top Jaccard values for average linkage clustering of model 7C Top Jaccard values for monothetic divisive clustering of model 7C Summary of top Jaccard values for clustering of model 7C Top Jaccard values for Ward's method clustering of model 7D Top Jaccard values for average linkage clustering of model 7D Top Jaccard values for monothetic divisive clustering of model 7D Summary of top Jaccard values for clustering of model 7D Summary of highest correlations in PCA/CA analyses of model 7A Summary of highest correlations in PCA/CA analyses of model 7B Summary of highest correlations in PCA/CA analyses of model 7C Summary of highest correlations in PCA/CA analyses of model 7D Summary of top Jaccard values for clustering of model 8A Summary of highest correlations in PCA/CA analyses of model 8A Summary of top Jaccard values for clustering of model 8B Summary of highest correlations in PCA/CA analyses of model 8B Summary of top Jaccard values for clustering of Ramsauer model (random seed 1) Summary of top Jaccard values for clustering of Ramsauer model (random seed 2) Summary of top Jaccard values for clustering of Ramsauer model (random seed 3) Summary of top Jaccard values for clustering of Ramsauer model (random seed 4) Summary of top Jaccard values for clustering of Ramsauer model (random seed 5) Summary of average Jaccard values for clustering of Ramsauer Highest correlations for correspondence analysis of Ramsauer model (repetition 1) Highest correlations for obliquely-rotated PCA of Ramsauer model (repetition 1) Summary of highest correlations in PCA/CA analyses of Ramsauer model (5 repetitions) Average point-biserial correlations in PCA/CA analysis of Ramsauer model Number of high Jaccard values with different clustering methods across all models Highest point-biserial correlations with different methods across all models Highest Jaccard values across all egalitarian models Highest point-biserial correlations across egalitarian models Highest Jaccard values across all ranked models Highest point-biserial correlations across all ranked models

X

ACKNOWLEDGEMENTS I would particularly like to thank my supervisor Dr. Mallory for help and guidance over the past number of years; Dr. Steve O'Brien for a considerable amount of work on early simulation development; Professor Breen of Q.U.B sociology department for reading suggestions; Dr. Mike Baxter, Professor Julian Orford and Dr. Gordon Cran for helpful statistical advice; and Dr. Gerry McCormick and Dr. John Powers for advice on simulation models. The following have also made helpful suggestions, particularly with regard to the statistical aspects of the thesis: Dr. Andrew Chamberlain, Dr. John Robb, Professor Fionn Murtagh, Dr. Bob Laxton, Tony Greenfield, Jose Binongo, Juan Barcleo, Simon Williams, Dr. David Dowe, N. Sriram, Dr. Pierre Philippe, and Dr. Greg Deets. Thanks are also extended to Mr. Martin Stroud and Pete McCready for their computer expertise, Gail Matthews for useful advice, and an especially big thanks goes to my parents for patiently enduring (and financing) yet another seemingly endless and incomprehensible project. This work is dedicated to them.

xi

xii

Chapter 1 Theoretical Approaches to the Study of Mortuary Practice 1.1 Introduction

thing is possible, or even desirable. Earlier work, such as that by Saxe (1970) and Binford (1971), attempted to define cross-cultural "laws", viewing burial as a faithful reproduction of social organization. More recent studies, such as Shanks and Tilley (1982), S. J. Sherman (1982), Bradley (1984), Whittle (1988) and Barrett (1990), to name a few, have concentrated on historical contexts for explanation, and particularly the importance of ideological processes in burial. These approaches have followed on from Hodder's emphasis on the potential burial practice has for "distorting, obscuring, hiding or inverting particular forms of social relationships" (1982c: 152), and the need to take into account the cultural context, rather than attempt to define general cross-cultural laws:

Joachim Whaley, in the introduction to "Mirrors of Mortality" (1981), comments on the sheer volume of evidence available to those studying contemporary and historical attitudes to death: The study of attitudes to death involves the analysis of death mythologies in art and literature, of medical practice and beliefs, of popular superstitions and folklore, of burial rites and customs, of ecclesiastical laws and structures, of civil law and custom, in short of virtually all aspects of human activity and most forms of human expression (Whaley 1981: 4). Despite, or perhaps because of, all these diverse sources of information, contemporary analyses of changing attitudes to death have illustrated the sheer complexities, and difficulties of reconstruction, over the briefest of times and the smallest of geographical areas. The archaeologist, in his examination of the "meaning" of death, is generally denied all these sources of information, with the usual (but not always) exception of the burial rite; even then he is limited to what material aspects have survived in the archaeological record. It is perhaps because the archaeologist has such a one-sided view of the representation of death in past cultures that the theories relating to the interpretation of the material remains have similarly tended to be one-sided, typically focusing upon a particular prevailing "attitude" within the society, rather than encompassing the multi-facteted attitudes which undoubtedly operated at any point in time.

When individuals act socially, and represent their actions to others, they necessarily do so within a framework of meaning, and this framework is relative and historically constructed (Hodder 1984: 53). Current theories more closely address the true complexity of processes inherent in burial practice. A major point of departure from the work of Saxe and Binford is the fact that burials are no longer seen merely in a passive respect, as having fossilised the social system at a particular point in space-time, but rather are viewed as very much part of the dynamic processes that created and modified social structure through time. This approach owes much to the sociological theory of structuration (e.g. Giddens 1984), which is specifically referred to in various mortuary analyses such as Garwood (1991), Morris (1992: 6) and Mizoguchi (1992: 40). This theory views social structure as being constantly reproduced by the actions and attitudes of the humans involved in it. Many researchers (e.g. Bloch 1977, S .J. Sherman 1982, Shanks and Tilley 1982, Taylor 1989, Sillar 1992) have emphasised the fact that burial provides an ideal opportunity to make political dominance seem legitimate and "natural", through ancestral association, as though it had always been the case. This obviously depends on the society's attitudes to ancestral relations. A striking example of a modem ideological attitude towards the dead could be seen in the recent Holocaust film "Schindler's List", where one of the most memorable images was the use of thousands of up-rooted Jewish grave stones to pave the road to a labour camp. In this case the fundamental right-of-existence for an entire people was being ideologically denied by removing even ancestral traces, and transposing them to a clear context of subjugation.

This chapter will examine the various theoretical approaches that have been followed in the study of burial. Succeeding chapters will then examine the social dimensions that find reflection in the mortuary domain. The second part of this dissertation is concerned with the multivariate analysis of burial data, to uncover such dimensions. The use of such multivariate methods has been quite controversial, and it is suggested here that the only way in which their usefulness can be determined is by the use of artificially-constructed burial data sets. By using such an approach it is possible to completely objectify the results that are obtained, and thus quantify how well the methods perform in a variety of defined situations.

1.2

Theoretical approaches last 25 years

to burials

over the

Economic factors, particularly the circulation of goods, are also emphasised in current approaches, as a factor creating change, not necessarily in association with changes in social structure (e.g. S.J. Sherman 1982; Bradley 1984; Chapman 1987; O'Shea 1995). A further point of departure from the Saxe-Binford approach is the realisation that burial need not automatically be a context for status display, but other contexts may fulfil this role (e.g. Whalen 1983; Bradley 1984; Bintliff 1984; Chapman 1991; Robb 1994). Rather, burial may become a context for status display in particular circumstances, particularly if some instability exists in the status system. If positions are less fixed in the social system,

The past quarter century or so has been a particularly turbulent period for the archaeological interpretation of mortuary practices. The variety of theories forwarded, and their changing popularity, has rivalled the complexities of the subject they seek to explain. With such variations apparent in the burial record through time and space, and so many factors interacting in its production, it seems unlikely that one single theory will ever be sufficient as explanation. O'Shea (1984) has commented on "the proliferation of ad hoc assumptions" (3), and the lack of a "central thread" to mortuary studies, and indeed one should question if such a

1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

then burial may become a focus for competition, as various authors such as Hedeager (1992) and Randsborg (1982) have noted. The reverse is also true, of course; Fritz (1981) noted how the establishment of a constitutional monarchy resulted in declined use of symbols at royal funerals.

forming cross-cultural rules, but there was at least an interest in the patternings seen in the burial data, whereas ideological approaches seem to show less concern with the social meaning of such patterns. A further point in favour of the Saxe-Binford perspective is a concern with the variety of social dimensions (age, sex, vertical, horizontal) that may be symbolised in burial, while ideological approaches inherently focus upon the vertical dimension.

Current focus on ideological aspects of burial are valid, but there is danger in concentrating too much on the misrepresentationalist point of view. Tarlow (1992) pointed out that in every-day life the status publicly presented - through the house lived in, clothes worn, car driven etc. - is generally not some "mystification" or idealisation but is close to reality. There is a need to investigate under what circumstances, and why, the burial domain should be any different. Particularly, the likelihood of ideological manipulation affecting a particular set of archaeologicallyrecovered burials needs assessment, rather than assuming that, because it can be demonstrated in some ethnographic instances, mis-representation of social structure will always, or even often, be the case. Part of the problem with the ideological approach is that it tends to view burial evidence in isolation from the other archaeological evidence. Mortuary practice does not provide the sole source of information regarding the social structure of past societies. Rather, it is the best source of such information, and by using other aspects of the archaeological record, such as settlement data, it should be possible to identify situations where ideological manipulation may be present. Another recent, and more useful trend, has been the increasing emphasis on regional explanations for mortuary practices, and an examination of the structure (as opposed to form) of ritual - for example, Morris (1992) considered the spread of inhumation through the Roman Empire not just as a wave of religious belief but, rather, as a series of local adaptations, for different reasons. This kind of approach really gets behind the different "meanings" and significance of burial practice, at an increasingly sophisticated level of understanding.

Intimately associated with an interest in the social patterning present in burial data are the quantitative methods that are utilised in an attempt to extract them. Early theoretical perspectives fitted conveniently into a quantitative scheme of things, but more recent approaches, with a focus on general ideological trends, tend to be anti-quantitative. Chapman (1987) emphasised the priority of theory over method. While theoretical concerns are vital, there may be a danger in ignoring the quantitative aspect. In fact, quantitative results may be of particular use when they contradict theoretical expectations, since this may, in some instances, draw attention to limitations in the theoretical perspective. If theory is developed independently of the results demonstrated in quantitative analysis, there is a strong danger of rejecting the quantitative results if they differ from expectation (e.g. Tainter 1975), even if it is, in fact, the theory that is at fault. Therefore, whatever theoretical stance is taken, an interest in quantitative methods will always exist because of the fundamental desire to dissect and explain the patterns present in burial.

1.3

The information obtained mortuary practices

from

study

of

One of the most fundamental and widely-asked questions applied in the analysis of burial data is what complexity of society the data represents. Subsequently a considerable amount of theoretical work in mortuary analysis has concentrated on this particular aspect of social structure, to the detriment of other, smaller-scale social elements that go towards making up the general social structure. Very often the question is reduced to one of simply labelling a particular society as egalitarian or hierarchical, despite the apparent limitations in this dichotomy, and recognition that more complex variations of structure occur (e.g. Binford 1972: 28; Wason 1994). Levy (1989: 156) has described this as an "almost universal" concern. Much of Saxe's dimensional approach and statistical techniques, for example, were geared directly to addressing this egalitarian-ranked query. The terms "social organisation" and "social structure" are often used inter-changeably but from the anthropological perspective they have different meanings: the "social organisation" refers to the day-to-day relationships of a community, the way things actually are as opposed to what they should be, while the "social structure" consists of the idealised set of relationships. Often the social structure, as opposed to organisation, is symbolised in the mortuary domain. Early works on the theory of mortuary practice, such as Saxe, did not recognise this distinction, or did not consider it to be of importance. However, the theory on determining this aspect of a society is still to a large extent (even unconsciously) based on the work of Saxe, Binford and Tainter, despite the criticisms noted above. This is because, although it is now recognised that the social complexity represented in burial may be misleading, there is still a necessity to study the physical data itself to determine what type of society (real or

The two fundamental approaches to burial study - that of Saxe/Binford and that of Hodder, Parker-Pearson et al should not necessarily be seen as conflicting, despite the apparent concentration now on the latter. Rather, they represent different viewpoints of the amorphous concept that is "social structure", and both have their faults. Recently there have been attempts to integrate the two approaches. Brown (1995: 10) comments that rejection of the SaxeBinford approach "is tantamount to throwing the baby out with the bathwater", and linked the two different approaches in terms of resource allocation: ...they are two perspectives to symbolic representation that are potentially coextensive. The economy of symbols framework adopted here helps place the two views on an equivalent footing by revealing the effects of the economy of differential allocation of scarce resources on modelling symbolic patterns (21). However, despite such attempts at integration, ideological concerns are still very much to the fore, with the work of Saxe and Binford widely equated with a dated and outmoded approach to burial in many contemporary archaeological analyses. What the two different approaches should have in common is a concern with what is represented in burial - in other words, what the patterns present mean in social terms, real or otherwise. Saxe and Binford were too concerned with 2

Theoretical Approaches to the Study of Mortuary Practice

otherwise) is represented; before cornrnents can be made about ideological misrepresentation of a hierarchical society as egalitarian, for example, there is a need to demonstrate that the burial data represents an "egalitarian" society as such. This requires a practical examination of the data, and this is why it is impossible to totally ignore the work of Saxe and Binford, even frorn an ideological perspective.

structure, rnonurnents, burials etc. (ibid., 3-5). While this approach would be the ideal, the vagaries of the archaeological record, particularly regarding settlement evidence (e.g. Harding 1984:141; Bintliff 1984c: 159) often means that the evidence frorn burial provides the rnain, and sornetirnes only, source of evidence for differentiation. The narrowness of this concern with social complexity, above other aspects, is well-demonstrated by Tainter's observations that, although horizontal differentiation is difficult to quantify, the degree of vertical differentiation provides an overall index of complexity, so that lacking this information is not seen as problematical (Tainter 1978). It is necessary to examine the actual structural variation in society, rather than concentrating on decisions as to which pigeon-hole the society fits. Particularly worrying is the fact that there is a mismatch between these qualitative terms and the quantitative methods used to identify thern; this results in the social labels limiting the analysis, rather than the analysis actively helping find new ways of defining complexity. This approach ignores the fact that burial data, rnore than any other aspect of the archaeological record, can, in the right circumstances, provide an opportunity for archaeologically-derivedclassifications of social complexity. Archaeology, with its broad tirne framework, has a clear advantage over ethnographically-derived typologies of society, in that the changing structure of society can be rnade apparent. In theory, the archaeological evidence should fill in the "gaps" to produce a rnore realistic out-lining of social typology and processes of change. There are examples of this: O'Shea's analysis of Plains Indian burial practice (1984), for example, revealed a rnuch rnore complex and finegrained picture of social structure than was apparent frorn ethno-historical accounts. This reflects the fact that ethnographic accounts of social structure are very often generalised and "ideal", lacking empirical data that would clarify the real complexities that exist; these complexities, in the right situations and having considered ideological issues, rnay be become rnost apparent in mortuary data.

To a large extent, the background work that was to become fundamental to these developments in mortuary theory was done in the 1950s and 1960s, in disciplines which were anthropological rather than purely archaeological. Saxe (1970) relied heavily on the status terminology of Goodenough (1965) - social identity, identity relationships and social persona, in particular - as the building blocks for his eight hypotheses relating burial practice to social structure. These terms and their application became a fundamental part of Saxe's work, and the concepts were also central to Binford's theory. The work of Service (1962) and Fried (1967) would also prove to be of crucial importance in later studies. Service and Fried were concerned with defining different levels of organisational complexity within societies. Their work has been rnuch cornrnented upon, e.g. Goldstein (1976: 13-16), Saxe (1970: 81-100), and need not be repeated in detail here. Suffice to say that rnuch has since been written about how Fried's egalitarian and ranked stages of organizational development should be distinguished frorn the data in the mortuary domain. It would also not be untrue to say that these concepts have gripped mortuary analysis in a stranglehold, driving the archaeologist to fit his burial data to a simplistic and unrealistic representation of society, without attempting to develop rnore appropriate representations. The limitations of such labels are clear when it comes to comparing cemeteries representing supposedly similar levels of complexity; the re-interpretation of the "egalitarian" Mesolithic cemetery of Oleneostrovski by O'Shea and Zvelebil (1984) was indicative of how broad a range of formations this single term could include. King (1978) also emphasised the dangers of viewing all hunter-gatherer societies as being at the sarne organisational level. Similarly, Tainter's work on Middle/Late Woodland cemeteries demonstrated that there were significant vanatlons encompassed by the term "ranked". Much of the problem seems to stern frorn inadequate definition of the terms themselves. The term "ranked" is often used in a context implying hereditary status or social stratification, yet Fried (1967) seems to consider a ranked society as only slightly different frorn an egalitarian one. Renfrew (1982: 2) surns up the situation well when he cornrnents that there is an "absence of any clear definition of exactly what is meant by ranking, in the living ethnographic present, even before its archaeological correlates are sought in material culture". In other words, if an anthropologist has difficulty in defining what exactly constitutes an "egalitarian" society when it can be scrutinised in its living entirety, the problems of identification frorn the archaeological remains are clearly enormous. Renfrew notes that Fried himself seems unclear about his exact definition, and the term rnay or rnay not imply stratification, hence the confusion in its use archaeologically. In Renfrew's opinion, rnuch of the confusion has resulted frorn the classification of societies into particular categories based on the status of individuals, rather than rnore broader examinations of the degree of central authority, through the integrated use of the various categories of archaeological information - settlement pattern and

Thus, over-concern with determining the general complexity of a society frorn its burial practice has tended to divert attention away frorn the actual components of society, and their integration. As Turner (1969: 119) expresses it, "the units of social structure are relationships between statuses, roles and offices". It is this level of investigation that forms the skeletal framework of the social structure. Society is constituted frorn various groupings and positions - status levels (if they exist), horizontal groupings (lineages, clans, moieties, separate cornrnunity groupings, etc.), particular roles within the society (warrior, shaman, craftsperson, clan leader, etc.), the relative social standing of different ages and sexes, presence of societies/religious groupings, social deviants, and so on. Again terminology can cause problems because of its vagueness and differing usage. "Status" is cornrnonly used to imply relative placement of individuals on a vertical scale, suggesting "high" or "low" status. Goodenough (1965), however, considered status to be a definition of rights and duties applying to a particular person, with "role" performing as the dynamic aspect of status. Similarly, "horizontal" and "vertical" differentiation are convenient terms for different dimensions of social organisation, but actually overlap considerably in real-life considering that "vertical" distinctions are often seen as inherited differences, they simultaneously incorporate an element of the horizontal, e.g. lineage. The impression is 3

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

that these terms are ones of convenience rather than necessarily practical value. The terms also ignore the interactions that articulate such divisions and which clearly are another area of interest (the components of the society are not isolated units but are dependent upon each other, cf. Durkheim's mechanical and organic solidarity).

apparent. Whether the key diagram tends towards being a paradigm or a tree was seen by Saxe as imparting information about the organisational nature of a particular society (ibid.). Saxe arranged his ideas into eight hypotheses, divided into two groups: the first four were concerned with the representation of different social personae within disposal domains, the second four being concerned with representation of different social structures cross-culturally. These hypotheses were tested using ethnographic mortuary data from three differentially organised societies. Despite the daunting terminology, most of Saxe's hypotheses are in reality quite simple. Hypothesis 1, for example, simply states that different social personae are represented in the mortuary domain by particular combinations of attributes, Hypothesis 2 states that the social structure - egalitarian, ranked or whatever - will determine the nature of the social personae as seen in the burial practice, and Hypothesis 3 states that the social status of the person will tend to correlate with the number of features present in the burial of that person ("positive components in their significata"), as a consequence of greater interaction and ties with more social groupings. Relatively simple as these hypotheses seem, nobody working in the area of mortuary analysis prior to Saxe had attempted to set out these principles in a formal manner so that they could be tested, verified and used by future workers. This is why Saxe's doctorate is seen as seminal, tackling as it does the fundamental theoretical framework upon which the social analysis of mortuary practice should be hung.

The interaction between these social components is an area of particular interest, and one that does not always receive the full attention that it should. This is perhaps because, theoretically, it can be something of a blind spot, largely because of the vagueness of the archaeological data. Much of earlier work focused on detecting different status levels, symbolised through differences in "energy expenditure", and taking such differences, if they existed, as indicative of hierarchical societies. There are numerous problems with this, not least of all the fact that the whole interactive complexity of society is being reduced to a few gross levels or "ranks", with little consideration of the integration between these levels, the variability within levels and the distribution of other social components relative to the supposed levels. Again it seems that much of the available evidence from cemeteries is being ignored so as to answer a very generalised question i.e. whether the society is egalitarian or hierarchical. The work of Saxe, Binford and Tainter has, as noted, been fundamentally questioned, in the sense that they assume social structure is faithfully reflected in burial practice, and that cross-cultural rules can be applied to burial data to reveal this structure. However, certain elements of their work still are applied, even unconsciously, in the study of burials, and their work, with its basis in role theory, has relevance to the points discussed above. There is a need to look at the more specific problems with their theory, since this high-lights general problems in archaeological approaches to studying burial practice. The following sections will look individually at the the contributions and problems of Saxe, Binford and Tainter, following which more recent approaches to mortuary studies will be investigated.

In general theoretical terms, Saxe's hypothesis 5 has perhaps received the most attention (in addition to hypothesis 8, which makes a connection between the importance of land, and the use of cemeteries by descent groups to make claims upon this resource). Hypothesis 5 connects the arrangement of the mortuary attributes, in terms of degree of redundancy, with the complexity of the social organization (degree of egalitarianism):

The more paradigmatic the attributes evidenced in the key structure of the Domain, the less complex and more egalitarian the social organization, conversely, the more tree-like the attributes, the more complex and the less egalitarian the social organisation (Saxe 1970: 75).

1.4 The work of Saxe Saxe's complex "Social Dimensions of Mortuary Practice" (1970) is widely seen as being the starting point of the new, social analytical approach to mortuary practice. Saxe was extremely precise in defining his terminology, and the aims of his study, making his hypotheses difficult to read and comprehend at first. This approach was, however, necessary if ambiguity was to be avoided and testable hypotheses formulated. He termed mortuary data "ethnographic archaeology" within the context of his study, reflecting the fact that this form of data is available to both the ethnographer and archaeologist. The aim of this work was to construct a body of theory that would relate burial practice to the organization and structure of the society creating the evidence. Saxe devoted a chapter (some 50 pages) to his methodology, which involved an adaptation of formal analysis to ethnographic mortuary data. The aim of this was to elucidate the basic components of the mortuary domain, how they are combined together, and what can be deduced about the principles regulating the various combinations (ibid., 16), representing "disposal types". The results of the formal analysis can be summarised in the form of a key diagram which allows cross-cultural regularities to become

Saxe goes into great detail in his discussion of this hypothesis, which basically incorporates his four preceding hypotheses, and, put more simply, states that the degree of complexity of a society can be correlated with the degree of redundancy of the components of the mortuary domain. This is based on the assumption that in an egalitarian society the range of social personae will be less distinctly segregated than in a non-egalitarian society. Features or "dimensions" of the burial remains will be similarly less segregated in their application to particular social personae in an egalitarian society, and so there will be a tendency for minimal redundancy in the use of these attributes. Hence when the mortuary domain is structurally analysed the resultant key structure will tend to be paradigmatic, a perfect paradigm representing a situation where there is zero redundancy (ibid.,44;75-77;107-108). Saxe points out that a perfect paradigm will never be produced from mortuary data, as there is no such thing as a perfectly egalitarian society (ibid.; 47). 4

Theoretical Approaches to the Study of Mortuary Practice

Conversely, a non-egalitarian society will have more distinct social personae, represented by distinct dimensions, and the key diagram for the mortuary structure of such a society will tend towards being tree-like, a perfect tree representing a state of absolute redundancy of the mortuary attributes (ibid.,4749; 77-79; 108-109).

workable whole relating the evidence of archaeological and ethnographical mortuary data to the social structure of the societies producing the mortuary data. Most of his eight hypotheses were confirmed, though some with extra qualifiers, and room for expansion was noted. The work was a significant step forward in the study of mortuary practices because it was thoroughly analytical in its approach, allowing adoption and manipulation of the theory by later workers, and because it tackled in a logical, well-defined manner the aspects of mortuary practice that are of fundamental interest to archaeologists - the reconstruction of the nature and components of society.

In order to test Hypothesis 5, it was necessary to devise some

measure of how tree-like or paradigmatic the key structure for a particular mortuary domain was. Saxe adopted the entropy concept for this purpose, paradigms representing a state of maximum entropy and trees representing minimal entropy. From this, four measures were derived: a measure of maximum entropy (i.e. assuming a perfect paradigm for a particular system), actual entropy, relative entropy (the ratio of the actual-to-maximum entropy, to remove cultural bias and allow cross-cultural comparisons), and redundancy, which is the relative entropy subtracted from 1, and which can be used to arrange mortuary domains in order of their redundancy and hence increasingly tree-like structure. These measures are calculated for a particular mortuary domain from the number of significata possible and the number actually derived, a significata being a particular set of attribute values representing a social personae within the mortuary domain (ibid., 102-112). Saxe notes that the redundancy measure should be a particularly good indicator of social complexity, as its value increases as more dimensions are added to the mortuary domain, so it does not simply measure the degree of organisation among its components alone (ibid., 109110).

Despite the usefulness of Saxe's work, there are potential problems with some of the assumptions that drive his theory, apart from the general, ideologically-based criticisms noted earlier. For example, he suggests, in hypothesis 4, that the social persona chosen for representation at death is one with the most significant identity relationships (ibid.,9); in death there is a fuller representation of a person's social identities than at any time during life, and thus a greater probability of conflict between them. The social identities expressed will be the most significant ones: because these identity relationship reciprocities exhibit greater influence, authority and/or power not only by virtue of involving more people, more groups of people, but also because they include people who themselves possess greater influence, authority and/or power (ibid., 72).

In testing hypothesis 5, Saxe concluded that it could neither

While there is a certain amount of truth in this when considering extreme examples, such as the burial of a president or king, it is questionable if this will always hold true for the kind of hierarchical societies which may be seen archaeologically, where such extreme and highly-symbolic social identities may not be present. The unusual nature of the occasion of death must make the nature of the identity relationships similarly unusual, and it need not always be the case that those relationships with greatest power, authority or influence will be represented to the detriment of more direct, familial (and emotionally-charged) relationships. These conflicts may arise during life, but the possibility of "conflicting" identity relationships occurring in death may be very different, particularly when it is considered that burial is the last occasion on which identity relationships may be enacted with the deceased person, which should give considerable significance to emotionally-charged relationships. O'Shea (1984: 11-12) has also cornrnented on the emotional relationships between people, drawing attention to Goodenough's (1965) distinction between social and personal identities. In fact, family involvement in burial should ensure expression of these relationships in addition to the more politically-orientated forms, which may be more ritually (as opposed to materially) oriented; even the extreme social persona of the President mentioned above may have elements of burial that contrast with the official, "important" roles. Thus, President Kennedy, although buried in Arlington war cemetery, is accompanied by two children, his infant son Patrick and an unnamed stillborn daughter. The presence of children in a war cemetery will no doubt be of interest to future archaeologists. Certainly it should not be assumed that political expression of solidarity will always be a priority in a burial through time; the development of nocturnal burials of the aristocracy in eighteenth century England was, in part, due to a growing desire to emphasise personal rather than

be out-rightly accepted or rejected, principally because of a high redundancy measure for the mortuary data of the fairly egalitarian Bontoc Igorot, higher than that for the much more organisationally complex Ashanti. This seems to have been a consequence of inaccurate, incomplete or biased ethnographic data for either or both societies (ibid., 230-231), and serves to emphasise the dangers of using ethnographic data as a basis for determining specific, cross-cultural "rules". Saxe's conclusion was that the hypothesis and its associated statistical techniques could be valuable for the comparison of social complexity at a cross-cultural level, though it needed to be thoroughly verified with appropriate data (ibid., 231). Tainter (1975, 1978) made use of entropy and information theory to determine the amount and degree of organization in Middle and Late Woodland societies, but applied these quantitative methods in a different context, studying the distribution of the burial population between rank levels, rather than a formal analysis of the mortuary components as such. Other workers, such as Goldstein (1976), used formal analysis and examined the degree to which the mortuary structure conformed to a paradigm/tree structure, but usually at a relatively qualitative level, jettisoning Saxe's statistical methods. In the case of Goldstein's analysis of the burial remains at Moss and Schild, the formal analysis was performed on the results of a cluster analysis. King (1978), in the analysis of a hunter-gatherer cemetery in California, appeared to use Saxe's redundancy measures, but does not quote what the figures were, beyond commenting that a "high" value was found relative to those obtained by Saxe. To summarise the significance of Saxe's work: his stated aim was to formulate a body of theory that would integrate various models and methods - formal analysis, role theory, modelling of social complexity etc. - into a coherent,

5

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

political connections. In the older funerals the focus of attention was on the ceremony in the church, with many mourners actually returning home before the interment itself, but now the interment, and expression of personal grief became priorities (Gittings 1984). This emphasis on the political/power aspects reflected in the burial domain is a quite cornrnon theme running through theory of burial practice and seems to neglect the emotive and non-political issues (what Morris 1987 terms the "noise" getting in the analyst's way) that must surely also influence the dimensions of burial. The situation probably arises because of an archaeological inability to deal with such less tangible and definable influences upon burial, and also because the focus of attention has been upon the political structure of societies.

A major problem is the assumption that a particular social identity will always find a particular, universal expression in the mortuary domain (a particular "set of components"); this is implied in Saxe's first hypothesis. While this may sometimes be true, the assumption seems to deny the natural variation that may occur which reflect individual preference as to how to symbolise things, perhaps through differing traditions or interpretations of the "meaning" of mortuary dimensions. An example of this would be Ucko's "archaeologist's nightmare" (1969) concerning orientation of the dead among the Ashanti. Here the general rule is that the dead should be buried facing away from the village. Some people, however, believe that the corpse, after burial, turns itself round to face the opposite direction, and consequently these people bury their dead actually facing the village, in the knowledge that eventually the corpse will face away (ibid., 273). In this case a particular "rule" is being followed, but with different interpretations of how it should be implemented. Despite the importance of ritual activities, it cannot be assumed that there will be consensus as to how they should be followed. In his study of rituals surrounding twins among the Ndembu of Zambia, Turner (1969: 61-3) cornrnented how those involved in the construction of twinship shrines had two conflicting interpretations of the "meaning" of the shrine structure, resulting in differing arrangements of ritual objects within the shrines. Other variations in the burial ritual may occur that have no particular social meaning; there may be variations in the depth of burial reflecting, for example, the season (Binford 1971); burial pits may show a variety of forms without apparent meaning; cremation and inhumation may be practised together as a matter of preference or simple fashion. The key problem, archaeologically, is knowing what variations have social meaning, and what are meaningless. This is less of a problem with ethnographic evidence, where such meanings may be explicitly expressed. In applying Saxe's hypotheses, therefore, great difficulties are encountered when it comes to selecting "meaningful" variations.

Saxe's hypothesis 3 also has some questionable assumptions. This hypothesis suggests that social personae of less significance have fewer positive components in their significata relative to others i.e. people of less importance in the society will have fewer social identities, and thus fewer distinct burial characteristics. This can be queried: for example, will every social identity result in a component set in the mortuary domain (as is implied in his hypothesis 1 and which has relevance here), particularly so in considering the archaeological record, as opposed to the real-life structure of the burial process? A particular identity relationship may be signified by the presence of a person/group of people at the burial, either directly involved in the burial process (e.g. a guard of honour, or construction of the grave) or as bystanders. Another way of symbolising an identity relationship could be through some ritualistic procedure, such as a gun-salute, or the lowering of flags to half-mast. Ethnographically, such duty status relationships are frequently marked in ritual that would leave no archaeological trace. Amongst the Lodagaa, Goody noted that a mock funeral hunt was performed on the day of burial by agnates, and some token farming by members of the dead man's farming group, fulfilling any obligations to the deceased. For a woman, pots may be ritually smashed by the women with whom she associated (Goody 1962: 129-30). Sham fights may be used to symbolise the deceased's role as a warrior (Wagner 1949). Bodily mutilation of the living may also be used to mark out a relationship - amongst the Dani of New Guinea, at the funeral of a war victim women close to the deceased may be required to have fingers chopped off, and this is, in fact, equated with other funeral goods (Heider 1979). The point is that social identity relationships need not be explicitly marked in the process of burial, and certainly need not be recoverable from the archaeological record. This latter point is clearly demonstrated when the dimensions which are used to symbolise social identities in Saxe's ethnographic examples are noted. For the Bontoc Igorot, for example, the methods of distinction used include the body container, placement of cloth on the eyes, burial robe, bark breech cloth, blanket wrapping, basketwork hat, amongst others none of which would be recovered archaeologically, except under exceptional circumstances. This underlines a serious problem with Saxe's theories for archaeological application, the fact that they derive from observation of ethnographic practices rather than direct burial data. The hole in the archaeological record created by the non-preservation of organic materials and non-marking of certain ritual activities must surely distort the recognition of social personae and the application of Saxe's hypotheses.

Saxe himself noted complications in the results obtained when compared to what he had hypothesised. For example, with the Ashanti, hypothesis 3 did not hold when comparing royals with non-royals; some non-royals of high significance had more components than some royals. Saxe suggested that this was because royals and non-royals were so distinct the components in the significata marked differences within the strata, rather than marking differences between strata. This is actually a very important contradiction, from the archaeological perspective, since it suggests that the "distinctiveness" of a particular hierarchical level in society can, paradoxically, reduce the need to symbolise this identity in burial. Masset (1989) made this exact observation with regard to aristocratic burials. Saxe's Hypothesis 4 also did not seem to be true when applied to non-royals. Similarly, for the Bontoc Igorot, there were complications in the application of these hypothese. For hypothesis 2 it was found that there was confusion between particular social personae, depending on individual circumstances - for example, unmarried types were associated with attributes relating to young adulthood, causing anomalies for elderly unmarried males. Saxe suggests that hypothesis 3 was confirmed with the Bontoc Igorot, yet, as with the Ashanti, there were serious contradictions; the greatest number of components were found among the unmarried personae, exceeding the number of components for people of greater significance. This may, as noted above, be a consequence of 6

Theoretical Approaches to the Study of Mortuary Practice

1.5 The work of Binford

an over-excessive emphasis on power relationships in the mortuary domain, without allowing for the possibility that more prosaic or emotional factors will have effect.

Binfords's paper in 1971, "Mortuary Practices: Their Study and Their Potential" is, like Saxe's work, widely quoted because of its contribution to the theoretical perspective of the analysis of mortuary practice. He started off by reviewing some of the earlier philosophical ideas about burial practices, which tended to concentrate on the spiritual and religious aspects rather than the possibility of deriving social information as a consequence of the variations observed in these practices. Binford then went on to examine the assumptions involved in historical reconstructions from mortuary data. He examined and disproved Kroeber (1927), who assumed that mortuary customs have unstable histories Binford demonstrated that mortuary practices may exhibit both stable and unstable histories - and that burial practices are independent of the everyday aspects of life in a society; in fact Binford showed that when the work of other investigators was collected together, the evidence showed over-whelmingly that variations in burial practice were linked to the organization of the society (Binford 1971: 214-223).

Perhaps the greatest criticism of Saxe's work, and which undermines all his hypotheses, is the fact that it is both derived and tested using ethnographic descriptions, yet is presented as a body of theory for use in archaeology. Particularly worrying, from the testing point of view, is the probability that the descriptions given of particular mortuary treatments in the accounts used by Saxe were not intended as being full and comprehensive, in some cases being only 1-2 pages in length for the complete mortuary system. This is a general problem with ethnographic descriptions of burial practices, since they are rarely a particular focus of attention, and even if they are, there are problems of limited periods of observation (in which it is unlikely that every form of funeral will be observed), inability or actual prohibition of observations of all aspects of the ritual, particularly regarding the material goods that may accompany burial, or reliance on indigenous accounts (with possible problems of translation and/or exaggeration - for example, Goody's observation that his informants "habitually exaggerate" the number of animals slaughtered at funerals (1962: 163) - or mixing of past and present practices). This must undermine the reliability of Saxe's test results, and particularly emphasises the need to examine them with archaeological-type data, taking into account the problems of non-preservation of organic materials which may have been significant components in the mortuary domain. Such problems have led Brown (1981) to observe that Saxe's approach worked best as a "behavioural model" and was not particularly suited to normal archaeological data, though of use from a purely theoretical perspective. However, there is something of a contradiction here in the suggestion that Saxe's work has theoretical, but not practical value, and it highlights the particular problems of social reconstruction from burial data, which may be lacking much of the social information that was originally part and parcel of the entire funerary process.

In his next section Binford went on to examine the potential that exists in the study of mortuary practices, and it is here that he has rnade his most significant and widely-used contributions to the theory of the subject. He proposed that there are two main social components involved in determining the nature of a particular burial. First is the social persona of the dead person - "a composite of the social identities maintained in life and recognised as appropriate for consideration at death" (ibid., 225). Second is the composition and size of the social unit with status obligations to the deceased, the number of which should correlate with the rank of the dead person. Binford went on to argue that it was this second factor that determined the location of the burial structure, and the associated ritual, because of the varying degrees of community disruption. He tested his theoretical assumptions on a sample of 40 nonstate societies, and came to three significant conclusions (ibid., 235). First, the dimensions of the social persona symbolised in burial are dependent on the organisational level of the society - egalitarian societies should symbolise age and sex dimensions most commonly, while more complex societies should utilise social position and social affiliation as basis for distinction in the mortuary domain; this conclusion is similar to Saxe's hypothesis 2.

In conclusion, Saxe's work, though initially accepted as a "bible" of research into the social dimensions of burial data, was later subjected to much criticism, particularly from the ideological perspective. The idea that social organization was directly and faithfully reproduced through burial was seen as unrealistic, as was the idea of defining "roles" from this data. Morris (1987), however, in some ways defended particular aspects of Saxe's approach by indicating that roles could be reconstructed from the data, though as aspects of social structural change rather than organisation. Morris also used formal analysis to examine early Greek cemeteries, though he ignored the statistical aspects of Saxe's structural analysis, instead examining variability of the burials within a particular cemetery as compared to a modal burial form (ibid., 110-115). Morris described this approach as useful in determining over-all structure, but it seems to be limited in determining any more specific aspects of structure, and basically is a comparative measure. It seems that all of the constructs of Saxe's work should not be simply swept to one side because of a focus on ideology. There remains much of potential value in Saxe's emphasis on role theory and social personae, as long as it is approached from a general perspective. A rigid approach to his work, however, seems untenable, particularly in light of the criticisms made above.

The second point that Binford makes is that the number of dimensions of the social persona symbolised in burial practice will also depend on the complexity of the society. The most complex of his sample societies, the settled agriculturalists, used an average of over three dimensional distinctions, significantly more than for the other three less complex social categories. Thus the more complex the society, the more structurally complex will be the nature of the mortuary domain. This conclusion is really a cruder version of Saxe's hypothesis 5, without taking into account the structural details of the latter. The final point that Binford makes about the variability of the mortuary practices in his sample societies is that the dimensions of the social persona symbolised will determine the form of the mortuary ritual. For example, status was usually symbolised by symbolic artefacts and quantity of grave goods, sex was usually differentiated by the types of goods, and sub-group affiliation by location and orientation 7

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

of the grave. A connection was also found between age and location of burial, correlating with the degree of public disruption that was a reflection of the number of duty status relationships, as Binford had predicted. This conclusion is closely tied to Saxe's Hypothesis 1, which states: the Components of a Given Disposal Domain Cooperate in a Partitioning of the Universe, the Resultant Combinations Representing Different Social Personae (Saxe 1970: 65). In other words, the structure of a particular burial will reflect the characteristics of the deceased person which the mourners wished to or were obliged to symbolise.

Certainly, Binford's results were not as clear-cut as he would have liked or, indeed, as he implies. Regarding his first conclusion - that the dimensions of the social persona symbolised in burial depend on social complexity - a look at Table 3 (p.231) reveals some contradictions. For the Huntergatherer sample of 15 societies, although twelve of the fifteen recognised sex as a dimension in mortuary variability, only two recognised age as a dimension, despite the egalitarian nature of these societies; this is actually less than the number who symbolised social position and social affiliation as mortuary dimensions (six and four respectively). Similarly, although the settled agriculturalists (taken as being the most complex societies in the sample) recognise social position as the most common dimension of variation (11 out of 14), as predicted, sex is also recognised almost as often (10 societies) and age also quite commonly (7 societies - actually more frequently than for the more egalitarian hunter-gatherers). Thus, although the general trend of Binford's hypothesis seems to be true, it is clear that the reallife patterning is nowhere near as simplistic as the theory would suggest. To a large extent this is due to the overlysimple dichotomoy between egalitarian and non-egalitarian societies, and also the dangers in working with small samples and ethnographic descriptions.

Thus to a large extent Binford's paper on the analysis of mortuary practices was a more limited and less analytically defined version of the work of Saxe (1970), though also made more accessible by that fact. The most widely used aspect of the paper has been his elucidation of the two main social components involved in the variability of mortuary practice, the social personae, and the composition/size of the social unit with status responsibilities to the deceased person. Binford's theories have also been widely criticised (e.g. Hodder 1980, O'Shea 1984). These criticisms are well summarised by Morris (1987) under three headings: 1) The cross-cultural test - criticisms have been aimed at the selection of the ethnographic sample, and the assumed correlation between subsistence economy and social complexity. Morris supports Binford on this latter point by noting that this is a generally accepted, "probabilistic rule", though O'Shea and Zvelebil (1984) commented upon the dangers of using subsistence economy as a measure of complexity, emphasising the fact that differing environments will have a significant impact on social organisation, even if the form of economy is nominally the same.

Similarly, Binford's second conclusion - that the number of dimensions of the social persona symbolised in burial will depend on social complexity - is not represented straightforwardly in the sample societies. It may be true at a gross simple-complex level of interpretation, but the results do not support this conclusion at a level of greater gradation of social complexity. Statistically, Binford noted, there were no differences in the average number of dimensional distinctions among hunter-gatherers, shifting agriculturalists and pastoralists (ibid., 227-230), even though the latter two categories are presumably taken as being of greater organisational complexity than the former, given Binford's assertion that form of subsistence production should correlate with social complexity (ibid., 227). Again this probably reflects the crude nature of the data, and the crude index of complexity, but also emphasises the dangers in trying to produce cross-cultural "rules' for the complex and varied mortuary domain. It is also notable that amongst the distinctions symbolised in the mortuary domain considered by Binford are cause of death and location of death, which have no intuitive connection with social complexity; one would expect these distinctions to be equally likely in simple and more complex societies (in fact, the location of death is not recorded at all by Binford for the settled agriculturalists, and only noted once for the hunter-gatherers and pastoralists).

2) Archaeological utility - this, as in the criticisms of Saxe, centres on the difference between ethnographic and archaeological mortuary data, and the ability of the latter to give a representative picture of what was symbolised in the funeral. 3) Ritual aspects of burial - the criticisms here centre on the recent trend towards concentrating on the ideological aspects of burial ritual, which Binford does not take into account in his theory. Binford himself notes the cursory nature of his paper when he comments that it is an "admittedly limited investigation of variability among a poorly structured sample of societies" (Binford 1971: 235). Fundamentally, the categorisation of his societies and the sources of the descriptions of their mortuary practices may be faulted, in addition to the comments above. He notes that his sample societies were placed into categories (hunter-gatherers etc.), "accepting the classification given in the 'World Ethnographic Sample' (Murdock, 1957) for the ethnic groups in the sample" (227). However, this is problematic in that some of the ethnographic descriptions date back to the early nineteenth century, when the social complexity may have been different. Furthermore, the descriptions of the mortuary practices in a particular society are in some instances from references of widely-separated dates - for example, for the Aleut, the two references from which descriptions of burial practice are obtained are dated 1806 and 1925, a substantial chronological gap.

1.6 The work of Tainter It was Binford's second social component that Tainter used in developing his energy expenditure model (e.g. Tainter 1975, 1978). O'Shea (1984: 15) has described the energy expenditure theory as "the most serious attempt to formulate an archaeologically relevant theory for the analysis of social organization through mortuary remains". According to this theory, individuals of higher rank should have a greater amount of energy expended on their interment because of the greater corporate involvement. The greater energy expenditure should be reflected in the size and elaborateness of the grave structure, the complexity of body treatment, duration of accompanying rituals, and the nature of the grave offerings. The occurrence of distinct levels of energy expenditure should 8

Theoretical Approaches to the Study of Mortuary Practice

reflect distinct grades or ranks in the society (Tainter 1978: 125). This method of determining ranking in society from mortuary data is advantageous because, firstly, it incorporates various dimensions of the mortuary domain, such as the structure of the grave, ritual evidence and treatment of the body, rather than the usual approach of concentrating on the grave goods, which may ignore much of the information available, and secondly, it allows analysis of cemeteries which may lack grave associations and which would otherwise, because of the usual analytical bias towards this dimension, not be as readily studied for social information.

Cambridgeshire village of Camberton, discovered that competition among farmers of similar status was responsible for the great diversity of structures. This competitive element of energy expenditure is one which is not accounted for in Tainter's theory, despite its significant impact on differential energy expenditure, obscuring rank differentiation. The importance of horizontal differentiation as a factor here should also not be under-estimated, and several researchers have cornrnented on the fact that the model tends to obscure the horizontal dimension (e.g. Chapman and Randsborg 1981; Pader 1982; O'Shea 1984). Behind the energy expenditure model is Binford's observation that the size and composition of the "social unit" with duty-status relationships to the deceased will affect the nature of the burial. This terminology used by Binford is notably vague: he does not define what he means by the "social unit", and his phrasing seems to imply a single, homogeneous body, rather than the possibility of different groupings being related to the deceased person in different ways - for example, through social affiliation as opposed to a relationship of servitude. To some extent an artificial social unit with a symbiotic relationship between the living and the dead may exist. In some Cantonese societies "old people societies" are formed to guarantee a large attendance at funerals of its members. Any surviving members have an obligation to attend the funerals of other members, the obligation passing on to their descendants (Watson 1982). The Merina of Madagascar also have tomb associations to arrange funerals (Ucko 1969:268; Pader 1982), and increased competitive display in Andalusia has been linked by Brandes (1981) to subscription to burial insurance companies, with improved wages. These examples question the notion that funerary ritual and energy expended in it result from purely spontaneous, 'true' social relationships that the deceased may have had while alive.

Tainter examined his energy expenditure argument in relation to 103 ethnographic cases, and did not find a single contradiction, apparently giving strength to the validity of the model. For example, under the heading "Construction and Placement of the Interment Factility", he observed that in a district of New Guinea bodies were exhumed after about five months and interred on a cliff face, with children and women at the bottom, and the most important members of the society on the highest and least accessible ledges (Tainter 1978: 126-127). Other examples relating to different dimensions of the mortuary domain also revealed an energy expenditure differential as a consequence of status differences. Tainter applied his model to various sets of mortuary data. For example, he used it to analyse the Kaloko cemetery on the island of Hawaii, which consists of a variety of cave and crevice burials, and platform burials varying in size, shape and decoration (Tainter 1976). The crevice and cave burials were used without artificial modification, and were thus considered to represent the lowest energy expenditure. The platforms were divided into different levels of energy expenditure on the basis of volume of stone used, and degree of stone facing. Consequently, seven distinct levels of energy expenditure were determined and, following the theory of the model, these were assumed to represent seven distinct grades of ranking. Corporate groupings of platforms were noted in the cemetery and since all these groupings had platforms of different levels of energy expenditure, it was deduced that the corporate groups consisted of a variety of people from different ranks, rather than specific ranks being limited to particular descent groups. The lowest level of energy expenditure, the crevice burials, were deduced as being the coresident affines of those buried in the caves, having been excluded from these family burial places, a pattern expected from nineteenth century ethnohistorical accounts (ibid.).

Obviously horizontal divisions in a society, such as clans, moieties or secret societies, will enter into the composition of the social unit. In a mixed-clan society, such as developed in the eighteenth century in the Saginaw Bay area (Mainfort 1985), the numerically more dominant clan may have greater energy expended on the funerals of its members purely because of the greater number of participants, rather than necessarily having any correlation with status. Mainfort noted that in the case of the Fletcher site, a mid-eighteenth century Native American cemetery, the clans/lineages present had different degrees of wealth, reflecting differential access to trade connections, and this is apparent in the burials, though not necessarily reflecting individual status differences (ibid., 559, 571). At Schild Knoll B Goldstein (1976) also cornrnented upon the impact of horizontal affiliation. Here, the differential treatment given to burials inside and outside the charnel area was analogous, so status distinctions could not be considered to be present between the two. This was despite the fact the charnel burials obviously had a greater energy expenditure. Goldstein suggested that there were two simultaneous interment programs representing different cornrnunity groups, the non-charnel burials perhaps belonging to outlying communities.

Tainter's energy expenditure model, though appearing intuitively satisfactory, and given further credance by its ethnographic support, has numerous difficulties in application to archaeological data. These problems are worth discussing in detail because, even with current emphasis on ideological use of burial, there is still an inherent tendency among archaeologists to search for differences in energy expenditure in a cemetery to identify the nature of the society. The criticisms of the model can be addressedunder a number of points: 1) The reverse of the hypothesis has not been assessed i.e. that differences in energy expenditure will indicate differences in the social status of the deceased (Braun 1981). This is an important point, since archaeologists clearly are interested in the reconstruction of the status system from the differences apparent in the mortuary data. Cannon (1989), in studying the differentiation in burial monuments in the

2) Cannon (1989) emphasised the fact that an automatic connection cannot be made between high-status individuals, and high energy expenditure - rather, it is the ability to maintain a distinction between high and low-status mortuary practices that is crucial. Brandes (1981) noted a similar, 9

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

contemporary process in a small Andalusian town; the ability for conspicuous display had spread to a greater proportion of the population with improved wages. All Saint's Day was traditionally an occasion for display, but since 1980 the elite have avoided these public displays because of their inability to distinguish themselves from the lower classes. Thus, those at a lower level in the social hierarchy may be more concerned with expenditure in burial. Parker-Pearson (1982), analysing data from undertakers in Cambridge in 1977, noted that the lower classes often spent most on a funeral; cremation was more popular among the upper classes, despite being lower cost than inhumation. Gypsies were noted as having among the most expensive and prominent monuments, despite their position in the lower levels of society. Masset (1989) gives an example of a noble family, the Earls of Latour de Geay, who had very simple burial places amongst much more elaborate structures, which he explained as being a consequence of their widelyacknowledged status and subsequent pointlessness in grandiose burial display: "The most spendthrift in tomb building may actually have been so because he felt looked down upon by others and was trying to make up for it" (ibid., 451). Clearly, then, the attitude to burial is allimportant in determining what degree of energy expenditure will be applied by which groups within the society.

4) Archaeologically, energy expenditure cannot be realistically measured in its entirety. Ritual and ceremony, distribution of gifts, the period of disruption and so on may be gleaned from the ethnographic literature, but will on the whole not be apparent to the archaeologist. Thus a large part of the data relevant to the model will be missing. The model assumes that archaeologically-detectable energy expenditure will form a constant component of the overall energy expenditure in each rank. This need not be so. For example, higher ranks may have greater energy expenditure, but there may be increased emphasis on ritual aspects that will not be detected by the archaeologist. Bradley (1989) pointed out that the elite may select a different form of conspicuous consumption relating to death - in eighth-century B .C. Greece, for example, the decrease in the number of grave goods is compensated for by a large increase in the number of votive offerings at shrines. Cenotaphs and other monuments may be erected to commemorate the dead because of the circumstances of their death - for example, those who die by drowning orfar away (Ucko 1969), or in battle - or because of their status; when Edward the first's wife Elanor died in Gratham in 1290, the King accompanied the body back to London and reputedly had between nine and eleven memorial crosses constructed on the route back. The erection of such monuments may involve considerable energy expenditure which would not be linked directly in the archaeological record to particular graves.

3) High energy expenditure on mortuary ritual can have other explanations, rather than simply reflecting relative status. Tainter himself noted the problem of correlating archaeologically-apparent distinctions in energy expenditure with real rank differences. In Huron ossuary burials, for example, most interments were defleshed, except for those who had died shortly before the reburial ceremony. An archaeological examination of an ossuary would thus reveal articulated and disarticulated bodies, which could be misinterpreted as representing distinctions in energy expenditure (Tainter 1978: 128). Individual graves in outlying Mississippian cemeteries in the American Bottom region have both articulated and disarticulated skeletons, but detailed analysis has not found correlation with particular characteristics of the deceased (Milner 1984). O'Shea (1981) argued that horizontal groupings, such as secret societies, could be distinguished in Indian cemeteries in the 18th-19th centuries by particular treatment of the bodies. Elaborate treatment of the bodies need not indicate a high status. A late Preclassic burial mound of the El Trapiche group in El Salvador was excavated to reveal 33 bodies. The bodies had been elaborately prepared but not as an indication of status, as the remains seemed to represent war sacrifices (Fowler 1984). In this example the elaborate treatment was used to emphasise the importance of the ceremony, rather than the importance of those being interred. Similarly, many ethnographically recorded societies may expend additional energy on the treatment of a corpse, not because of the highstatus of the deceased but rather to emphasise a transgression that the individual may have made during life - among the Ashanti, for example, criminals are disarticulated and it is interesting to note that the King is also disarticulated, though subsequently re-articulated with gold wire (Saxe 1970). An analogous example would be the treatment of people accused of treason in the Middle Ages, where the destruction of the body is symbolic of the power of the state (Finucane 1980). Some deviants may even have additional ritual observations because of their status, as in the case of the homicide among the Lodagaa, where an additional ceremony is associated with purification (Goody 1962:141-2).

5) The significance of grave goods within the energy expenditure model is a point of controversy. Tainter (1974) investigated the importance of grave goods for symbolizing status, and found them to be surprisingly of little significance. In surveying 93 ethnographic cases he found that grave good inclusions were used to signify status distinctions in less than 5% of his sample, which is in sharp contrast to the material evidence that most archaeologists face. Tainter's findings were contrary to those of Binford (1971), who tabulated form of distinction in the mortuary domain against characteristics of the social persona in 40 societies. Altogether, 21 of the 40 societies used type and/or quantity of grave goods to distinguish social position. This compared with only two societies who used body treatment, or preparation, or disposition to distinguish status, and only three societies who used form of grave as a marker of status. These results would tend to indicate that grave goods were of importance in distinguishing status, with other dimensions of energy expenditure seemingly of more limited consequence. This is hardly surpnsmg from the archaeological viewpoint, where grave goods are the most commonly used status indicators for the simple reason that they are very often the only distinct signs of energy expenditure, in cemeteries where the form of the grave itself may show little or no variation. Tainter used a larger sample and it may have been that Binford's sources could have been biased towards the recording of artefactual inclusions. However, this in itself emphasises the dangers in trying to form general, cross-cultural conclusions from ethnographic accounts. The fact remains that the archaeologist very often has to rely on the single dimension of burial goods for analysing social structure. The dangers of this are, of course, well-known (e.g. Ucko 1969). 6) There are problems with the conceptualisation of the differences in energy expenditure. According to the model the amount of energy increases with each level, and there should 10

Theoretical Approaches to the Study of Mortuary Practice

be clear breaks in the energy expenditure between levels (Tainter 1978: 125). This is clearly a simplistic view of the status system. Within a society there will be a limit to the potential increase in energy, and it may be that the higher ranks will not be differentiated as clearly as the lower ranks, where the potential for differentiation is greater. There is also the question of whether there is a need for higher ranks to differentiate among themselves as clearly, with the exception of the apical rank. It may therefore be that the energy expenditure gaps between ranks will not be consistent and may in fact decline with increased rank so that the highest levels may not be distinguished at all, instead appearing as one undifferentiated level in terms of overall energy expenditure, but possibly distinguished by particular symbols, or spatial distinctions. Variations within ranks may also add confusion. A particular rank is unlikely to have a consistent level of energy expenditure; instead one would expect a gradation in the expenditure relating to both achievement and sub-ranking, which may potentially cause ranks to blend into each other, in which case there will be some ranks not separated by a break in expenditure.

burial are thrown together into the undifferentiated concept represented by energy expenditure. At Kaloko cemetery on the Island of Hawaii Tainter (1976) deduced seven distinctive levels of energy expenditure, evaluated in terms of volume of stone and extensiveness of stone facing used in the burial platform. Type D were ranked lower than type B, though having larger horizontal dimensions. D was placed below B because the latter have more elaborate facings, but it seems difficult to objectively assess elaborateness of facing against volume of stone. Tainter's divisions must surely be open to question in terms of representing purely a difference in energy expenditure. Types A, B and E were circular platforms, while C and D were rectangular. This may have had some significance which is unrelated to rank, considering that circular platforms are ranked both above and below the rectangular forms. The existence of so many "distinct" ranks in society also seems dubious, and Tainter himself acknowledges that chronology may have been an influence (ibid., 102). The distribution of the population among the ranks deduced by Tainter in his archaeological applications of the energy expenditure model also often seem to be contrary to that which would be expected from the usual conceptualisation of ranking. In his study of Middle Woodland burials (Tainter 1975), six levels of ranking were determined. However, rank level 1 (the highest) had 91 burials, rank 2 had 58, rank 3 had 11 and rank 4 had only 5 burials - exactly the reverse of what would be expected from a ranked pyramid. This again undermines the validity of the rank levels determined by applying the energy expenditure model.

Related to this is the assumption that energy expenditure will remain at a constant level over time or, at least, if there is a change in expenditure, it can be accounted for. Cemeteries are, almost by definition, products of time, and the problems of chronology have not always been adequately cornrnented upon, despite the potential to confuse the number of ranks detected. There is no reason to assume that the energy expenditure on burials will remain constant with time and in fact the need for competitive distinction, as demonstrated by Cannon (1989), will in fact promote changes in the level of expenditure. This could have a two-fold effect. First, if there are major changes in expenditure the number of apparent ranks in the society could be over-estimated, if the chronological aspect was not detected. On the other-hand, small scale variations in the amount of energy expenditure over time could actually have the opposite effect, in that initial differences between ranks could become blurred.

Thus Tainter's model, though initially appearing satisfactory, has many theoretical and practical problems, limiting the usefulness of its application to archaeological data unless these are accounted for. The effects of chronology, horizontal divisions in society, the archaeological component of energy expenditure relative to total expenditure, competition between people of similar status, the degree and clarity with which ranks are differentiated, the objective assessment of the expenditure, and numerous other factors need to be identified as to their consequence, if the model is to be of any use.

7) Apart from all these theoretical problems with the energy expenditure model, there are also practical considerations. How exactly is energy expenditure to be measured objectively? Some aspects of status differentiation in burial are simply unquantifiable - for example, the use of spatial distinctions, as in separate burial locations, or different areas of one cemetery. Braun (1981: 407) cornrnented on the fact that on his analysis of the Klunk-Gibson burials, Tainter distinguished between rank level 3 and subordinate level 4 on the basis that the former had limestone slabs and the latter had local socio-technic artefacts, without actually explaining what energy expenditure difference was involved. In their analysis of Anaehoomalu cemetery Tainter and Cordy (1977) distinguished four major burial modes which differed "significantly" in labour expenditure. Level 2 consisted of burials with canoes, while the lower level 3 consisted of disarticulated bundles of bones. It is difficult to see how the relative difference in expenditure can be assessed in these two cases. Disarticulation of the body could be seen as involving greater effort than burial with a canoe; indeed, the disarticulated bundles could represent a different phase of the same burial programme. Inclusion of the canoe could represent a particular role in society, rather than being symbolic of a higher status. This is one of the great problems with the model: the fact that symbolic distinctions, and their meaning, are ignored when all the aspects of the

1.7

Other early theoretical analysis

work in mortuary

Although Saxe, Binford and Tainter laid the main foundations for subsequent work on the derivation of social complexity from burial practices, others have also rnade significant contributions, though to a greater or lesser degree they are founded upon the theories put forward above. Peebles and Kus ( 1977) examined the aspects of the archaeological record that could be used to identify a ranked society, and one particular aspect of their study has been much quoted by those studying burial practices. This was their prediction of two independent dimensions of social personae in the burials of a ranked society: the superordinate dimension, which represents an ordering of statuses based on genealogy and not attributable to age and sex, and the subordinate dimension, representing an ordering of statuses based on the egalitarian principles of age, sex and ability (ibid., 431). Badges of office will occur mostly in the superordinate dimension. The superordinate dimension will have infants, children and adults in every category except the apical one, whereas the subordinate dimensions should have rank correlate with age, 11

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

with probably a bias towards men in the higher ranks. Within both dimensions relative position will be archaeologically demonstrated by energy expenditure and the use of distinct symbols, particularly in the superordinate dimension. Peebles and Kus analysed burials from the Moundville site, using cluster analysis, and concluded that these two distinct dimensions could be detected, implying that the Moundville population did represent a ranked society. The usefulness of these two dimensional characteristics is that they give a more exacting test for the presence of a ranked society, rather than the usual limited notion of rich child burials as indicative of ranking. Superordinate and subordinate dimensions were recognised at the Fletcher Native American cemetery (Mainfort 1985: 576), although the terms have been applied quite loosely here.

structure society, particularly emphasising that the symbolised social structure may not be the real one. The assumption that the mortuary arena will always be an important arena for status display is also open to question. 2) The use of ethnographic evidence is too simplistic. Ethnographic descriptions tend to be generalised, not covering all the variations that would be apparent with archaeological data. The fact that Tainter and Binford's ethnographic samples gave different results (with respect to the use of grave goods, for example) also emphasises the dangers in trying to formulate crosscultural "rules", and also suggests a certain subjectivity in the selection or interpretation of the ethnographic evidence. A more inclusive approach to the ethnographic evidence is required, with particular emphasis on the variety of symbolisms that may exist within a particular society.

Brown (1981), in a paper examining the various ways in which ranking could be detected from burial remains, emphasised the fact that ranking could exist in a society that lacked centralised leadership, drawing attention to the fact that the term "ranking" can cover a broad spectrum of social situations. He summarised the three main methods by which ranking is usually detected: the energy expenditure model (cf. Tainter), symbols of authority (Peebles and Kus) and the demographic structure of the burials (particularly the presence of rich child burials). Brown noted problems with the latter, "since inherited prestige of ranked lineages can be symbolised by this means as much as inherited authority" (ibid., 30). Thus rich child burials may indicate ranking, but not authority; however, child burials with symbols of authority do provide a good indicator of the possible inheritance of centralised authority. Brown also noted potential problems archaeologically in that the elite may not be detected because of separate location, symbols of authority may also not be identified, and there is the possibility of confusing stages in a mortuary programme as representing different statuses (ibid., 37).

3) There is insufficient consideration of the different aspects of social structure, beyond the vertical. Other aspects of society, such as horizontal groupings, can have a significant impact on mortuary practices, and are worthy of study in their own right. 4) The ritual and ceremonial aspects of death and burial, and how they relate to the archaeological evidence, received little attention. 5) There is an assumption that a single attitude to death and burial applies to all members of the society. The possibility of different attitudes, and hence different responses, is generally not considered, despite the evidence that this is often the case. There is also an insufficient linkage with other aspects of society, such as the stability of social positions and economical factors, which will determine what is reflected in burial.

Thus, although there were further developments regarding the detection of social complexity from burial remains, these later theories owe much to the foundations laid by Saxe, Binford and Tainter. Peebles and Kus (1977), though emphasising the importance of symbols and demography, still rely heavily on the detection of different grades in society through differences in energy expenditure. Any studies focussing on wealth differences (e.g. O'Brien 1978, Skomal 1983) are also implicitly using the idea of status being marked by differential expenditure, in these cases limited to the consideration of the material inclusions in the grave, rather than the broader context used by Tainter. Of course, all these studies subscribe to the basic viewpoint of Saxe and Binford, that archaeologically-detected burial remains provide a reconstructible view of the associated social system.

1.8

General Tainter

Criticisms

of

Saxe,

Binford

Both Saxe and Binford's theories (and subsequently that of Tainter) centre upon Goodenough's elaboration of role theory (1965), in terms of social identities and social personae. Again, this is simply a model for social interaction, rather than being aimed at a practical examination of data, and here lies much of the problem in the application of these theories archaeologically. It is interesting to note that Goodenough comments upon the possibility of 'pretending' a social identity (ibid., 5), something which neither Saxe nor Binford picked up on as a possibility in burial, though now an aspect very much to the fore with current preoccupations with the ideology of burial. Also of interest is his distinction between the occasion and the setting of a particular identity relationship. The occasion represents a particular interaction for example, a funeral - while the setting embodies more who is present at the event. Goodenough suggests that the setting will affect the way in which a person behaves, rather than the selection of a particular identity. Thus, while a funeral is always a formal affair, the degree of formality may also be influenced by the people present at the funeral itself, possibly independently of the social personae of the deceased. This may potentially find expression in the mortuary domain; a co-resident affine, for example, may not be treated with the same degree of care if few consanguineal relatives are present because of distance from the lineage/clan settlement. Dubisch (1989: 197) commented upon a contemporary example in a Greek village, where a former resident had been placed in the

and

To sum up some of the general problems with the Saxe/Binford/Tainter approach to archaeological study of burial: 1) They assume that the archaeological evidence for burial is a faithful reproduction of the living social structure. This is a fundamental assumption and contemporary approaches now view burial as actually helping 12

Theoretical Approaches to the Study of Mortuary Practice

village ossuary in a simple tin-box, because her children lived in Athens and thus were not concerned with village opinion. Alternatively, the tomb of the Unknown soldier is particularly elaborated because of its focus for dignitaries and the nation, remembering the war dead; in this case the social persona of the deceased is deliberately anonymous so no direct connection can be made between the mortuary treatment and the individual, beyond the social identity of a soldier having fought and died for his country.

rituals than more impure castes, and had a need to appear austere, though there were contradictions in that they also had to be seen to give impressive feasts (Bayly 1980: 157-8). Other castes could "negotiate" for status by similarly shortening and simplifying burial ritual to imitate the Brahrnins (ibid.). It was with the growing realisation that the social structure represented in burial may not be a true reflection of the living society that there was a "paradigm shift" in mortuary studies in the 1980s, incorporating various new perspectives, which will be considered next.

The different attitudes and responses of individuals with regard to burial is one of the major problems with the SaxeBinford approach, though the criticism also applies to ideological approaches. Obviously, in analysing mortuary data, archaeologists subscribe to the idea that the burials are determined by particular "rules", which can be deducedand which will enable a dissection of the social structure. However, quite often it is forgotten that we are dealing with individuals, and burial, particularly because of its associations, will be subject to the modifying effects of capricious behaviour, emotional issues or other particular circumstances. Hodder (1982: 5) makes this point explicitly: "As soon as any human choice is involved, behavioural and functional laws appear simplistic and inadequate because human behaviour is rarely entirely mechanistic ". Individual choice is a factor commonly observed ethnographically with regard to burial ritual, but not always recognised archaeologically, instead being explained in terms of chronology or status differences. For example, the Kaoka speakers of Guadalcanal traditionally are buried beneath the floor of their dwelling, but alternatives - such as exposure on a rock, or burial at sea - are partly a matter of individual choice (Hogbin 1964). In addition, the choice of extended or crouched burial position is purely a matter of choice by the relatives, and the choice of cremation or inhumation is also a personal decision (as in our own society) (ibid.). Among the Bantu burial in a deep grave is the norm, but some may request that the body is thrown into bushes, because of a dislike of the concept of burial; burial for a man is usually located in front of his hut, but he may request burial inside the hut; in theory the corpse should be placed on the right side, but in practice this is not considered important and the left side may also be used (Wagner 1949).

1.9 Post-processual

approaches to burial practice

Instead of assuming that social structure is automatically and faithfully reproduced in burial practice, current archaeological approaches to its study take on board a number of considerations not incorporated in the Saxe-Binford approach. These considerations can be summarised as: 1) Status differences may be ideologically hidden in burial, so the society symbolised in the archaeological record appears to be more egalitarian than it actually is. 2) Status differences may be over-emphasised or exaggerated in burial, particularly when there is competition between individuals. 3) Burial is not automatically an important arena for status display. Other contexts may be used, and the importance of burial will depend on various factors (e.g. the stability of the social structure, religious attitudes, etc.). Burial may not be important in general, or to specific groups of individuals within the society. 4) Economical aspects of society may have an important relationship with burial practice, particularly regarding the circulation of goods. One of the fundamental underlying concepts in the modem approach is the recognition that burial actually helps create a particular form of social structure (e.g. Shanks and Tilley 1982; SJ. Shennan 1982; Rissman 1988; Barrett 1990). The ideological use of the mortuary domain has been particularly emphasised. Shanks and Tilley (1982: 130) defined ideology as "practice which operates to secure the reproduction of relations of dominance and to conceal contradictions between the structural principles orientating the actions of individuals and groups within the social formation". Hodder (1984), on the other hand, defines ideology as "meaningful social action and negotiation within specific historical contexts", which is a wider application of the term, though emphasising the importance of context. Hodder's definition seems to be more open, stressing the "negotiations" that may be expressed in burial, while Shanks and Tilley's definition focuses more on the "concealing" of reality.

Very often there may be an "ideal" of burial procedure that should in theory be followed universally but which, in practice, is not followed at all. In Chinese rural cemeteries, for example, burials are supposed to be located specifically with regard to age, sex and seniority; thus seniority with regard to generation should be marked by burial on higher ground, and for burials at one particular level, the senior burial should be to the left of a junior, with husband and wife side-by-side, the man to the left of his wife (Hsu 1949). In practice, though, different generations are inter-mingled in the graveyard, and husband and wife may be placed in different parts, or even in different cemeteries (ibid.) The assumption also exists that all individuals had the same desire or compulsion for status display in the mortuary domain. It needs to be determined to what extent mortuary behaviour was explicitly defined, as opposed to individualistic behaviour attempting to compete in terms of status display. Also allied to this is the assumption that individuals at different levels in society had the same desire or capacity or tendency to materialise their status in the mortuary domain. This may be untrue; the Brahrnins of India, because of their greater "purity", tended to have shorter and simpler funerary

Death has always provided an opportunity for political manipulation. with Taylor (1989), for example, speaking of the "uses of death" in Europe. The construction of tombs in the Neolithic to mark out territories (e.g. Renfrew 1973) and promote ideas of egalitarianism (e.g. Bradley 1984, Sharples 1985, Hodder 1990), and the propaganda value of the paramilitary funeral in Northern Ireland (e.g. Prior 1989) are

13

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

good demonstrations of these uses. Representation of "ideal" social structures was commented upon by Pader (1980) regarding the Merina of Madagascar. Here, archaeological interpretation of the mortuary remains corresponded closely to the ideal conceptualisation of their society, rather than what the organisation actually was at the time of burial.

observation. On a regional scale, he notes that for the Nuba cemeteries are closely tied to settlements, suggesting that ancestral links to the land are important and have remained stable, despite population shifts and "a wider and more complex Sudanese economy" (Hodder 1980: 164-165). This, Hodder suggests, indicates that burial customs do not provide a realistic reflection of what social and economic changes may have occurred. At the cemetery scale of analysis, he noted that there were clusters of burials, but these clusters could be composed of people from off-shoot communities, and females (because of patrilocal residence) were usually buried in their home communities, rather than where they had been living since marriage. Thus "who gets buried where is complex and certainly reflects directly neither the composition nor the nature of the living social and settlement pattern" (ibid.,165). Hodder notes that the burial clusters represent "the ideal of matrilineal groups", an ideal which is only partially true in practice. At the most detailed scale of analysis, within-grave variations, Hodder points out how the Nuba people's particular attitudes to death especially the idea of the polluting nature of death- shapes their burial ritual, and it is only because of these beliefs that status, age and sex are materialised in the burial. He suggests that a change in such beliefs could result in less differentiation being apparent in the burials, giving the misleading impression of less social complexity: "If age, sex and status are not differentiated in grave content, this does not mean they were not differentiated in life" (ibid., 166). Hodder further demonstrates the impact of attitudes on burial with examples of Gypsy and Merina burial practices, concluding in summary that a "new approach" to the study of burials was required, in which the impact of attitudes and the need to explain the position of burial within a society are emphasised.

In many ways, mortuary symbolism gives ample opportunity for creating an artificial status - the status display involved need not be sustained over a long period of time, unlike during life, and following burial or secondary treatment, the energy expenditure is pretty much complete. The temptation therefore exists to attain something that was otherwise unattainable by the living person. This was recognised long ago by anthropologists, such as Victor Turner, but was not considered in the Saxe/Binford approach. Turner, in his book "The Ritual Process" (1969) notes: "The structurally inferior aspire to symbolic structural superiority in ritual; the structurally superior aspire to symbolic communitas and undergo penance to achieve it" (193). There is no reason to assume that people did not exaggerate their status. This can be observed even in relatively recent times; in Highgate cemetery, for example, one particular grave stone gives a detailed genealogical descent from the Saxon God Woden. The reverse situation may also exist, as Turner observes, with the status represented in mortuary ritual reduced in comparison to the reality. Such a situation may arise through tradition, as in the simple burials of Saudi Arabian kings (Chapman and Randsborg 1981), through levelling mechanisms which legislate against excessive differences in mortuary display, or a simple lack of compulsion to communicate though mortuary practice a status already universally recognised. The question of misrepresentation in the mortuary domain applies not just to the separation of individuals into different rank levels, but also is relevant when it comes to considering the social identities that may be presented (or interpreted by the archaeologist) as appropriate in burial. For example, amongst the Bontoc Igorot burial with a spear and battle axe represents status as an unmarried male, rather than the warrior status that an archaeologist may infer (Saxe 1970). Harke (1991) raises a similar point when he interprets the weapon burials of the early Anglo-Saxon period as symbolising ethnic differences, rather than role differences; this explains the presence of weapons with the very young and very old, and with individuals suffering various diseases, such as osteoarthritis, that would have made the use of weapons in a warrior role impossible. Thomas (1991) also notes the occurrence of a Beaker burial with a bow and archer's wristguard, despite the individual having suffered from ankylosis of the spine; the artefacts therefore possibly identified idealized roles, relating to resources important to the community. Obviously the danger here lies in attempting to make a direct connection between particular objects placed in the grave and a function that they might have performed during life as used by the deceased.

In some respects Hodder was only drawing attention to caveats that have been noted by other workers, or points readily observable by anyone taking more than a cursory look at the ethnographic literature. The point was, though, that attention had not been previously drawn to these problems in archaeological circles, and this again emphasises the selective approach to ethnographic data that has characterised archaeological studies of burial. Goldstein (1976) noted the possibility of different communities burying at Schild Knoll B, so it should come as no surprise that a cemetery may be multi-community. The fact that patrilocal post-marital residence may result in females being buried in their home community also comes as no surprise, but certainly is not a hard-and-fast rule; Goody notes that for the Lodagaa burial tends to be local because of the understandable dislike of transporting a dead body over any long distance (Goody 1962: 142). Monnig (1967:138) observes that often women are buried among affinal relatives, once permission has been obtained from her consanguineal relatives. Amongst the Mapuche Indians, who have patrilocal residence, a married women dies amongst her husband's people, and they are responsible for her burial (Faron 1967). The period of time for which a woman has been married may also be a determining factor; Wagner (1949) notes that, for the Bantu, a women who is only recently married may be buried at her father or brother's homestead, but only if her relatives insist on bringing her back. Even if a woman is buried in her original community, it is most likely that, with the limited means of communication we can assume of prehistoric societies, this will be limited to communities which exist in reasonably close proximity and have close affinities, so that

Hodder (1980, 1982a,b,c, 1986) has been one of the main critics of attempts to gain a realistic picture of the true social structure from the burial evidence, and it is his criticisms which have led to attention being focused on ideology. In his ethnographic study of the Nuba of Sudan, he notes contrasts between the sort of assumptions that are made in the archaeological analysis of burial remains, and the practical realities observable ethnographically, on three scales of 14

Theoretical Approaches to the Study of Mortuary Practice

Hodder's criticisms about the artefacts present in the cemetery coming from different settlements will hardly be of major consequence. The further apart the communities of husband and wife are physically, the less likely it is that a woman will be returned for burial to her home community, even if this is the ideal. With the advent of improved, twentieth century communications, such an ideal may be more readily followed and observed in some ethnographic societies Goody (1962:52) observed that motorised transport greatly extended attendance at funerals of the Lodagaa - but its effect in the past, certainly from an analytical point of view, is probably negligible.

possible social structure, and which can be difficult, or impossible, to challenge. Thus, until our understanding of the processes at work in mortuary representation is better clarified, concentration on ideological aspects, in isolation from other aspects of burial, may in some circumstances be misleading, since attention is directed away from the fundamental problems of mortuary analysis, and no attempt is made to address them. An example may help demonstrate potential fallacies in ideological approaches to burial data, and the need to assemble as much ethnographic/historical data as possible, to guide theoretical interpretations. Shanks and Tilley, in a paper published in 1982, investigated the symbolic significance of human bone sorting in British and Scandanavian Neolithic long barrows. They placed particular emphasis on the symbolic value of the body, stressing the number of ways in which it can be viewed (left-right symmetry, upper and lower parts, anterior and posterior, transitional parts like the pelvis and scapula etc.). Shanks and Tilley suggested that these groupings can be manipulated to reflect particular ideologies in the living society (Shanks and Tilley 1982: 134-5). They noted a number of patterns in the skeletal remains, including the selection of specific bones, before or after interment, varying frequencies of different bones between different piles - at Luckington barrow, for example, there was a separate pile of ribs, vertebrae and phalanges-, and an emphasis on left or right limb bones; at Fussel's Lodge and Lanhill there was a bias towards left limb bones, for example. More complex pattern existed in the Scandanavian tombs. Ramshog had more right than left upper limb bones and more left than right lower limb bones, while Carlshogen there were more left than right upper limb bones, and more right than left lower limb bones (ibid., 135150).

Hodder's points about attitudes to death are certainly valid, though his suggestion that a change in attitude amongst the Nuba resulting in death being less feared as a pollutant might result in less differentiation in the burial rite can be queried; this point of view sees burial as being structured by a single attitude, rather than a mesh of attitudes; even if death is no longer feared as impure, the desire to display status probably a fundamental human attribute - will still exist as an influential force in burial. In any case, Prior (1989) argues that it is death as an ambiguous social category, rather than necessarily in a physical sense, that labels it as "polluting", so that to a certain extent in all societies it may be seen as "impure", regardless of actual burial practices. The basic problem exists in that burials are often treated in isolation from the other archaeological evidence, such as settlement data or ritual deposits, which may be indicative of potential changes in attitudes, such as alternative forms of status display (e.g. sacrificial deposits of metal in Denmark at end of the second millennium B.C.; Jensen (1982)). Following Hodder's papers questioning previous approaches to mortuary analysis, subsequent work dealing with the theoretical side of mortuary practices have concentrated almost exclusively on the ideological aspects of burial. Parker-Pearson's much-quoted comment that "social systems are not constituted of roles but by recurrent social practices" (1982: 100) sums up the ethos of this approach. However, although such approaches are valid, the emphasis on philosophy and sociology sometimes makes it seem that a detailed study of the actual archaeology has become almost irrelevant. In trying to gain these broad perspectives the detailed evidence, and the specific information it contains may become pushed to one side While it would be true to say that a particular social structure is "greater than the sum of its parts", it seems only common sense that the parts as such should first be clearly identifiable and understood before proceeding to an even higher level of abstraction. ParkerPearson's comment can be refuted by a casual look at our own contemporary social structure; there may be "recurrent social practices" at work in creating the social structure, but at any point in time it would be ludicrous to deny that roles can be identified and have relevance - there is a class structure (even if loosely defined), people have particular functions in society (occupational, parental, marital etc.), they have particular religious beliefs, and belong to particular associations (sporting, Freemasons, Women's Institute etc.). This should be of as much concern to the archaeologist as any "hidden agenda" of status manipulation. While not denying the value of ideological studies of burial practice such approaches often make good use of the broad time frame that is commonly seen as one of archaeology's strengths there can be problems in that an "explanation" can be given that doesn't have to be explicitly fitted to details of the

Shanks and Tilley explain these patterns in terms of several principles, amongst which were: 1) Bone sorting was a means of disguising status differences in the living society: "The regrouping of the disarticulated remains may represent an assertion of resonance between essentially discrete individuals, and thus a denial of aysmmetrical relationships existing in life" (1982: 150). 2) The bones were mixed following the properties of the body: The regrouping of the disarticulated remains was carried out incorporating basic body symmetries such as body/limbs, upper/lower, right/left (1982: 150). Shanks and Tilley's paper is useful in showing some of the processes that may be involved in human bone sorting. However, they use ethnographic evidence in an abstract sense, with the processes they suggest relating more to the ideas of anthropologists, rather than what people necessarily did or thought themselves, in a practical sense. It is clear that they have taken a singular, ideological approach to the "meaning" of bone sorting, interpreting it in terms of dominant groups "misrepresenting" the nature of society as being egalitarian. While this is a possible interpretation, other, more specific ethnographic evidence can suggest other interpretations. For example, the Ifugao people of the Philippines use a form of collective burial, called a lubok (Barton 1946). This structure is not a tomb as such, but 15

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

resembles an artificial cave, dug into a bank. Barton actually witnessed the periodic clearing out of one of these tombs:

leave the same archaeological traces. If all the contextual archaeological evidence suggests, for example, that a society is hierarchical, but the funerary remains indicate an egalitarian society, then a number of explanations could be forwarded:

The floor was covered with small and large bones which the Ifugao swept outside, for he subjected the sepulchre to a general cleaning at this time. There was a bench-like ledge at that farther side on which rested four heaps of bones of fairly recent corpses in various stages of decay. One of these - the most decomposed he dumped into a shroud and took outside. Then he sorted out the large bones and the long bones of the sweepings and returned them to the sepulchre. The ribs and vertebrae he threw away. He took the bones of the ancestor to his house and put them on a large beam under the floor (Barton 1946: 197).

a) There could actually be ideological manipulation controlling burial so that status differences are represented as being minimal. b) Status distinctions could be symbolised in burial in ways which are not archaeologically-recoverable - for example, through ritual, or, in the case of collective burial, by symbolising status through the construction of a tomb, even though the tomb can be subsequently utilised by other people, as in the case of the Tanala of Madagascar (Linton 1933). The archaeologicallyrecovered aspects of funerary ritual, therefore, may give the appearance of egalitarianism, while the entire ritual itself may have more accurately reflected social structure.

This description shows a number of things. Clearly, the tomb was cleared out at regular intervals, with the oldest bones thrown out. Some of these, the long bones, are kept while others are disposed of completely. Some bones are also taken back to the long house, probably for a ritual or protective purpose. Apparently when new bones were placed in the tomb, older remains were swept on to the floor to make way for them, if needed, so an archaeologist excavating here would find a jumble of bones.

c) The availability of other display arenas not associated with mortuary practice; this may make display in burial unnecessary.

However, Barton also witnessed further sorting of the bones, of more direct interest to the interpretations of Shanks and Tilley. Unsurprisingly, the bones in the tomb became mixed up over a period of time, and it was considered important to try and keep together the bones of a particular skeleton. This was a semi-ritual procedure, involving the consultation of what was called an agba stick. The individual sorting the bones would collect together several bones of one particular type from different skeletons, and would "question" the agba stick. This questioning would be along the lines of "Does this bone belong to ancestor A?". Supposedly if the answer to the question was yes, the stick would increase in length (ibid.).

d) Religious beliefs and general attitudes to death, burial and ancestors: this is clearly a very important consideration, as noted by Hodder (1982a). Religious belief, though in itself a form of ideology, may control the amount of display in burial; in contemporary Western society, overt display in burial would be viewed as "unseemly". Additionally, the actual form of belief itself will influence physical burial. There may be a belief in an egalitarian after-life, at odds with grandiose display, and the conceptualisation of the soul can be influential. Graslund (1994: 19) notes that in societies where the soul is believed to leave at the moment of death, inclusion of significant grave goods may seem pointless: "As a consequence, in such societies, the social and ideological needs of the survivors must be manifested otherwise than be grave goods".

This example is interesting because it represents an attempt to put bones together to form a single person. Inevitably since it involves "talking" to a stick - the bones of different people will get mixed up. Faced with such evidence, Shanks and Tilley would interpret it as an attempt to symbolically hide status differences in the living society. In this case, though, it would be simply the end-product of a misguided attempt to keep the bones of one person together. There was no hidden ideology apparent, or, at least, not one that attempted to "secure the reproduction of relations of dominance". Hodder (1986) also commented on Shanks and Tilley's interpretations, and suggested that the left/right emphasis on body parts could be explained in terms of male/female negotiations rather than the power relations they suggested. This again emphasises the need to keep an open mind when applying ideological interpretations to burial.

1.10 Problems with ideological burial Several problems with ideological archaeological burial are apparent:

interpretations

of

interpretations

of

e) Conservative behaviour: it cannot be automatically assumed that what is symbolised in burial relates to the contemporary social structure or, if it does not, that this represents ideological manipulation. Again, this is dependent on attitudes towards burial. f) The archaeological record may not include the full range

of burials that were representative of the society, or links may not be made by the archaeologist between these different practices. This may produce a bias, in the sense that only a sub-stratum of the population may be observed, giving the impression of "egalitarianism", though status distinctions would be quite clear in the living society. The archaeologist's problem is attempting to decide which of these scenarios is appropriate. There may be ideological manipulation, or there may be other explanations. The fact that so many different interpretations may be fitted to one particular pattern in burial data emphasises the need for a wide-ranging and unbiased study of ethnographic and historical attitudes to burial. By doing so, as many different

1) The major problem seems to be distinguishing what is seen in the archaeological record as representing "ideological manipulation" from other processes that may 16

Theoretical Approaches to the Study of Mortuary Practice

alternative explanations as possible can be considered. The archaeologist must then use his contextual knowledge to apply the explanation that best fits the data - or, probably more realistically, suggest the alternative processes which are most likely.

proven using archaeological data (e.g. Brown 1981, O'Shea 1984). It must be remembered that the data used in the testing of these theories consisted of the description of the mortuary practices, rather than the raw cemetery data, which is all that the archaeologist has available to him, and which any theory must be applicable to. The dangers in using ethnographic description as a reliable source of information are, of course, well known, and the particular danger here is that the descriptions given of burial may be the "ideal" procedures which in practice are rarely closely followed, or they may describe only a limited range of the practices used, undermining archaeological theories thus derived. Ethnographic descriptions inevitably fail to encompass the true variability that an archaeologist witnesses in the material remains. The limitations of ethnographic descriptions, particularly regarding material aspects and patterning, has led to the growth of ethnoarchaeology (e.g. Hodder 1982a, Parker Pearson 1992) in which simpler societies are studied in the field with particular interest focused upon the material remains that might be seen archaeologically. This approach bypasses many of the problems of traditional ethnographic description, from the archaeologist's perspective, but the number and nature of such studies is currently quite limited, certainly as regards mortuary practices; inevitably, the time scale of study here is a major obstacle.

2) Ideological interpretations of burial practice depends on identifying the social structure which is represented (real or otherwise) in the burial evidence - for example, whether the structure represented is egalitarian or hierarchical. Whether this is a true reflection of society is incidental, initially, to this identification. Considering the difficulties that have been encountered in pigeon-holing certain societies as egalitarian or hierarchical from interpretations of burial data (e.g. Shay 1983 versus Palumbo 1987), there may clearly be problems in suggesting ideological misrepresentation of a society as "egalitarian" . Such an interpretation depends on demonstrating that the burial data is intended to symbolise an "egalitarian" society, which therefore requires definition of "egalitarian", "hierarchical" or whatever, in burial terms. This inevitably leads back to the work of Saxe, Binford and Tainter, whatever their flaws, since they were at least concerned with such considerations. 3) Ideological misrepresentation can cover a number of different aspects of burial, not simply that relating to control by dominant groups, in the sense of an elite. Clearly there may be competition between groups of equivalent status (e.g. Parker Pearson 1982), male-female competition (e.g. Hodder 1986,1990), and misrepresentations in other senses, such as age, marital status or even gender (for example, "rule breaking" noted by O'Shea 1984, to mark out high-status females). Therefore, practically all social dimensions symbolised in burial can be manipulated in some way or another, and these need to be studied in their own right, rather than focussing specifically on elite "ideologies". These other ideologies, after all, are just as revealing about the nature of the society and the varied attitudes that existed.

Despite these problems relating to the use of ethnographic evidence, it is none-the-less indisputable that it must be used to guide interpretations of archaeological mortuary practices. In fact, criticisms of the Saxe-Binford approach have themselves used ethnographic examples to demonstrate the problems in interpretations (e.g. Hodder 1980), and careful study of this evidence in the first instance would have drawn attention to the various caveats that are now recognised as applying. Various archaeologists, such as Chapman (1987), Veit (1992), Graslund (1994) and Brown (1995), have emphasised the necessity of ethnographic evidence for the interpretation of burial. Veit, in studying burials within settlements, commented on the dangers of using analogies from different regions (applying African examples to Europe), but this was a lesser problem compared to that of the imposition of the archaeologist's own attitudes and experiences of death upon the archaeological evidence (1992: 108). Graslund (1994: 16) has noted the often sub-conscious use of analogy: " ...archaeologists sometimes totally reject the use of cross-cultural analogies, unsuspecting their own consistent and implicit use of them". In suggesting any archaeological theory, the archaeologist inevitably will be drawing parallels from his own experiences and knowledge to test the validity of the theory; this will define, for him, what would constitute a completely outrageous and unlikely suggestion, and what may be acceptable. Ethnographic evidence simply represents a means of broadening this knowledge, with the particular advantage of coming from societies that may be closer in structure to those seen archaeologically. Given the ritual nature of burial, and the unusualness of some practices from our own perspective (e.g. bone sorting, exposure, "killing" of grave goods), together with the lack of material emphasis in burial in contemporary Western society, this clearly is a necessity for social interpretations of burial remains; in Scarre's words (1994: 79) these analogies "can make the archaeological data less mysterious and alien ...".

4) While it is quite possible that there may be ideological manipulation of burial to misrepresent the nature of the society, it is also quite possible that the burial structure may be a reasonably accurate representation of social structure. Just because it can be demonstrated that there are some contemporary and ethnographic examples of status misrepresentation, it does not follow that this is always the case, or even the case in a majority of situations. The evidence used by Saxe, Binford and Tainter, whatever the criticisms of their theoretical approaches, and while noting the problems of ethnographic data, does potentially indicate that quite often social structure may be represented in burial. Therefore it is necessary to try and identify under what circumstances ideological misrepresentation may be present, and why it should be utilised in these circumstances and not others. The only way in which this can be done is by a broad investigation of ethnographic and historical evidence.

1.11

Use of ethnographic/historic study of mortuary practice

examples

in

The use of ethnographic analogy in archaeology, and specifically in the interpretation of burial practice, has had a controversial history. The central theory in mortuary analysis developed in the 1970s (Saxe, Binford, Tainter) was derived through the use of ethnographic descriptions, and it has been questioned how appropriate these theories are when not

Graslund also comments that the random use of ethnographic analogy may be better than a systematic approach. The 17

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

problem with searching ethnographic literature for examples relating to a specific theory (for example, Tainter's energy expenditure theory) is that the evidence is been viewed from a particular perspective, rather than an open-minded approach to the different processes that the evidence may suggest. Tainter suggests that in his ethnographic sample artefactual inclusions were rarely considered to be of importance. If this is true, it questions the usefulness of much ethnographic evidence, since in many archaeological cases burial artefacts are the most prominent aspect of burial, materially. However, Tainter's conclusion may have resulted from an inherent bias on his part (or, in fact, that of the ethnographer) towards the most striking aspects of the burial (Tainter 1974). The example of Shanks and Tilley's ideological interpretation of bone sorting, noted above, was based on a generalised ethnographic analogy; the use of a specific example suggested other non-ideological interpretations.

1.12

whether the Saxe-Binford approach, or that such as Shanks and Tilley - share a common fault in that explanations tend to be in terms of a single prevailing attitude held by all individuals. Escaping from this mode of thought requires an all-embracing study of the ethnographic, historical, contemporary and archaeological evidence for burial practice. Particularly, there is a need to focus on the four main social dimensions that structure burial - age, gender, vertical divisions and horizontal divisions. Other social dimensions, such as circumstances of death, are also important, but it is these four dimensions, and their interaction, which basically structures the ethnographic and archaeological burial process. Although these dimensions have been examined previously, such as in Binford's cross-cultural sample (1971), they have been considered as general categories. This neglects the fact that each of these terms encompasses a variety of other social identities and related processes. For example, the social category "age" encompasses various different elements. Different age categories may be culturally defined and may influence various elements of mortuary practice; age will be related to marital status and subsequently to other social aspects such as parenthood (and age/sex of children), which may find reflection in burial. By studying all the aspects of each of these categories, and particularly the attitudes that centre around them, an insight can be gained into the kind of patterns existing in burial data, and the social information they may contain. This then becomes a building block upon which to base more abstract interpretations of burial evidence, such as the impact of ideological manipulation.

Conclusion

In conclusion, current approaches to the study of archaeological burial remains have focused particularly on ideological interpretations. While this is certainly one avenue that requires much exploration, concentrating too much on this one "attitude" to burial can be potentially misleading, since almost anything can be given an ideological explanation, even if other processes are, in fact, at work. The earlier work of Saxe, Binford and Tainter has some serious flaws, yet at least there was a direct concern with the burial data itself and the patterns that undoubtedly exist. The concern of the archaeologist, initially at least, should be with these patterns, and their potential meanings. It is only when archaeologists understand what is being symbolised in burial in the first instance that judgements can be made as to the reality or otherwise of these patterns in terms of social structure. There is a particular need to focus on the varied attitudes that exist in relation to burial, not just in the sense of change though time and between different societies, but from the perspective of different attitudes co-existing within a single society at one point in time. Whaley, in criticising Aries' idea (1981) of a chronological progression of historical attitudes to death and dying, summarised the complexity of the situation:

Allied with a theoretical consideration of the social dimensions structuring burial is a need to investigate the quantitative methods that typically are used to extract the patterns present, these hopefully representing dimensions of social interest. The following four chapters will deal individually with the main social dimensions (age, gender, horizontal, vertical) in theoretical terms. Following this the most common multivariate methods of burial analysis will be discussed, leading on to the central part of the dissertation - an objective assessment of how well these methods uncover the social dimensions of burial practice when applied to artificially-modelled cemeteries.

.. .it is futile to look for one common attitude to anything in any given society. The assumption that all human beings will think the same way at any time is one which our own experience of the present should indicate is not a valid one to impose on the past. The implication is thus that attitudes must be, indeed can only be studied meaningfully in close relation to specific geographical, religious, social, economic, political and psychological circumstances, bearing in mind that there is bound to be an almost infinite diversity both among individuals and also among groups in the same society (Whaley 1981: 9). Undoubtedly, therefore, it is impossible to impose one particular underlying attitude upon the archaeological record of burial, and the sheer complexity of interacting factors mentioned above particularly emphasises the need to examine burial remains within the context of all other aspects of society that are archaeologically available. Unfortunately, archaeological theories regarding the interpretation of burial -

18

Chapter 2 The Age Dimension in Mortuary Studies 2.1 Introduction

of a social persona. Even if disposed of in a formal cemetery, the location may reflect this non-entity status; at Cammoney cemetery outside Belfast, still-born children have a plot, near a public dump, which used to be for pauper burials (Prior 1989: 116). There are cultural exceptions, however, to this general trend. Thus in Japan there are special memorials for aborted foetuses, motivated not out of status recognition but basically out of fear of revenge (Barley 1995). The age at which an infant will receive recognition as having a social persona varies from society to society. For the Mandari of Sudan, a child which dies before it is three days old is buried without ceremony; after three days, though, the child is named and then receives some degree of ritual with burial (Buxton 1973: 146), a distinction not dissimilar to that adopted by the Catholic church regarding baptised and unbaptised children. A further stage is attained when the child is 8-9 years old, the point at which the lower front teeth are removed, and grave poles may be erected over the grave (ibid.). Among the Lodagaa, the distinction is made between weaned and unweaned children; the latter are considered to lack a social persona and receive a different form of burial in which they are not actually placed in the ground (a mound is placed over them) as this is considered to be dangerous to the fertility of the crops and women (Goody 1962). In Roman Britain children under 18 months were usually disposed of casually, but after this time there seemed to be some recognition of social persona, with children being buried with adults in cemeteries, though in some cases distinctions were maintained by placing the children in peripheral locations (Philpott 1991: 232). Locational distinctions, if children are buried at all, seem to be quite common, as Binford (1971) noted. Thus for the Pedi of the Transvaal, infants were buried inside the hut, while young children were interred under the eaves of the hut, in opposition to adults, who were buried in the cattle kraal or courtyard (Monnig 1967: 139). Some researchers have linked the presence of infant burials under houses with female strategies counter-acting male dominance (Hodder 1990; Scott 1992), but such theories are difficult to comment upon or objectively criticise. The placement of children in association with houses has been alternatively explained as reflecting less fear of pollution in comparison to adult corpses (e.g. Kurtz and Boardman 1971). Links may also be made between the house and the womb so that in north Sudan, for example, miscarried children are buried inside the house, while stillborn children are placed near the outer wall, symbolising the fact that they existed outside the body, but not in the outer world (Barley 1995: 137).

Age is a factor in mortuary analysis that is not always adequately considered, despite its omnipresence and capacity to modify many aspects of interpretation. Frequently it is seen as being an important factor in only egalitarian societies, yet age is an important dimension in all societies, whatever their complexity, and may encompass a significant proportion of the mortuary symbolism in non-egalitarian societies; in fact, in Binford's sample of 40 societies (1971), age was more commonly symbolised in burial among settled agriculturalists than for the simpler hunter-gatherers. Goodenough (1965) particularly noted that age, and sex, were always relevant social identities in any given interaction. Given the ritualistic nature of burial, and the specific connections between death and age (a person who dies "before their time" is, even in contemporary society, viewed as having had a "bad death"), this factor is clearly in need of careful consideration. However, recognising and accounting for the influence and symbolisation of age within the burial domain is often difficult and may potentially lead to misinterpretations as a consequence. Archaeologists, in analysing and comparing cemeteries, tend to look at age from two main perspectives: 1) Recognition of particular symbolic markers of age. 2) Determination of how wealth relates to age: a) with particular reference to wealthy child burials. b) the relationship with age in general.

2.2 Symbolic markers of age 2.2.1

Introduction

Frequently, the patterning of burial remains within a cemetery reflects the symbolic recognition of age categories within the population, and potentially their importance to the functioning of the living society. Such symbolic recognition can take almost any form in the mortuary domain - location, orientation, positioning and treatment of the body, structure and dimensions of the grave, grave artefacts and so on though in Binford's ethnographic sample age was symbolised only by body disposition, grave form and location (1971: 233). While most of these distinctions relate usually to adultsub-adult differentiation, the burial goods, and the particular combinations they occur in, may reflect a more precise and wide-ranging distinction into age categories which, when recognised, can be revealing about the society.

2.2.2 Sub-adult

symbolism

Particular artefacts may accompany sub-adult burials, reflecting these transitionary periods or certain beliefs of the society. The ancient Greeks placed a small juglet in the grave of a child that had not reached its third year. This was because it had missed its first Anthesteria festival, which marked admission into the religious life of the community (Garland 1985: 82). This example is interesting because a negative situation - not having achieved a particular level of social integration - was being marked by a positive attribute in the mortuary domain. Young graves, along with the graves of those who had died violently, were also frequently used to "deliver" curse-tablets to the Gods, because of their unusual

in burial

Looking firstly at sub-adults, it is clear that there are culturally specific divisions relating to age that segregate this category into further sub-categories which may determine particular mortuary treatment. Still-born children provide one category that are treated practically universally as nonentities, even within our own society (e.g. Prior 1989: 173); basically, they are treated as "things", "barely considered to be human beings" (Gittings 1984: 83), the ultimate lacking

19

Theoretical and Quantitative Approaches to the Study of Mortuary Practice status (ibid., 6). Turner (1969) has also commented on the ritual significance of some children (see section 2.3). Ritual positions relating to children have been suggested in some archaeological analyses. At Oleneostrovski Mesolithic cemetery, O'Shea and Zvelebil (1985) noted the presence of effigy figures in graves of variable age, including the young, which they interpreted as representing a hereditary position relating to ritual, but independent of wealth.

2.2.3 Symbolic sub-adult

distinctions

between

spears also seemed to have some correlation with adulthood, being found with males aged twelve or over (ibid.); Pader also noted that these items need not have any association with active warfare. Age also seemed to be more formally marked for males than females, for whom the size of the artefact was the usual way in which age was symbolised (ibid.). Another interesting aspect that Pader noted was the contrast in mortuary patterning between the two cemeteries of Holywell Row and Westgarth Gardens. In the latter cemetery, the positioning of the bodies and the selection of artefacts for burial seemed to be more closely regulated with regard to age, while at Holywell Row such patterning was more obscure, as a consequence of "different social relations being emphasised" (Pader 1980: 149). This is despite the fact that the two cemeteries are only 19 km apart, and are of a similar period (ibid., 158). The differentiation in patterning in such a small spatial area emphasises the need to be aware of differing attitudes to age factors and their representation; O'Shea (1984) also noted differing degrees of age symbolism in the cemeteries he studied. Pader also emphasised the fact that symbolic indicators of age could change through time and space; in contemporary Slovakian communities an object may represent a child at one time, and perhaps an adult some years later, and a form of dress that symbolises an unwed mother in one village may symbolise a virgin in another (Pader 1982).

adult and

Probably the most important, and archaeologically-visible, transition relating to age is that to adulthood, which may find recognition in various different ways. It is important to realise that, as well as adulthood being attained at different ages for different societies, not all individuals within a particular society may attain adulthood at the same chronological age, depending on ability and other factors (Mair 1972); this inevitably will result in a "fuzziness" in the artefact patterning. Initiation into adulthood may involve several stages of ceremony. For the Kiwai Papuans, the stages for a boy include uninitiated, partially initiated, unmarried young man, married young man and "proper" man (Landtman 1938). These different stages may be correlated with particular ornamentation and the use of particular objects. In certain Chinese communities, true adulthood, as represented by marriage, is symbolised by burial in the family graveyard, as opposed to the use of a public graveyard before this state is attained (Hsu 1949). However, the community may use other symbolic expressions to mark transitional periods before this. The funeral procedure involves the display of "spiritual tablets", which are placed on the family altar. Children under the age of twelve do not have a tablet, those over the age of twelve but unmarried have a bare tablet, while married individuals have a tablet which is enclosed within a case (ibid.). Particular circumstances may modify such procedures, as noted by Ahem (1973) in a Chinese village in Taiwan. Here, a subadult who died was considered to be the incarnated spirit of a person to whom the sub-adult's parents owed money, and would not be considered a lineage member; consequently, a tablet is not placed in the ancestral hall (ibid., 125-6). However, if a lineage member falls ill following the death of the child, this may be attributed to the latter; in these circumstances the child is considered to be a full lineage member, and has a tablet placed in the ancestral hall (ibid.). These age distinctions would not, of course, appear in the grave itself. This example emphasises how superstition or fear, rather than ascribed status, can result in some children being differently treated from the majority.

The explanation for the age patterning of artefacts may be cross-cut by other groups. Harke (1991) has suggested that the "warrior" burials in early Anglo-Saxon cemeteries may, in actual fact, represent ethnic differences, perhaps being symbolic of people of Germanic origin (ibid., 154-5). This explains the fact that the weapons are found with both the old and the young, including those who physically would have been unable to use the weapons placed with them (ibid., 153). In addition, there was age symbolism involved in the choice of weapons. Arrowheads are only found with children aged 14 or under, while adults only have a seax or an axe; juveniles, however, may have heavy swords, so there was no practical reason for this differentiation. Harke suggest that there may be three main stages of age symbolism: c. 3 years, perhaps relating to weaning, 12-14 (puberty) and 18-20, representing full adulthood (ibid., 156-7). Again, this example demonstrates the need to take into account the various symbolisms that may be at work, and particularly the possibility of different ethnic groups within the same population using different systems of age symbolisation; this could produce more complex age-patterning than in situations where a single system of age-patterning (relating to the presence of only one horizontal grouping, for example) is present in a cemetery. Age distinctions between adults and sub-adults are often marked ethnographically and archaeologically not just by the type of artefacts associated, but also also by their positioning, size and the material from which they are manufactured. At Branc, Sherman (1975: 282) observed that the placing of willowleaf rings was related to age. Children wore them as armlets, adults used them as ear-rings, and juveniles used them in both contexts. Positioning of artefacts can relate to more than just stylistic differences, but can be used to signal different symbolic "meanings"; in the Mokrin Bronze Age cemetery in Hungary, badges of office inherited by sub-adults were not placed in the correct functional position in the grave but could be placed, for example, at the feet, to symbolise the fact that the role was not actually held

Particular artefacts may be used to mark the attainment of adulthood. Initiation into adulthood among the Dinka of Sudan is symbolised by the giving of a well-made spear (Deng 1972), and weapons often seem to symbolise adulthood in males ethnographically and archaeologically even if there is not necessarily a connection with warfare. Spear and battle axe were used by the Bontoc Igorot as symbolic of unmarried male status, and not warrior status, as might be assumed (Saxe 1970), and this reflects the fact that weapons may have an inherent fertility symbolism. Pader's study of Anglo-saxon symbolism (1980, 1982) was particularly interesting with regard to adulthood symbolism. A shield was the only definite indicator of adulthood, and 20

The Age Dimension in Mortuary Studies before death (O'Shea 1995: 130). In the Hallstatt cemetery Hodson (1977: 403) noted a slight bimodality in the diameter of bracelets which may have indicated a dichotomy into child and adult sizes, and in Anglo-Saxon burials the size of the cremation vessel may be proportional to the age of the deceased (Richards 1987). Bronze Age cremation urns may also show variation in volume that relates to status as child, adolescent or adult (Allen 1987). Differences in the size of spear-heads in Anglo-Saxon cemeteries, however, does not just relate simply to an adult-sub-adult dichotomy, since the oldest males tend to have the largest examples (Harke 1991:158).

century Anglo-American cemetery in Virginia showed that the child graves had been distinguished by the use of red sandstone grave markers (Little et al 1992). Children may receive different mortuary treatment, as in the case of the Merina, who do not break children's bones in the way that adult bones are (Bloch 1971), or at the Mississippian Turner site, where most children are buried as single skulls, while bundle burial is restricted to adults (Black 1979). At the Angel site in Indiana, dating to the middle Mississippian period, adults were noted as being more likely disarticulated than sub-adults (Schurr 1992), and disarticulation was also limited to adults in some of O'Shea's (1984) cemeteries, such as Barcal, probably because of the greater effort involved.

Children may receive "toy" versions of adult artefacts which may, for example, substitute wooden spearheads for metal spearheads (Wood 1868). This possibility of organic substitutes for adult objects in child burials should not be ignored. Frequently it is assumed that non-preservation of organic materials is a factor that affects all social categories equally. However, if there is a tendency for ascribed social identities to be symbolised in sub-adult burials by organic equivalents - perhaps for reasons of ritual or economy - then a misleading impression of egalitarianism, regarding subadult inheritance of social identities, may be obtained. The very flexibility and ambiguity that characterise symbols make this not unlikely in some circumstances. The Nuer considered an ox and a cucumber (as substitute) to be ritually equivalent, despite the very apparent physical difference to somebody external to the ritual system (Metcalf 1981). From an economic perspective, the symbols may change to save wasteful expense - thus the Comanches had a tradition of killing a dead man's entire herd of horses which was later substituted by placing their shaven manes and tails in the graves (a status symbolisation that would be lost to the archaeologist) (Wallace and Hoebel 1952: 152-3). These factors may, on occasion, affect sub-adult symbolisation, particularly as the artefacts were not "possessed" by the children as such, and would be entirely symbolic of the social roles that they might have played, had they survived to adulthood. Use of different materials for adult and sub-adult socio-technic items was described by O'Shea (1984) at Leavenworth, where symbolic stone pipes made from rare catlinite were found with adults, while local limestone was used for a pipe found with an infant, which "may be indicative of the presumptive nature of the sub-adult's status" (216).

Another related point-of-interest is the fact that sub-adults often seem to display more variability in their mortuary treatment when compared to adults. At the Mohr site adults had relatively uniform body disposition, while sub-adults were much more variable; Gruber (1971) suggested there was a "cultural determinant" at work here. At the Fletcher site, the patterning of artefacts seemed to follow a more strict set of rules for adults than for the sub-adults (Mainfort 1985). The greater flexibility or variability in the treatment of subadults undoubtedly relates to importance of this grouping within the society. The processes involved here may be quite complex, and it is difficult to put an exact interpretation upon the "meaning" of variability of a mortuary dimension. For example, Goldstein (1976) correlated the increased variability in orientation at Schild, as compared to Moss, with an increase in the structure of organization, and other researchers (e.g. Saxe 1971) has attempted to link greater mortuary variability in females, compared to males, with a patrilocal system of post-marital residence. Therefore the degree of variability in the burial of sub-adults in contrast to adults could be interpreted in different ways, depending on the nature of the variability. A distinction must be made here between variability in the sense of different burial orientations and positioning of the body or artefacts, and variability in the sense of a wide range of artefact types or wealth differences. Naturally, increased variability, in the former sense, could reflect less care in the burial of sub-adults or, at least, a less stringent application of particular burial rules, which may in tum emphasise the lesser social personae of these sub-adults. Certainly, it would not be possible to argue for the greater variability of these attributes as resulting from a greater range of social personae amongst the subadults as compared to the adults - though the kind of "transitional" periods prior to adulthood noted above may produce changes in mortuary treatment that would not be applicable to adults, and which could explain some examples of greater variation. From the opposite perspective, it cannot be assumed that when sub-adults show the same degree of variation as adults that this necessarily implies a greater emphasis on the importance of children - interpretation is very much dependent on the attitudes of the society regarding the formality with which the burial rules are applied. Skomal (1983) compared adult-sub-adult variation in a different dimension, by comparing the range of wealth in both categories. For example, her hypothesis 3 suggested that in an egalitarian society there will be a greater ratio of adult to sub-adult wealth than in a less egalitarian society, since there will be a degree of wealth inheritance in the more hierarchical form of society. Her other hypotheses also involved comparing adults and sub-adults, and will be more fully dealt with below in the section dealing with wealth accumulation.

Other dimensions of the mortuary domain, other than artefactual, may distinguish between adults and sub-adults. The important category, location of grave, has already been mentioned. The dimensions of the grave may also incorporate an age symbolism; the Barcal cemetery analysed by O'Shea (1984) had adult graves significantly larger and deeper than sub-adult graves, while at Linwood adult graves were longer, though the other dimensions were not significantly different. The positioning of the body may also distinguish the two, either as an intentional, cultural difference, or perhaps in some cases relating to the nature of death; one suggestion for the high proportion of crouched child burials in urban cemeteries of Roman Britain has been that this was indicative of death during sleep (Philpott 1991: 71). Again, O'Shea's analysis gave examples of body positioning relating to age, with the minority extended positioning at Leavenworth, for example, being associated with sub-adults (1984: 195). The grave marker, if present, may also differentiate, even in relatively recent times; an examination of a nineteenth 21

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

2.2.4 Age symbolisms

youth. Saxe (1970) encountered problems of this nature in testing his hypotheses (see section 2.2.5).

for adults

Archaeologically, the recognition of age-grading symbolism is inevitably intertwined and complicated with differences in wealth and possible markers of ascribed status, and separation of the two may be difficult. Rowlett (1984: 209) comments upon the difficulties of associating a definite status with Corded Ware burials in some areas possessing battle axes, and suggested a possible relationship with age. The Russian Fatyanovo complex had four classes of warrior, whose burials correlated with the position of axes and the age of the deceased (O'Brien 1978: 64). More often-than-not, though, it seems that precise rules about the placement of artefacts with adults, relating to their age, do not exist; adult symbolism relates more to roles, achievements, ascribed status, or fashion variations, though there may be progression through different ranks in a role, or levels of a sodality, which may receive material symbolisation. At Galley Pond Mound, Binford (1972: 415-6) considered artefacts accompanying burial to be markers of esteem, correlating with age, "motivated by affect and etiquette as opposed to an established set of jural rules governing behaviour appropriate to individuals occupying deferred status positions". In some societies, where seniority is esteemed, there may even be exaggeration of the age of a person who has died; in Chinese communities it is considered "bad" to have died too young and this sentiment is so strong that the age of a deceased may be intentionally increased in the tombstone inscription and obituaries by five or more years (Hsu 1949).

The symbolic representation of age is not limited to the adult-sub-adult level. Artefacts and their placement (and other aspects of the mortuary domain) may symbolise different stages in adulthood, either formally (e.g. progression through age-ranks) or as markers of achievement, or perhaps simply representing different styles of dress (Collis 1984, for example, noted that in Iron Age burials in the Asberg area there is a tendency for mature women to have three brooches on the shoulder, while young women and children typically only have 1-2). Ethnographically there may be quite complicated systems of adult age-grading. The Ohaffia of Nigeria initiate children into age-sets at three-yearly intervals, which progressively move up as new children are initiated, until, in early adulthood, there is a senior and a junior age-set (Nsugbe 1974). Ten years later the senior age-set is converted into another association, the Akpan, above which is the Umuaka, recruitment into which is done by selection on the basis of personal qualities. The Umuaka, which consists mostly of men aged 46-55 years old, is particularly important in making decisions, even though a group of elders exists above it, who function mainly in an advisory role (ibid.). Thus this system of age-grading is complex, although fitting into a society which lacks hereditary leadership. "Social complexity", as defined in terms of the interaction between social units, can result as much from age principles, as from vertical status differences. Another interesting example of age-related differentiation is seen for the Pedi, where age-groups form regiments whose members are identified by a particular form of axe, which is distinct for their regiment; the regiment leader has an axeshaped copper-disc as identification (Monnig 1967: 119-120). It would be easy to identify these archaeologically as some form of clan-type differentiation, or even, given the different shape of the axes, some kind of functional difference, without recognising that the axes symbolise equivalent age groupings. The age-group of a deceased individual may be required to participate in the funeral to demonstrate their solidarity, and may in some societies be subject to similar taboos as the deceased's family, such as the avoidance of particular foods (e.g. Buxton 1973: 119). Age (and hence ability) may also affect progress through grades of a relatively fixed social identity, such as a shaman. Among the Selkups of Siberia, the equipment used by the shaman increased with increasing ability; the first bit of the shamanistic kit obtained was a drumstick, than a drum, followed by apron, clothes and boots, and finally, at the highest level, the shaman's cap and staff (Hadju 1968).

2.2.5 Symbolisms

relating

to marital status

One particular age-related category that seems to find material recognition in the burial practices of many different societies is marital status. To a large extent this is because the married state is frequently seen as the achievement of full adulthood (e.g. Deng 1972, Harris 1982), though there are also strong connections with ideas of fertility and reproduction. It was the effect of marriage symbolism that gave Saxe (1970) some of his more contradictory results, following the logic of his hypotheses. For the Bontoc Igorot, the unmarried social identity was associated with material attributes of young adulthood, causing anomalies for elderly, unmarried males, and confusing the results obtained for hypothesis 2. The Bontoc Igorot also caused confusion when testing hypothesis 3, since the greatest number of mortuary components was found with the unmarried personae, exceeding the number of components for people of greater significance (ibid.). Pader (following Bogatyrev 1971) noted similarly how in Slovakian communities the costumes worn by unmarried men tended to be more ornate and colourful than those of married men (1982).

The ethnographic literature also demonstrates that the social identity represented in burial, in terms of age, may not actually be the "true" one of the person being buried, further complicating archaeological attempts at identifying patterning. A chief of the Trobrianders, Uwelasi, when dying, was decorated with a particular form of necklace "that all young people wear ...Internally Uwelasi's body was dying, but outwardly he was adorned with the cultural symbols of youth and chiefly rank, expressive of his past seductiveness and fame" (Weiner 1988: 34). In this case there was a contradiction between the actual persona of the person, at the point of death, and the persona represented in burial; the burial persona, in some aspects, related to earlier stages of the life-cycle, particularly to the importance of symbolic

Ethnographically, the marital status of a person is often symbolised in burial, in a variety of different ways. Distinction may be made in the treatment of the body (e.g. young unmarried individuals are buried as opposed to cremated in some areas of India - Fuchs 1960, or the mutilation of the bodies of the unmarried among the Pedi Monnig 1967:130) or in the burial ceremonies (which may correspond to marriage ceremonies for those who are unmarried, as in Palestine for unmarried males - Spoer 1927: 137). The length of marriage, and the payment of bridewealth may determine whether a woman is buried by her own 22

The Age Dimension in Mortuary Studies

relatives or by her husband's, in societies which have patrilocal residence, such as the Lodagaa (Goody 1962) or the Bantu (Wagner 1949); in some instances a part of a married woman's body, such as the skull, may be disinterred and returned to her home village (e.g. Layard 1942: 565). Unmarried individuals, particularly females, may have mortuary rites that are not as elaborate as married individuals. In the Chinese community studied by Ahem (1973), unmarried females, though having an ancestral tablet made, could not have it placed in the ancestral hall.

male children was considered to be part of the criteria for the "paradigm" of a lineage member (Ahem 1973). Respect related to number of offspring is commonplace ethnographically (e.g. Moore 1982: 78), and can directly influence burial; among the Merina, a wife who has provided her husband with a particular number of children is then entitled to burial in his family tomb (Bloch 1971). There may even be a direct symbolism of the number of children a person has had within the grave - for example, by placing the equivalent number of dolls in the coffin (Mugoci 1919: 216) - and direct impact of the number of offspring on mortuary practice is again seen among the Merina, where, at the famadihana ceremony, each child must contribute a lamba mena shroud in which the deceased will be buried (Bloch 1971: 157).

Commonly, though, the distinction of marital status is through dress or artefactual inclusion. In Romania it was customary to dress the corpse of an unmarried girl in a bride's costume, and this mis-representation of the social persona was taken to such a degree that a ring would be placed on her finger (Murgoci 1919: 93). The burial of the unmarried, or those just recently married in wedding costume was also an ancient Greek custom (Garland 1985: 25), and the graves of the unmarried were sometimes marked by a loutrophoros vase, which was associated with the marriage ceremony (ibid., 72). The placing of jewelry with young female burials in Roman Britain is also interpreted by some as representing a symbolic dowry, indicative of the girl's unmarried status (Philpott 1991). Some forms of ornamentation may be included which are symbolic of marriage itself, because they were worn at the wedding ceremony e.g. a bracelet and necklace string thrown on the funeral pyre in some parts of India (Fuchs 1960: 331). Other symbolisms may be more obscure - for example, the placement of a hoe in the grave of an unmarried man is a custom of some of the Mashona of Zimbabwe (Gelfand 1956: 243), or the old Swedish custom of placing a mirror in the grave of an unmarried women, which apparently was symbolic of the different hair-styles adopted by married and unmarried women (Puckle 1926). Another custom which would cause some archaeological confusion, and potentially be mistaken for vertical status differentiation, was the Arab custom of placing in a woman's grave items corresponding to the number of times she had been married - for example, two dresses and two combs if she had been married twice (Spoer 1927: 128).

On the other hand, those who die without having had children can also receive specific forms of mortuary practice. Some additional ritual observations may be required, particularly for women; the Lodagaa place a childless woman on a partiallybroken pot during pre-burial ceremonies, to symbolise her failure (Goody 1962), and the Yoruba formally distinguished such a person by burial in a different location, that which was also used for children (Bascom 1969). This distinction is also seen, for example, in Kaoko-speak:ing societies of Guadalcanal for those who lack an heir (Hogbin 1964). In Amba religion a woman who has died without having children has a plantein stem placed on the chest when being buried, as a symbolic representation of a child (Winter 1967). Objects representing "surrogate" children may be broken on graves during ritual performances by the Hadza of northern Tanzania (Woodburn 1982). The age of death relative to a husband or wife will also influence the nature of mortuary rituals. In the Hindu religion, the ideal death for a woman is one in which she has died before her husband, and high-caste women who fulfill this criterion have the most elaborate funerary costumes (Parry 1994). Similarly, a study of Roman tombstone descriptions revealed a predominance of females in the age group 15-29, reflecting dual status as both wife and daughter, and also the fact that females who died relatively young were more likely to have predeceased their husbands, and therefore could be commemorated by the latter ( T .G. Parkin 1992: 104). The relative order of death of husband and wife may also determine who receives the most grave goods (e.g. Welch 1992).

These types of symbolisms are not always recognised archaeologically, and there is the inevitable danger of mistaking what may appear to be "esoteric" artefacts for vertical or horizontal symbolisms, when in actual fact they represent neither - though the married state may confer greater status in some societies because it represents "true" adulthood. A further consequence of marriage, related to age, which may confer greater status and be marked in burial ritual, is the number of children a person has had. This can be seen even in relatively complex societies like the Maloh of west Kalimantan, where the status of parents changes, depending on whether they have had a first child, grand-child or whatever (King 1985). The ornamentation worn by a woman may change with child-birth - married women of the Akikuyu exchange a stick worn in the ear lobe for hoops of beads following circumcision of their first child (Landtman 1938). In fact, the status of the children themselves (in terms of age, wealth etc.) is another factor that may determine a parent's standing within the community (Mair 1965: 57). Hodder (1982a: 163) noted that for the Nuba, older men may have more placed on their graves because of the greater number of dependencies they have, in addition to the status/wealth aspects of their social persona. The sex of children may be of importance - in Ch'inan, Taiwan, having

There are a few examples of marital status being recognised in the archaeological record, though the examples above indicate that "married" status may in actual fact be feigned for those who were single; Goodenough's observation that a social identity may be "pretended" (1965: 5) is clearly relevant here. In the Ula burial ground in Norway a particular form of dress and ornamentation for females was interpreted as symbolic of marriage or childbirth (Welinder 1988). Philpott (1991: 223) suggests that phallic ornaments found in Roman-British burials may also be symbolic of the unmarried, while some Iron Age burials of mature women, with what would normally be sub-adult goods, could also symbolise unmarried status (Collis 1984). The occurrence of male and female double graves is also often taken as representing a married couple, though this should not be automatically assumed (Klejn 1979); there is also the possibility that they may have been siblings or even, on occasion, simply buried together because they died at the 23

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

same time, to avoid the effort of digging an extra grave. The Lo Wiili dispose of suspected witches in old graves (Goody 1962: 153), which could in some instances give the impression of intentional burial together of husband and wife. Burial of relatives together may have ulterior motives Wood (1868) gives the example of the burial of two female relatives together so that the burial goods of the original burial could be reclaimed.

2.2.6

for elaborate treatment when the birth rate is low or the family circle is narrow (ibid., 29). and, similarly: As the hierarchical aspects increase, children will be accorded relatively more elaborate attention in proportion to the decline in the opportunity for replacement of the following generation (ibid., 29).

Conclusion The interesting thing about these statements is the implication that rich child burials need not always be present in a society with ascribed status; rather, Brown seems to consider elaboration to particularly reflect circumstances when there are fewer children, as such, increasing their social importance. Thus, the society's attitude to children, as determined by particular demographic circumstances, may be a crucial factor as to whether or not children will be buried with significant wealth.

In conclusion, age symbolism may take a variety of forms which encompass adult-sub-adult differentiation, age-gradings that may distinguish between adults, and distinctions relating to marital status, number of children and death relative to another individual, such as a husband. Archaeologically, there are a number of things to be aware (and wary) of: 1) Children may be buried with "esoteric" artefacts that cb not relate to ascribed status as such, but may reflect particular religious beliefs (such as the sacredness of twins) or the marking of particular age transitions. Emotional responses, such as sentiment or even fear, may also modify "normative" burial forms for children.

Historically and archaeologically, there are numerous examples of changing attitudes to the burial of children that need not necessarily reflect any change in social structure or, indeed, any hidden agenda of political motivation. Philpott (1991:101) noted that the increased burial of infants in Roman-British adult cemeteries in the fourth century may have reflected a growing belief that these infants were individuals. Also, he notes that in the most Romanised areas wealthy child burials may occur that reflect family, rather than achieved status, but this recognition only comes after the symbolically-important age of 18 months (ibid., 223). Children in India, along with other non-entities such as lepers, used to be symbolically distinguished by inhumation, as opposed to cremation, but this has now changed (Bayly 1980: 184). In ancient Athens there was also a change in attitude to children in the eighth century B.C.; before c. 760 B.C. most children were buried separately from adults, but after this there was a tendency for them to be buried in family plots; Sourvinou-Inwood (1980) saw this as reflecting less community involvement in death, and an attempt by the wealthier to provide "a symbolic focus for aristocratic selfdefinition" (34-35). It seems likely that the aristocrats were the first to include children in the adult cemeteries, a process later emulated by others of lower status (ibid.,35). In this instance, the initial inclusion of children seems to have been ideologically-motivated, though the later emulation would have counter-acted this ideological utility. Whittle (1988: 193) actually seems to be arguing contrary to Brown in some ways when he suggests that the change from collective to individual burial in the Neolithic, with reduced numbers of children buried, may suggest less attention being paid to reproduction and continuity of kin, and more focus being placed on the "leading figures" of the society.

2) Social identities may in some instances be marked in children by the use of organic equivalents of adult artefacts, which may subsequently not be preserved. 3) The importance of age, as symbolised in burial, may vary between communities and through time, and the particular symbols used to represent age may be modified similarly. 4) Marital status, number of children, and distinct age-grades may be symbolised in the mortuary domain and could be misinterpreted as representing some other form of status. The relative order of death of husband and wife will also influence the degree of status display in the burial. 5) Age-related social identities, such, as "married", may be "pretended" in mortuary treatment. In addition, the age of the deceased may be mis-represented, depending on the society's attitudes to age, and there is the possibility of symbols inappropriately being associated with a particular social persona as a consequence. To some extent this could be a factor behind child burials with adult artefacts, rather than necessarily reflecting ascribed status.

2.3 The significance of wealthy child burials The occurrence of rich child burials within a cemetery has traditionally been taken as representing a society in which status is ascribed (and hence non-egalitarian); a child is too young to have achieved social identities that would find recognition by wealthy grave goods, so these social identities must have been ascribed at birth. Brown (1981) has been more specific than most in specifying the connection between the two, while also managing to be slightly vague about the exact conditions in which rich child burials are to be expected:

Attitudes to the death of children may be culturally regulated, even at the state level. Some German states in the seventeenth century actually attempted to ban mourning for children completely (Whaley 1980: 91). More usually, though, there is a particular pattern of behaviour that is expected of the bereaved family regarding a deadchild. Thus, for the Mandari of Sudan, Buxton (1973) observed how personal grief of a mother regarding her deadchild had to be held in check, and a shorter grieving time was expected than, for example, an elderly relative, even though the feeling of loss for the former was usually much greater: "It may be psychologically correct that it is bad to give way to too

If the loss of children to a community or lineage can be argued to be critical to the future of a heritable claim, then children can be expected to be singled out

24

The Age Dimension in Mortuary Studies much grief over the newly born and healthier for the mother soon to become pregnant again" (ibid., 153). Various researchers have commented upon the fact that there may be a psychological detachment from young children in societies with high infant mortality (e.g. Garland 1985: 80). In fact, death of infants may provoke considerable fear in different societies because of the potential malevolence of the spirit for example, the Mashona of former Rhodesia believed that if a child was not buried in the correct position the mother may become sterile (Gelfand 1956: 240), and even in contemporary central European countries there may be a fear of contact with the infant's corpse (Ireland 1925). This is an alternative perspective to Brown's view of attitudes to child deaths, one which involves varying degrees of emotion. Houlbrooke (1989) has commented upon the problems historians face in trying to "measure" grief, and this is a problem which inevitably is even more difficult for the archaeologist to address, despite the possible effects it may have on the interpretation of burial remains.

" ...among adults, one views primarily individual achievement, whereas among sub-adults, one sees primarily the wealth of the families" (ibid., 252). Again, for the Bodrogkeresztur culture of the Carpathian basin, child burials with adult objects were seen as reflecting roles acquired from parents, even though leadership was considered to be achieved (Skomal 1980). These processes are readily observable in the ethnography, as noted by Heider (1979) for the Dani of New Guinea. Here leadership is in the shape of "Big Men", whose sons also tend to achieve the same position. Heider attributed this to the fact that the important men in society would have more wives, so that his sons would have more sisters and would be involved in more kinship exchanges. Wallace and Hoebel (1952) make a similar point with regard to the Comanches, where chiefly status was achieved by ability, but had a tendency to be hereditary purely because of the advantages of being the son of a chief, who would give training and example to his offspring, as well as giving a material advantage.

The above examples have demonstrated the variable and changeable attitudes to the death of children that may exist in different societies through time. Children may be considered important in political terms; for example, in the Baringo district of Kenya, larger clans had more political influence, so children were important in enlarging clan size (Hodder 1986). However, the sentimental aspect is one which is also potentially very powerful, if not closely regulated culturally, and the possibility of this producing "wealthy" child burials which do not reflect ascribed status has been commented upon by researchers such as Pader (1982). On the other hand, regulation may result in ascribed status not being reflected in burial. Pader (1980: 156) has similarly emphasised the importance of attitudes: " ...burial programme relates to 'attitudes' towards children as a category, not solely to distribution of power or wealth ...". Pader considered that the treatment of children may indicate the degree of their integration into the society (ibid.).

O'Shea's analysis also introduced further complications in describing (at Linwood, for example), "a hereditary position or privilege that was not directly tied to personal achievement or wealth" (1984: 123), and which was not restricted by age. This would indicate an "ascribed" position, but one not related to vertical status, as such, but perhaps more horizontally-orientated. O'Shea also noted that, at Clarks, for example, there were sub-adult burials with a reasonable degree of wealth, but lacking socio-technic items; he suggested that this reflected the fact that the socio-technic items marked the actual holding of positions of authority, rather than the potential to hold these (ibid., 150). Again, this cautions against making simple connections between possession of sociotechnic items and ascribed status. It can be difficult to determine what exactly constitutes a "sociotechnic" artefact, marking ascribed status, as opposed to, for example, an "ideotechnic" artefact (Binford 1972), relating perhaps to religious beliefs. Bloch-Smith (1992: 94,96) notes that in Judahite burials rattles found in child burials could be simply toys, or they could have a more significant meaning. Similarly, pig astragals have been interpreted both as childrens' toys, or as ritual artefacts (ibid., 208; Mallory 1990). In Greek burials, "rattle" type objects have also been found in child burials, but also in cult shrines, suggesting interpretation as either playthings or, rather differently, "instruments of cult" (Kurtz and Boardman 1971: 76-7).

A major problem when dealing with wealthy child burials is the distinction between achieved and ascribed status. While the two categories seem straightforward in theory, in practice the distinction seems to be much less clear, in the same way that the dichotomy between egalitarian and hierarchical societies is unclear. Brown (1995: 8) has commented on the problems of associating wealthy children with ascribed status, noting that the assumption depends " ... more on its internal logic than on any examination of the strengths and limitations of the record, either cross-culturally or historically .. for the present moment, the presence of grave wealth among the young cannot be tapped with any assurance". The literature is full of examples of societies where there may be an unofficial element of ascription resulting in prominent child burials. Thus Bintliff (1984: 97) talks, in the European early Neolithic, of child burials reflecting the status of "Big Men", and Susan Shennan (1982:30) notes that in early Bronze Age Slovakia rich male burials may reflect wealthy fathers who have achieved their status, and who confer advantage on to their children, even though status is not truly ascribed. O'Shea (1984) noted the same pattern; the Larson site he examined had a second rank level with rich sub-adults, though ranking was theoretically achieved, while the wealth patterning at Leavenworth, also with rich child burials, "signified the qualitative uniqueness of this particular child" (251). He suggested that in these examples there may be a fundamental distinction in that

The ritual status of some children has also been noted by Turner (1969), particularly with reference to twins. In many societies twins are seen as paradoxical, and the society may "confer on them a special status, often with sacred attributes" (ibid., 41). This can be seen even in complex societies such as the Ashanti, where they may "symbolize the sacredness and fertility of the chief", even though twins born in the royal family itself are killed (ibid., 42) The special, semisacred status of these children are, Turner notes, reflected in burial rite (ibid., 44). The connection of children with ideas of sacredness can be seen in other contexts, such as in Mexico, where young children may be buried dressed in costumes representing local saints (Lewis 1951: 416-7). These examples make it clear that there are more complicated social processes at work that would be suggested by the simple division of status into "achieved" or "ascribed". These need to be taken into account when determining the potential significance of wealthy child burials. Brown (1981) notes 25

Theoretical and Quantitative Approaches to the Study of Mortuary Practice himself the problems involved when he comments that elaborate child burials, apart from ascription, "seem to be related to other social and demographic variables" (ibid., 29). He also observes that rich child burials may reflect the "prestige" of particular lineages, as opposed to the inheritance of authority (ibid., 30), which also emphasises the different degrees of interpretation that may be placed on the phenomenon. In discussing these burials, it seems difficult to avoid the possibility that ideological processes may be at work. The examples above can be interpreted at two levels. Firstly, a "Big Man" may bury his children with wealth simply because he has the wealth in the first place and wants to demonstrate his loss. At a more manipulative level, though, the expenditure of much energy on the burial of the child could be seen as a step towards formally legitimising position of leadership; it may not yet be inherited, but this could be a method of moving towards that situation, making a public statement about the loss of a potential future leader. The crucial point, archaeologically, would therefore be distinguishing between a society which actually does have inherited status positions, and one where attempts are being made to shift the social structure in that direction, even though status is still technically achieved. Furthermore, the emphasis on children may relate to the stability of the status system. If this system is relatively stable, and ascribed status well-established, the "need" for wealthy child burials is reduced. It seems impossible, therefore, to make the simple connection between wealthy child burials and ascribed status, as such. Such a simple equation ignores the complexity of the social processes at work.

into discrete levels representing "status levels" (e.g. Mainfort 1985, Arnold 1980). This tends to ignore the more dynamic aspects of wealth accumulation, and particularly how it interrelates with age. The distribution of wealth with age should, after all, be of direct interest, particularly as it may be instructive as to the structural organisation of the society. Skomal (1983) is one of the few researchers who has looked at wealth in detail. She formulated a number of hypotheses relating to the difference in wealth distribution between egalitarian and ranked societies, which were then applied to the Carpathian Basin in the fifth and fourth millennia B.C. A number of these hypotheses relate to differences in wealth with age: 1) Hypothesis 3 suggests that in an egalitarian society there will be a greater ratio of adult to sub-adult wealth than in a less egalitarian society, and the wealth in the sub-adults will be more uniform than for adults in the egalitarian society, because of less variation in age and social involvement (ibid., 182-4). 2) Hypothesis 4 suggests that adult males will have greater wealth proportionately than sub-adults in a nonegalitarian society, but the two groups will have similar variations, because of inherited roles (ibid., 184-6). 3) Hypothesis 5 suggests that burial wealth will increase with the age of a male adult but will be uncorrelated for a female, in a non-egalitarian society. This is because in the Proto-Indo-European society, wealth/status is achieved by males through ability, in addition to being transferred patrilineally, so should favour older males (ibid., 186-8).

In conclusion, there are various different interpretations that can be placed upon the archaeological occurrence of rich child burials. They may reflect the presence of an ascribed status system or they may represent a situation where achieved status occurs, but the wealthier members of society may bury their children with wealth proportionate to their means, possibly in an attempt to make status more institutionalised. These two situations are cross-cut by the effects of varying attitudes to children - sentiment, fear, the importance of reproduction to the society and the cultural regulation of how a child should be buried. Clearly, children may be considered more important in some societies than in others, as a general category. There may be a link here to attitudes to unmarried individuals or women who have not had children who, in some societies, are actively despised, usually because they have made no contribution to the continuance of their lineage. Amongst the Marakwet of Kenya an unmarried man may not be greeted, and is not considered to have achieved full adulthood (Moore 1982: 78). Unmarried individuals may even be the subject of post-mortem mutilation, since they can be considered unlucky (Monnig 1967: 130). Significantly, the absence of wealthy child burials need not represent a society lacking ascribed status, because of some of these factors, and also because the ascribed status of the child may have been more appropriately symbolised by other means, such as elaborate ritual or the location of burial. Also, archaeologists need to be aware of the fact that changes in the methods by which children are treated in the mortuary domain may reflect changes in attitude that are independent of any changes in the social structure.

The results of the hypotheses were somewhat contrary to what was expected. For hypotheses 3, all three cultures had a greater ratio of adult to sub-adult wealth, but the uniformity of the wealth distribution for sub-adults increases with time, suggesting an increase in egalitarianism. Hypothesis 4 indicated that the early and middle Copper age assemblages were egalitarian in nature but the earlier Lengyel graves seemed to be organised within a non-egalitarian context. For the Middle Copper Age sites there was a greater ratio of adult to sub-adult wealth which, Skomal suggested, indicated that age was a more important social factor. Hypothesis 5 also suggested that the earlier social structure was less egalitarian. The wealth of males in each culture was correlated with age at death, but in different ways. For the Lengyel sites, the correlation was negative so that the older he was when he died, the less wealth was buried with him; thus the younger males had greater access to wealth, which Skomal took as representing a non-egalitarian social structure. For the other cultures, wealth showed a positive correlation with age at death, demonstrating that personal ability was a factor in wealth accumulation, beyond any ascription of status. Female wealth in all three cultures was unrelated to age. O'Brien (1978) in his study discovered some clear relationships between age and wealth, though the main distinction was generally between adult and sub-adult. At Vikletice, the mean wealth scores for the different age categories were: infans 4.5, juvenilis 9.26, adultus 9.72, maturus 9.72 and senilis 9.33 (ibid., 194). Wealth was most considered to increase notably beyond the achievement of adulthood. The cemetery at Schafstadt was unusual in that the wealthier graves were mostly sub-adults, basically because

2.4 The effect of age on wealth accumulation Archaeological focus on wealth in burial has usually been in terms of trying to divide up the distribution of the wealth 26

The Age Dimension in Mortuary Studies stone mortuary structure in the cemetery were sub-adult associated (ibid., 216-7). However, it is notable that only 29 graves were analysed, and these were considered to be part of a larger cemetery. O'Brien also noted at Polepy there was an unusually high proportion of sub-adults with grave goods, compared to adults, possibly as a consequence of low status children being excluded (ibid., 260). Chronology was also a possible confusing factor; in the early Unetice burials of the Kolin district, the presence of some sub-adults with greater wealth than adults could, it is suggested, have been due to them being dated to the Classical Unetice period (ibid., 266).

egalitarian mortuary domain may involve the selection of only some sub-adults, typically those of higher status, for burial with adults. Subsequently this may result in a uniformity among sub-adults that is more apparent than real. As above, attitudes to sub-adults are crucial and uniformity of wealth (or lack of it) may relate to the emphasis placed on sub-adults. Skomal's hypothesis 4 suggests that in a nonegalitarian society male adults and sub-adults will have similar variations in wealth, because of the inheritance of social roles by the sub-adults. This view ignores the fact that wealth relates to more than just particular social roles; it also is determined, even in a non-egalitarian context, by factors of achievement/ability, kinship circle and horizontal groupings such as societies (which often are not entered until adulthood is attained). These factors make it difficult to make a direct comparison between wealth variation for adults/sub-adults in different social contexts.

O'Shea and Zvelebil (1984), in their examination of the Oleneostrovski Mesolithic cemetery also briefly dealt with the relationship between age and wealth. Those individuals who were in their prime had the most wealth, suggesting that physical ability was an important factor in obtaining wealth. Female wealth was less related to age, which O'Shea and Zvelebil suggested was because they were not directly involved in the "wealth-generating system", but obtained their wealth through links with the males (ibid., 18-19). The suggestion that females obtain their wealth through marriage is a common one. Shennan (1975) suggested two possibilities for the distribution of wealth among the females at Branc: one was that wealth was achieved on marriage, and the presence of wealthy child burials could be explained by arranged marriages. The other explanation was that these rich infants represented a system of ascribed wealth for females. It was unclear which of these two explanations was correct, though there was evidence that wealthy females received more favourable treatment from birth (ibid., 285-6). Shennan also noted the problems in assuming that greater numbers of adults with wealthy grave goods indicate achieved status, since these adults could have had their wealth from their younger age (ibid., 284). The greater difference between adultsub-adult wealth for females, compared to males, in Slovakian sites of the Early Bronze Age as a whole was also used by S.E. Shennan as an indication that females achieved status, possibly through marriage (1982: 30). Arnold (1980: 132) also suggested that for Anglo-Saxon burials the pattern of wealth indicated that female wealth was a reflection of husband's wealth.

There are also a number of practical problems with Skomal's study of wealth variations. For a start, the distinction between adult and sub-adult is not necessarily a clear one. Biologically, a universal distinction may be made, but culturally the division between the two categories is variable, and a further distinction could be made between "adult" status in the sense of maturity (e.g. married), and "adult" status in terms of ability to participate in work. Nor should it be assumed that the boundary between the two categories remains constant in a particular society though time, particularly if there is an evolving social structure associated with economical changes. In British society children a couple of centuries ago would begin working at a much earlier age than they do today. Thus the uncertainty of where the adultsub-adult division lies, and possible shifts in the division with time/change in social structure could confuse attempts at comparison of wealth ratios. Further problems come with Skomal's wealth indices. Only one wealth index was used, though the analysis was applied to three separate periods (Chalcolithic to Middle Copper Age). Different objects may have different status values with time. For example, Pig/boar mandibles are considered by Skomal to have a ritual significance, and are subsequently given a high score. However, although only 1.3% of males in the Middle Copper Age have them, in the Early Copper age 64% of males have these mandibles in their graves, which would suggest that they were of lesser significance or, indeed, never were indicative of high status. O'Brien (1978) avoided such problems by having a separate index for each cemetery, though this may produce additional problems of comparison. No distinction was made between the sexes in the wealth scores, despite the fact that some burials may be marked out by having goods usually associated with the other sex. At Westgarth Gardens Anglo-Saxon cemetery, for example, Pader (1982) noted that there was one group of burials which had only one female. Her artefact count was not distinctive, but she was unusual in having a knife. Although this was a common object in the cemetery as a whole, only three females in the entire cemetery had one, which obviously seemed to be marking them out as unusual in some way. These kind of variations can be difficult to deal with when using a static wealth index.

A number of points can be raised about these studies, particularly about Skomal's hypotheses. These relate to attempts to make general hypotheses that will apply to all egalitarian and non-egalitarian societies. Hypothesis 3 suggested that an egalitarian society will have a greater ratio of adult to sub-adult wealth than in a less egalitarian society. Several situations may exist which make this untrue, or confuse the picture. Sub-adults in a non-egalitarian society may have their status recognised by various non-wealth means, such as by location, grave structure or bodily treatment, or by inclusion of certain artefacts that are "esoteric" but which do not necessarily contribute greatly to the total wealth of the burial. There is also the possibility, mentioned above, of sub-adults receiving wealthy burials in what is technically an egalitarian society, as part of a strategy to try enforce more permanent status divisions. Further, attitudes towards sub-adults may change independently of changes in social structure - as in Brown's observations that children may receive more lavish burial when the population numbers are low (1981). The second part of hypothesis 3 suggests that the wealth distribution will be more uniform for sub-adults (in comparison to adults) in a more egalitarian society. Again, similar objections can be raised. A non-

A further uncertainty exists, when attempting to relate the pattern of wealth to age, in that it is not always clear what the nature of this grave wealth is. It may have been the deceased's personal wealth, it may have been contributed by family, lineage/clan, members of societies etc, it may have 27

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

been more ctirectly symbolic of the deceased's social persona (i.e. careful selection of particular artefacts), or some combination of these factors. The presence of personal wealth can sometimes be clearly recognised and linked with the age of the person. Thus, grave 79 at the Anglo-Saxon cemetery of Holywell Row had a large number of worn, damaged brooches which Pader (1980) suggested could reflect the elderly status of the woman buried. Older people may also have a greater spread of kinship links which may also find recognition in burial goods, if this factor is relevant in a society - though the particularly old may have fewer relatives surv1vmg and may subsequently be at a material disadvantage, very much depending on the society's attitudes to its most elderly members. Craftspeople may also have a greater degree of apparent wealth in burial, simply because of the material products of their occupation (e.g. Rowlett 1984).

status system, as in the sacred qualities of twins in some African societies. Even attempts to identify ascribed status through association with socio-technic items, as opposed to overall wealth, can be complicated by ritualistic items found in child graves, reflecting religious beliefs relating to children as a general category. Particularly important is the society's attitude, or attitudes, to children. In times of high mortality (or low fertility, in some exceptional circumstances, such as with the Marind-anim of New Guinea - Edgerton 1992: 182), or competition between groups for dominance (which can depend on numerical superiority), children may take on particular importance, given their symbolic association with the continuance of the social group. In periods of low mortality, or when the social system is relatively stable, or when food resources are limited (too many children can be a drain on these resources), children may become de-emphasised in political terms (though, correspondingly, there may be an increased element of sentiment). There can be no automatic assumption that the ascribed status of a child will find reflection in burial. In particular, there is a need to question the general assumption that children who have an ascribed status will have this status potential automatically reflected in burial. There seems to be little empirical ethnographic evidence to justify this assumption.

In conclusion, relating wealth to age is difficult in archaeological contexts because of the different methods by which the grave goods may have been placed in the burial, and attempts at determining the social structure through the relative proportions of wealth for adults/sub-adults seems to be particularly dangerous, given the different attitudes that may exist relating to sub-adults and their treatment in the mortuary domain, in both time and space. The age aspect of mortuary studies as a whole involves potentially a wide range of symbolisms and cultural attitudes that must be more explicitly addressed, where possible, when analysing burial data and making assumptions about the representation of "horizontal" and "vertical" dimensions. There are many processes that may be at work individually or collectively in a particular society - economical, sentimental, religious, ideological and so on - and these may all have an impact on the apparent connections between age and wealth as seen in burial.

2.5

2) Other forms of age-symbolism are potentially complex, and can introduce considerable variations into the mortuary domain. A society that stresses the importance of age as a social dimension can therefore produce complex burial patterns, independently of the degree of vertical patterning. The complexity of age patterning may also result from different systems of age-grading used by horizontal groupings within a single cemetery, which would also clearly contribute to the apparent complexity of the society. This stresses the importance of investigating horizontal patterning in mortuary data, despite Tainter and Cordy's (1977) claims to the contrary. Even societies which are geographically, spatially and organisationally close can place quite different emphasis on the age dimension, though attempts at explaining such differences seem rare.

Conclusion

This chapter has examined some of the complexities that are involved in age symbolism in mortuary practice. "Age" is not, as such, a single category, in the sense of Binford's (1971) attempts to link it (as an undifferentiated identity) with specific elements in mortuary practice. Rather, age encompasses a number of different sub-categories, it relates to other social identities such as marital status, and, particularly, it may encompass a variety of different attitudes. Attempts, such as that of Saxe (1970), to relate the complexity of the mortuary domain to the complexity of society ignores the fact that a supposedly "egalitarian" social characteristic such as age can in itself produce considerable complexities within burial. A number of points can be made:

3) The importance of the age dimension as symbolised in burial can fluctuate through time, which may relate to changes in the society, or changes in attitude. Changing attitudes to children have been noted above, but age symbolism as a whole may also vary. The symbolism of social identities such as marital status, and respect relating to procreative ability will obviously tie in with attitudes to children and reproduction. Other changes in age symbolism may reflect changes in economy or ritual control of burial (e.g. O'Shea 1984), or perhaps there may be different ideas of what social dimensions may properly be expressed in mortuary practice. Ideological interpretations of burial have tended to focus on "negotiations" between different status levels, or between male and female (e.g. Hodder 1990), yet age is another dimension that may be subject to such manipulations. Examples of older individuals associating themselves with the vitality of youth have

1) The treatment of children can involve a number of

processes. "Wealthy" children may symbolise the existence of ascribed status, but alternative explanations can also be given. Expenditure of wealth on children may represent an attempt to legitimise social ranking, even if status within the society is nominally achieved. The distinction between ascribed and achieved status is itself a simplification, since wealth and status achieved by a parent will inevitably influence the status of a child. Differential treatment of children will also result from other factors such as sentiment and fear, both of which, in some instances, can result in greater elaboration of child burial. Some children may receive a special status independent of that ascribed in the normal 28

The Age Dimension in Mortuary Studies been noted above, and the potential exists for younger individuals to associate themselves with mortuary elements normally connected with more mature people, if this has a status association. In conclusion, the age dimension is a complex one that has relevance in all societies, whatever their social structure. It relates to some of the most fundamental aspects of society its reproduction, its economy, the definition of social ranks, and changes relevant to all these through time. Recognising these complexities in the archaeological record is a challenge, but it is only really through the study of mortuary data that such social aspects (and potential ideological influences) can be directly addressed. The next chapter will similarly address the complexities of gender symbolism in burial.

29

Chapter 3 The Gender Dimension in Mortuary Studies 3.1 Introduction

females is frequently not arbitrary, but can have symbolic meaning.

As with age, the gender dimension is an important aspect of mortuary analysis in all types of societies. Gender, as opposed to the biological characteristic of sex, is "a social and cultural construct, comprising the roles given to, and identities perceived by, men and women in a particular society" (Gibbs 1987: 80). This definition makes it clear that the cultural perspective is crucial in determining ideas of gender and how it may be symbolised. The universal importance of gender is emphasised by Binford's ethnographic survey (1971: 231), with sex being either the first or second most common dimensional distinction in all four types of society, with social position being the only dimension that may in some cases occur more frequently. Gender also tends to be one of the most clearly discernable dimensions of burial practice, often signalled by the positioning/orientation of the body and/or by distinct artefacts. However, gender differences may not always be so clearly signalled, and have in the past been mistaken for vertical groupings (Graslund 1980: 77). This stresses the need to determine the different ways that gender distinctions can affect burial practice. The gender aspect is of theoretical interest in mortuary studies from a number of perspectives:

3.2.2

distinctions

The use of space for gender ideology has been commented upon by researchers such as Chapman (1994), and the mortuary domain provides various types of spatial distinctions. Spatial differentiation is an important means of symbolising distinctions between males and females in burial, and in some circumstances reflects spatial segregation of males and females in the living context (in houses, for example, or reflecting different economic activities) or in conceptual terms. The forms of spatial distinctions found in burial include: 1) The exclusion of certain categories completely from formal cemetery burial, or the use of different disposal methods (e.g. cremation versus inhumation) for males and females, which may give the impression of one category being excluded from formal burial. 2) Placement in different cemeteries; this may apply in general, or just to specific categories, as in the separate burial of males who have died in battle (e.g. Roth 1896: 142).

1) Identifying male/female symbolism in burial 2) Determining relative status of males and females 3) Post-marital residence rules

3.2 Male/female

Spatial

3) The separation of males and females into different areas within a cemetery - for example, King (1978) noted a concentration of females in the east lobe at Buchanan Reservoir, which was interpreted as possibly representing non-local affines.

symbolism in burial

3.2.1 Introduction

4) The use of particular locations within groupings, such as clusters or rows, inside the cemetery - for example, the placing of a husband to the left of his wife in rural Chinese cemeteries (Hsu 1949). Even in double burials of a male and female there may be particular placing of the male and female relative to each other so, for example, at Wadi Halfa, Saxe (1971: 47) suggests that the male, being of higher status, may be placed on the outside of the pair, a process possibly also extending to older females versus younger females.

The determination of male and female symbolism in the mortuary domain is important not simply from the perspective of identifying the sex of particular burials, but also because it may shed light upon the roles played by the sexes, the society's attitudes to gender (and hence larger scale considerations such as reproduction), and the possible evidence of ideological struggles between males and females expressed in this context. As Levy (1989:157-8) commented, burial practice relates to the control of both human and environmental resources, and the gender roles in society form one among several aspects of the former. With regard to ideology, Hodder (1986) noted that in some particular contexts (such as megalithic tombs), the processes observed archaeologically may relate as much to gender "negotiations" as to ideology propagated by an elite group. This emphasies that various ideologies may be at work in the mortuary domain, potentially involving gender as much as those concerned with "elite" groupings.

Under-representation of females, perhaps indicating deliberate exclusion, is commonly recognised archaeologically - thus 75% of graves in the Single Grave culture of Denmark, for example, are identified as male (though some of these are identified via grave goods) (Jensen 1982). Some male/female imbalances may not necessarily reflect cultural exclusion as such; Scott (1992: 85-6) suggests that the imbalance in favour of males in Roman British cemeteries may result from female infanticide, for example (though this in itself implies a lower social standing for females) or a deficit of males may be a consequence of warfare (e.g. Ember 1974), emigration (Molleson 1992) or polygamy, as Saxe (1971) suggested for Wadi Halfa. The potentially varied processes that are at work warn against jumping to conclusions about the social significance of male/female imbalances in cemetery populations.

According to Binford's ethnographic survey (1971) gender was only symbolised by orientation of the grave, and by the form of grave furniture. While these do seem to be the most common methods of symbolising gender ethnographically and archaeologically, clearly other distinctions may be employed, and the full range of these will be considered here, together with their potential significance; why a particular distinction is used within a society to distinguish males and

30

The Gender Dimension in Mortuary Studies The segregation of the sexes is a process which can also be seen in some megalithic tombs, such as West Kennet (Bradley 1984), and the patterning in some of these tombs has been interpreted in terms of gender ideologies. Given the connections made ethnographically between tomb form, wombs, and the process of rebirth (e.g. Huntington and Metcalf 1979; Graslund 1994), the importance of gender in this context is unsurprising. Hodder (1990), in discussing megalithic tombs in southern Scandanavia, notes the hidden messages relating to gender that the distribution of artefacts in the tombs can signify. The spatial separation of tools and pottery underlines "a symbolic opposition between the male control of ancestors and esoteric knowledge (in the domus) and female control of the entrance to that knowledge (in the Joris)" (ibid., 198). A similar opposition can be observed in megaliths in northern France, with female associations with entrances marked by carvings of breasts (ibid., 233). Insights into gender-related processes involved in megalithic burial can be seen ethnographically amongst the Tanala of Madagascar (Linton 1933). Among the northern clans of this society the tombs were divided into three compartments, to separate men, women and children. However, in certain regions tombs could be built by individuals, who could specify the nature of the inclusions within the tomb whether for men only, or with men and women kept separately (ibid.). This example illustrates the potentially individual factors that may produce the patterning of remains within and between tombs, though there may also be more specific "rules" at work. Thus, among the Merina, also of Madagascar, a woman can be buried in her husband's tomb after she has given birth to three of his children (Bloch 1971: 115). A similar example would be the underground tombs of the Ifugaos in the Philippines; Barton (1946: 197) notes that in some regions male and female may be buried together, while in other areas this is not the case because kinship avoidance makes it seem "indecent".

1992: 37). However, the symbolism can be interpreted at a number of levels other than this; the cist form can be interpreted as representing a hearth, while the key-hole shaped slabs can be seen as symbolising the other important element of a house, the entrance (ibid., 37). Monuments erected in memory of the deceased among the Menabe Tanala of Madagascar have similar symbolisms - for men a standing stone is erected and for women a flat stone supported by three uprights, which is symbolic of a fireplace and the female's role in society (Linton 1933:183). Apart from the physical structure of the grave, its orientation also frequently is used to symbolise gender differences, as can be seen archaeologically in many different cultures and periods. The orientations chosen can have specific meanings, which may relate to the side on which the body is placed in the grave. There are various references in the ethnographic literature to males and females being buried on different sides, relating to positioning during sexual intercourse, though a right-left differentiation is, of course, a well-known phenomenon in many societies for distinguishing males and females; the degree to which such rules are followed in some contemporary cultures has been related to ethnic intermingling (e.g. D. Parkin 1992). The actual choice of orientation in itself can be given meaning in terms of the roles played by males and females in the living society. The Lodagaa bury a man facing east so that sun rise will wake him in preparation for hunting, while the burial of a woman facing west is explained in terms of the setting sun marking the need to prepare food for her husband's return (Goody 1962:144). Kraig (1978) has interpreted the relative orientation of male and female in later Kurgan burials in even more abstract terms. Females face south, with the head to the east, and this is associated with the rising and passage of the sun. Kraig interprets this as having fertility associations, while burial on the left side links up with the idea of death being associated with the left hand (ibid., 166). Males, on the other hand, are buried on their right sides with the head to the west; the association with the right is seen as a representation of skill and ability (ibid., 166-7).

3.2.3 Structure and orientation of the grave The structural details of the grave can contain gender distinctions, which may relate both to status and symbolic differences. Males may be buried in deeper graves than females, as in the Ftizesabony early Bronze Age phase on the Hungarian plain (Coles and Harding 1979: 80). Completely different burial structures may be used for males and females, or, at least, there may be a bias towards one or the other. In Judahite burial, for example, Bloch-Smith notes that pit graves have females occurring three times as frequently as males, while in bench graves the male to female ratio is 3:2 (1992: 69). Grave markers provide another means of distinguishing gender, and can make ideological statements about male-female relationships. Prior (1989: 118-20) has observed how gravestones of the nineteenth century may indicate the patriarchical nature of society, since the grave inscriptions frequently only mention males by name, such as "The family burial ground of ... ". The form of gravestones distinguishing gender may not be arbitrary but relates to symbolic sexual differences. In Java, a pointed grave marker symbolises a male, while a flat or rounded one is symbolic of females (Geertz 1960: 70); an almost identical distinction is found amongst the Japanese Ainu (Munro 1962: 135). Memorial stones not directly linked with burial may also contain symbolic gender distinctions. Scandanavian picture stones in Jutland can occur in slab and cist forms, which have a phallic or womb symbolism respectively (Andren

3.2.4

Ceremonial

distinctions

The actual organisation of the funeral itself may define gender roles within the living population, in addition to identifying the gender of the deceased person. Male and female mourners may have clearly defined positions relative to the corpse in pre-interment activities, emphasising their status differences and their separate functions in the burial ritual. Among the Ainu, for example, the corpse is orientated very specifically in the mourning house, and women are not allowed to stand at its head, but must mourn at the feet (Munro 1962: 129). There may be a distinct separation of male and female mourners in the early stages of the mortuary ritual, as with the Bara of Madagascar, who have separate male and female houses in the first few days following death, with the corpse being kept in the female house (Huntington and Metcalf 1979: 102-3). The activities undertaken by males and females at funerals are also often distinct, with men usually carrying the coffin and women prominent in visible mourning; Prior (1989) quotes one informant summarising the distinction as being "Men make the arrangements, and women make the tea" (ibid., 172), and again this reinforces the status differences that exist in the society. However, female involvement in mourning can have much greater significance 31

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

than this statement would imply, particularly given inherent female associations with fertility and reproduction, issues brought to the fore by the loss of a community member (e.g. Monnig 1967).

specific gender aspects in the process - so, for example, in Hindu funerary practices in India, cremation is viewed as removing the female part of the body, the flesh, leaving a pure male component, the bones. The bones are then placed in the Ganges, which is itself seen as "female" (Parry 1994: 188). The disposition of the body in the grave is also a method that can be use to distinguish gender, such as the tendency in Anglo-Saxon cemeteries such as Holywell Row to have females flexed and males extended (Pader 1982). Some of these differential treatments may relate to particular ritual beliefs related to gender, while others may represent status distinctions distinguishing male and female; for example, flexing of the body is often seen as being a lower energy expenditure alternative to extended burial, since a smaller grave can be dug.

Ceremonial distinctions in the funerary procedures, marking the sex of the deceased, are also common and could be observed even in Victorian times, when black or white "weepers" worn on the arm or hat denoted the sex of the deceased (Morley 1971: 29). For the Lodagaa the length of the initial burial service is related to sex (Goody 1962: 4344), and in many societies females tend to have shorter rituals, though there are exceptions; the Dinka of Sudan, for example, have a ritual separation ceremony called cuol, which is three days for men and four days for women (Deng 1972); the unusualness of this is commented upon by Deng. This may relate to particular beliefs, such as the fact that in Africa there commonly is an association between males and odd numbers, and females and even numbers (Goody 1962: 61). Male and female are also distinguished among the Lodagaa by ritual re-enactment of certain gender roles prior to burial - the men by a mock hunt and some token farming, and the women by ritual smashing of pottery by female associates (ibid., 129-30). A ceremony associated with funerals, called taskeet, was a Palestinian custom, in which a bag was passed around and men placed money in it, and women gave bracelets or rings (Spoer 1927: 139), further emphasising gender differences among the living; the taskeet itself was symbolic of the esteem with which the deceased person was held.

3.2.6 Artefactual

Archaeologically, it is the artefactual symbolism in burial that usually is of particular interest with regard to male/female divisions and relations in the society. Clearly, the artefacts included in burial with males as opposed to females represent both role distinctions, relating to separate use of the artefacts by male and females, and also symbolic distinctions, in that particular artefacts may be taken as representing "maleness" and "femaleness", possibly independently of their actual functional use by the deceased. The degree of symbolism will be very much dependent on the attitudes of the society concerned, and the relative activities (and their importance) of males and females within the society.

Ceremonial aspects of funerary practice, therefore, provide a prominent means of symbolising gender differences, both in the living community and with respect to those who are being buried. In fact, as Huntington and Metcalf (1979: 93) note, "it is common for life values of sexuality and fertility to dominate the symbolism of funerals", and the ceremonies provide perhaps the most visible means of drawing attention to these gender-oriented values. The whole process of death itself may, in some societies, be viewed in gender terms. The Bara of Madagascar consider life in terms of a journey from the mother's womb to the father's tomb, with death a consequence of too much of the male quality, "order"; this imbalance is corrected through the symbolising of "vitality", the corresponding female quality, in the funeral ceremonies (ibid., 99-101). Clearly, even if male/female distinctions are not apparent in the archaeologically-visible aspects of burial, they may have been clearly signalled in the invisible ceremonial activities surrounding death.

3.2.5 Treatment/arrangement

distinctions

Ethnographically, there are clear examples of grave goods being selected which are deemed to represent "maleness" or "femaleness", rather than being simply possessions of the deceased person. Some goods representing facets of male/female persona may be ritually destroyed. The Mandari of Sudan destroy, amongst other things, a woman's cooking pot and a ritual pot used by her husband; the wife's pot is symbolic of her role as wife and mother, while the man's pot is symbolic of his marriage to his wife (Buxton 1973: 121). These items are destroyed because they are personal items which cannot be inherited (ibid.). Certain possessions hung up within the grave hut placed over a burial are selected particularly for their gender distinctions - weapons for masculinity and decorative items for femininity (ibid., 122). These examples involve artefactual distinctions that would not actually be marked in the grave itself, though examples of this also occur. The Lugbara of Uganda include objects with the burial that symbolise gender roles - thus a man has a quiver, drinking gourd and stool, while a woman has beads, firestones and a grinding stone (Middleton 1982). In rural Japan, a pipe or razor may be placed in the coffin to symbolise a man, and a pair of scissors for a woman (Norbeck 1954:190). The Sea Dyaks placed items on the grave to specifically indicate the sex of the deceased, in addition to goods placed in the grave - for a man, items of weaponry and antlers or tusks symbolising hunting achievements, and for a woman items of female clothing, or objects such as spindles or water gourds to indicate female activities (Roth 1896: 140).

of the body

The treatment and/or arrangement of the body may also be used to distinguish gender, sometimes in quite obscure ways. Of the burials recovered in Iron Age hillforts, for example, multiple partial burials have a tendency to be male, while those recovered singly tend to be female (Woodward 1992: 82). At Galley Pond Mound, the removal of teeth seemed to be restricted to females, while cutting and polishing of skulls was found only with male burials (Binford 1972). Whether the body is cremated or inhumed may relate to gender; in the Hallstatt cemetery the choice between inhumation and cremation may have been been of greater importance for males than for females (Hodson 1977). Even if there is a singular treatment for both males and females, there may be

Clearly, there is a concern in the burial of many societies to particularly mark out gender distinctions through artefactual inclusion, and this is also the case archaeologically. In some 32

The Gender Dimension in Mortuary Studies cases apparently functional items have been interpreted in more symbolic terms; for example, smith's equipment has been seen as representing secret male knowledge, with spindle whorls symbolising female divination (Andren 1992: 49), while Welinder (1988) suggests that in the Ula cemetery female fertility was symbolised by the sickle. Hedeager (1992:155) envisaged a link between rich male burials with military equipment and the presence of a patrilinear system. This connection may be dubious, since weaponry in burial may be purely symbolic; with the spread of Bell Beakers in Europe, for example, there is an emphasis on war in male burial goods which is not reflected in settlement evidence (Champion et al 1984: 177). In some instances the same object may be differentiated by its location; the Mesolithic burials analysed by Alekshin differentiated male and female bracelets by placing the former at the wrist and the latter higher on the arm (Alekshin 1987), and in contemporary society there is the functionless male-female distinction in the side on which clothing buttons are placed. A different function for the same object may be indicated by different locations, as Bognar-Kutzian (1963: 187) suggested for pig mandibles with females as opposed to males at TiszapolgarBasatanya. Even the way in which an artefact is actually placed in the grave may mark a gender distinction, reinforcing the message provided by the object itself. Thus, in Early Bronze Age Danish graves a contrast was drawn between ornaments and a sword, in that ornamentation was placed with the body, while the sword would be placed on top of a hide covering the body (Gibbs 1987: 450).

Burial with artefacts typical of the opposite gender may also be indicative of high status, as O'Shea and Zvelebil (1984: 16) suggested for some females with what were typically male artefacts at Oleneostrovski Mesolithic cemetery. At Nitra some females are buried with axes, a "male" object (Bintliff 1984a). The same process is also seen ethnographically and historically. The Lodagaa may dress a female corpse in male clothing, for the simple reason that male costumes are looked upon as being of a higher status (Goody 1962), while in Benin a Queen Mother, because of her high status, is buried as if she were a man (Barley 1995: 87). Female English monarchs in the Middle Ages, such as Mary 1, had "masculine" objects on display at their funerals, such as gauntlets and a battle-axe (Gittings 1984: 222), clearly symbolic of leadership and status, rather than femininity. Therefore, in some cases the very fact that a particular artefact or burial practice is seen as typical of someone of the opposite gender may gives added status potential to this object and be reason enough for inclusion in burial, particularly the inclusion of male artefacts in a female burial. The inclusion of grave goods "typical" of a different gender may also be explained by other factors. Ethnographically there exist certain categories of male - the Sarombavy of the Tanala (Linton 1933) or the Bedrache of the Crow Indians (Bock 1969), for example - who may dress in female clothes and who are involved in typically "female" industries. Certain ritual activities may also involve an inversion of typical male and female roles in a society, to the extent of women wearing male clothing (Turner 1969: 172-3). Examples this extreme are not necessary to explain the inter-change of what, even within a particular cultural context, may be "male" or "female" artefacts; ethnographically, there are accounts of males wearing typically female ornamentation, simply because they were financially limited in their choice (e.g. Wood 1868). Further confusion may be produced in some instances by double burial where the skeletal evidence has vanished; Graslund (1980) suggested that this may have been an explanation for the mixing of "male" and "female" artefacts in some graves at Birka. Additionally, it must be remembered that the grave goods need not necessarily have been personal possessions of the deceased, or symbolic of their gender - some artefacts may have been contributed, and this may introduce artefacts that may be "typical" of the opposite gender, a point also raised by Kurtz and Boardman (1971: 209) for Greek burials.

Inevitably, there may not be an absolute distinction between male and female in terms of burial goods, despite attempts, such as by van de Velde (1979), to sex burials on the basis of grave goods when skeletal evidence is lacking. In the Protogeometric of Greece, Kurtz and Boardman (1971: 37) indicate that neck-handled amphorae may have held male cremations, while belly-handled amphorae were typically for female cremations but the distinction was "not too strictly observed". The degree of constraint involved in burying particular artefacts with a particular gender may, in itself, be indicative of gender status differences in the society. O'Shea (1984) noted this in the native American cemeteries he analysed; at Linwood, for example, eight artefact types were limited to males, while none were limited to females, and this increased emphasis on males was suggested as being a consequence of wider trading contacts (ibid., 115;131). Gibbs (1987) studied gender relations in N.E. Zealand in the Bronze Age and noted that until the Late Bronze Age there was an open association of males with weapons, tools and agricultural activities, after which grave goods with males tended to be of a more personal nature. At this time female burials became indistinct, in that they lacked characteristically female items, and this was seen as representing male control over female emphasis on their increasing importance to the economy (ibid.). The increasing importance of females in agriculture was represented by hoards linking the two, possibly representing female "protest" against male control (ibid.). This interpretation, if correct, indicates the potential complexities of gender symbolism in burial, the links with other aspects of the archaeological record and the possible "active" nature of such symbolism in structuring the society, emphasising the fact that the symbolism may be constantly changing and evolving to deal with particular circumstances. This reinforces the need to look at burial symbolism from an evolutionary, non-static perspective.

There are stereo-typical and ethno-centric ideas which the archaeologist may have about "male" and "female" goods weapons being male, for example, and spindle whorls being female, for example - which quite often do not hold in a particular cultural context. Dommasnes (1991: 4-5) notes the presence of hunting or shipbuilding equipment with female graves, and the presence of spinning whorls and weaving battens with males; these role reversals possibly reflected women taking over male roles in the latter's absence (ibid., 5). In our own contemporary society earrings and bangles would, say fifty years ago, have been viewed almost exclusively as female items, yet today they are commonly worn by both male and female. Clearly earrings in the past were worn by both men and women (Malinowski 1983), and can be observed ethnographically as being both male and female objects within a single culture. There is a clear danger

33

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

here in imposing our own cultural viewpoint regarding gender differentiation upon the past.

millennium B.C. there were only half as many high-status female burials compared to males was taken by Jensen (1982: 174) as indicating that some of the high-status males had married "commoners" whose status was not seen in burial; this actually suggests that female status need not necessarily reflect that of the husband.

Therefore, gender symbolisation in burial can take numerous different forms and can have a varied significance. In particular, symbolisation need not be static, but can have an evolving and active role in defining gender relationships within the society. The degree of ritual control over relative male-female symbolisms will reflect economic, demographic and social changes, and it can be difficult to equate the relative degree of male/female symbolisation with status. The dangers of imposing ethno-centric ideas of gender roles and symbolisations also need to be avoided. An additional problem is that gender symbolisations may survive in burial even if not quite appropriate in a particular setting - the survival of opposing male/female orientations, a pagan custom, in some areas of Estonia until the seventeenth century, for example (Valk 1992). Thus a particular gender symbolisation seen archaeologically may have a distant origin, and may not be necessarily an expression of gender relationships as they exist at that point in time.

3.3

Determining females

relative

status

of males

Generally, there seems to be uncertainty as to male and female connections in the wealth system. Arnold (1980: 132) in studying Anglo-Saxon cemeteries admitted that not much was known about how females acquired their wealth, but suggested that it was most likely a reflection of husband's wealth. One complicating aspect of female status, which can be observed ethnographically influencing mortuary practice, is the timing of death relative to an important male relative, such as a husband. For high-caste Hindu women, a "good" death is one in which she dies before her husband (Parry 1994: 158); a women who dies fulfilling this criterion has the most elaborate costume and pre-cremation preparation (ibid., 174). Females commemorated in Roman tombstone inscriptions also tended to have died before their husbands had, so there was some one to actually make the commemoration (T.G. Parkin 1992). Therefore, "status" differences apparent amongst females in burial reflects not simply their status in the living society, but also who survived them, and had sufficient interest to make their burial elaborate.

and

Given the problems inherent in exammmg male/female symbolism in burial, it is unsurprising that determining the relative status of males and females is similarly difficult. Earlier ideas about the existence of matriarchies in the early Neolithic clearly demonstrated the possibility of misinterpretation of the archaeological evidence (Hutton 1991); Hutton takes a pessimistic view of attempts to reconstruct such relationships in prehistory : " ...the question of gender relations in prehistoric societies is wide open and probably insoluble" (ibid., 339). The very fact that the gender distinction represents two different scales of analysis makes the comparison of status across gender boundaries all the more problematic. As with general studies of status based on grave analysis, the possibility of status being symbolised in non-mortuary or non-preserved forms is a complication made even greater by the cross-cutting gender dimension.

That females may obtain status and wealth from male associates is an undoubted and ethnographically-observable phenomenon, and is not limited merely to the husband-wife relationship. Wood (1868), for example, observed how amongst the Ovambo of southern Africa, the sister of a chief could be distinguished by the wearing of dozens of rings. Other studies have focused upon female status in general, particularly looking at how it may change through time. O'Brien (1978) suggested that in Corded Ware society females had good potential for achieving status, but female status seemed to be more restricted with the appearance of Bell Beakers. O'Shea and Zvelebil (1984: 17), though suggesting that female wealth was attained through male links, as noted above, also pointed out that females were active in the ritual/political life of the society, and could hold all status positions, except one. Female status in 1st century A.D. Norway was considered by Dommasnes (1992) to be equal to, or even greater than that of men. As mentioned above, female status at Branc was possibly interpreted as being ascribed at birth by Shennan (1975); this possibility was to some extent borne out by the fact that the rich females were less likely to die as infants than other groups, suggesting preferential treatment (ibid., 285-6). However, Hedeager (1992) seems to be over-simplistic in equating rich female graves in the Iron age of eastern Denmark with a possibly matrilineal/bilateral social system when she states that there would be no reason for wealthy female graves if status was passed purely through the male line. This views grave-wealth from a purely political aspect, rather than considering the various other factors that can result in wealthy graves.

The relative status of males and females is usually established through analysis of their grave goods. Frequently rich female burials are explained in terms of a wealthy husband or family. Dommasnes (1992) has criticised this viewpoint, noting as an example how the Oseberg ship burial (of an unidentified queen) was analysed from the point of view of the male activities represented by the artefacts found, rather than focussing upon the status of the woman in her own right. This is very common archaeologically. At Oleneostrovski the fact that female wealth varied less with age compared to males was taken as indicating that wealth for females was obtained through their links with males who were directly involved in the "wealth-generating system" (O'Shea and Zvelebil 1984: 18-19). At Branc females were seen as being a "vehicle" for display of status by their husbands (Shennan 1975: 286); rich female infants were possibly the product of arranged marriages, though the alternative possibility was that they represented ascribed status (ibid.). Bintliff (1984b: 97) suggests that rich female burials at Nitra and other sites of similar period reflected the kin of "Big Men". The relative numbers of male and female burials have been used to imply connections between males and females. The fact that in Denmark in the second

One of the more specific examinations of the relationship between gender and wealth was by Skomal (1983), examining changes in social structure in the Carpathian basin in the fifth-fourth millennia B.C. Two of her hypotheses related to changing aspects of male/female relationships. Thus, hypothesis 2 states, that under the influence of ProtoIndo-European culture: 34

The Gender Dimension in Mortuary Studies Arnold's wealth analysis of Anglo-Saxon graves (1980) demonstrated that female graves had on average a greater degree of wealth than males (Pader 1980, 1982 produced similar results); however, when wealth was examined in terms of a Number of Artefact Types (N.A.T) analysis, there was less of a difference between male and female which, Arnold suggested, indicated that female wealth was displayed not by a great variety of grave goods, but by larger numbers of whatever objects may be included; at Long Wittenham 1, for example, the woman buried in grave 71 is considered to be high status not because of a great range of burial goods, but because she had many amber beads (Arnold 1980: 120). A further complication was encountered in that the number of "distinct" female groupings in terms of wealth scores was greater than for the males. Arnold suggested that this was a result of the greater range of female wealth being dispersed across the histogram, which undermines the usefulness of the method for discerning groupings in the first place. This is further underlined by Arnold's admission that groupings on the individual sex histograms should probably be associated, though falling in different groups, which may have resulted in the number of social positions being over-estimated by as much as double the "real" number. Clearly there are problems introduced here by the gender factor in attempting to identify the number of status positions represented in the cemetery.

Wealth will be sexually related: Males will [a] possess greater wealth than their ratio to the females in the population; [b] exhibit a less uniform distribution of wealth as measured by the Gini coefficient; [c] their mean wealth will be greater; [d] exhibit a greater standard deviation; and [e] a greater range of wealth points than that of females (ibid., 177). This hypothesis is based on the assumption that with an ascribed, patrilinear system, females are "a homogenous segment, who participate less visibly in group-wide affairs", with correspondingly a lesser amount and variability of wealth (ibid., 178). Analysis showed that the Tiszapolgar culture satisfied four of the above criteria, the Lengyel culture satisfied two, while for the Bodrogkeresztur culture females actually had a higher proportion of wealth, on average, 16% more wealth than males (ibid., 179-182; 203-205). The results were not quite as expected. In the Chacolithic, female wealth had a greater range and variability than males, suggesting "females appear to have enjoyed greater visibility in group-wide affairs than would otherwise be expected in a male-dominated economy" (ibid., 203). In the Early Copper Age males had clearly more wealth than females, though females had a greater variation in wealth, which was suggested as reflecting their inferior status (ibid., 204). In the Middle Copper Age, the situation was completely different, with female wealth predominant, so that "female input had greater impact upon survival than during either of the preceding cultural periods"; another observation was the fact that twelve burial goods previously associated with males in the ECA were also found with females (ibid., 204-5).

Pader (1982) suggests that the apparent wealth of females in Anglo-Saxon cemeteries results from the fact that they wore more ornamentation and were "used" for status display by males. Philpott (1991) noted that unworn jewellery found in Roman-British burials with young females has been suggested by some as representing a token dowry for those females who died before marriage. Pader also noted the fact that female burial goods are often related to clothing and may be subject to fashion variations, adding further complications. Similar artefacts may have different "meanings" and even symbolic value when placed in male versus female graves. At the Fletcher native American cemetery site wampum necklaces were taken as indicating high male status, yet were also found with poor female burials (Mainfort 1985: 573), and S0rensen (1992: 39) queried whether the presence of a sickle in Danish graves had the same symbolic meaning for males and females.

Hypothesis 5 suggests that: Burial wealth increases with the age of a male adult, while the female adult's wealth is uncorrelated with age (ibid., 186). This hypothesis basically suggests that with a system of ascribed wealth, females would obtain wealth though their links to males. Males, in addition to having an ascribed status, will also achieve a degree of wealth, thus favouring older individuals. The hypothesis was confirmed in the sense that female wealth was uncorrelated with age, but the results for males were different; for the ECA and MCA cultures, a positive correlation with male age was found, but for the Lengyel culture the correlation was negative. Skomal suggested that "the fact advanced age did not necessarily provide a Lengyel adult with greater wealth lends further support to the non-egalitarian character of this society" (ibid., 209).

Ahern's study of a Chinese village in north Taiwan (1973) provides some interesting insights into the complications that can be involved in assessing female status. Married females have full membership in the lineages of their husbands, and have a commemorative tablet placed in the village's ancestral hall. Those who have children are particularly esteemed, because of their contribution to the continuation of the lineage. Adult females who are unmarried, however, are of a lower standing and, although they can have a tablet made, do not have it placed in the ancestral hall. Young girls have the lowest status, and are not considered to belong to the lineage at all, since they will not contribute children and will in fact take away wealth in the form of a dowry. However, this basic pattern is complicated by the fact that some girls may have a posthumous marriage, and consequently may have a tablet placed in the lineage hall of her husband (ibid., 127-9). This particular example emphasises how the importance of reproduction in a society can produce a variety of female statuses that are unrelated to status in the usual archaeological sense of achieved or ascribed wealth. Also, status in death is given recognition in a non-burial context, even though this status recognition is

While it is useful to attempt making general expectations with regard to male/female wealth in burial, difficulties in quantifying and "explaining" the significance of male/female wealth in different cultural contexts and through time, as examined below, will always make attempts at such general hypotheses dubious.

3.4

Complicating factors in determining relative male/female status

A number of gender-related issues have complicated attempts to analyse relative status of male and female through burial. 35

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

intimately connected with ancestor worship; the ancestral hall provides a focus for this, rather than the grave itself, which is actually viewed in a completely different way, even to the extent of being associated with different kinds of spirits (ibid., 173-4).

need, since the evidence of gender role comes mostly from burial itself. Some horizontal-type groupings will be differentially organised for males and females within a particular society, which may complicate attempts at analysing relative status, particularly if the horizontal and vertical dimensions are difficult to differentiate. In South Pentecost, in the New Hebrides, there are graded societies for both males and females, but whereas the male society has on average ten grades through which men can pass, the female version averages only three grades (Lane 1965). Also, female attainment of particular grades may be linked to achievements of male relatives, such as a husband or son, within their own graded society (ibid.). Generally, male societies are more common ethnographically than female versions (Mair 1965: 57). However, Mainfort (1985) has noted the presence of greater horizontal differentiation among females at the Fletcher site, though he is vague as to the significance of this. Fashion variations among females in terms of costume (Pader 1982) also can be a factor when considering potential "horizontal" symbolisations for males and females.

This example demonstrates that symbols of status, following death, may not be directly linked with individual burials, and may not be equally applicable to males and females. In ethnographically-observed megalithic burials, part of the structure may be associated with male burial, but not female. Thus in Madagascar traditional construction of the tomb involves the eastern wall being erected by the family of a man's first wife, while other wive's families erect the other walls (Parker Pearson 1992: 944). In addition, male interments added to a tomb may be marked by placing pairs of standing stones at the east and west ends of the tombs (ibid.). Various other post-burial activities seem to be restricted to males. In some regions of the New Hebrides, male commemoration involves the launching of a model canoe containing provisions and "luxuries", which ends up at the deceased's mother's village, and is displayed on a stone platform (Layard 1942: 552-3); this, and other ceremonies, are not applicable to females (ibid., 564-5). The man's skull is used to make the head for the effigy, and the effigy is placed on display, with the skull eventually being placed in an ossuary (ibid., 546-7). In the Mandla region of India a dead man's son may erect a monument by a road side in his name, either a gur (a pole with cloth and bangles on top of it) or a stone platform called a chaura. Offerings may be made at these monuments by passers-by (Fuchs 1960: 352-3). While archaeologists have recognised that status may be symbolised in non-mortuary contexts, the different applicability to males and females is rarely considered, so that, for example, apparently equivalent burial status for male/female may only be an illusion when the other aspects of post-mortem status symbolisation are examined.

Yet another factor of interest, rarely considered either ethnographically or archaeologically, is the relative attitude of males and females to death and burial. The usual assumption is that both sexes have the same or similar opinions and approaches to burial, yet the possibility of different mental templates regarding the significance and opportunities presented by burial should not be discounted completely. Females, if they have the domestic context as an arena for status display, may have less emphasis placed on the burial domain as a context for display, while males may be more inclined to use the ritual of the occasion as a status platform. This may exaggerate differences in status. Female activities in the funerary procedure also tend to be less directly involved with the physical burial and placement of goods in the grave, unlike males - instead they often are employed in the washing/painting of the body, and the process of visible mourning. These processes, involving more direct display of emotion than is usually open to the male counterpart, may represent the fulfillment of obligations to the deceased; in the case of a dead woman, therefore, the female associates with whom she would have been closely connected may not be as inclined to symbolise friendship through material goods, unlike the male associates of a dead man, who are denied the more emotional display and may therefore be required to make a more material contribution. This also may produce exaggerated differences in apparent status, when examining the material aspects of burial. Clearly, however, this all depends on the degree of ritual control over burial, and particularly what, and who, determines the nature of the grave contents.

A further gender distinction that could complicate attempts at analysing male/female status is the kind of industries in which male and females are involved with in everyday life. For example, the Tanala of Madagascar have a strict dichotomy of manufacturing pursuits related to gender; males do all production relating to metal, bone, wood, horn and hide, while females work usually with mats, baskets and cloth (Linton 1933). There is an obvious difference here with regard to archaeological preservation - products of male industry will generally preserve archaeologically, while female products will not. If the grave goods placed with a deceased relate in part to the role played by the individual in society, or the possessions of the individual have a bias (not unnaturally) towards those objects he/she may have been able to manufacture themselves (e.g. Rowlett 1984), or contributed grave goods come from predominantly people of the same gender (again, not unlikely, because these will have been the people the person was most closely associated with, beyond relatives), the kind of differential preservation noted above may produce a false or exaggerated imbalance of wealth in favour of one gender. Dommasnes (1992: 5) analysed male/female roles in Scandanavia and found that, from the grave evidence, males performed more tasks than females; while this could represent status differences in itself, it could also produce misleading impressions of relative wealth. This high-lights the need to examine the roles male and female played in production when assessing burial wealth - but, unfortunately, there is a circular argument inherent in this

The chronological aspect is an important one when considering male/female status, since the relative importance and standing of males and females can vary drastically as a consequence of economic, demographic, social and other factors. As Sjijrensen (1991: 47) emphasised, there is no reason to believe that male and female status patterns should change in parallel with general social changes. These changes can be seen in both contemporary and archaeological societies. In rural Greece Dubisch (1989) has observed how female control over the ritual associated with burial has been diminished with the increasing "commodification" of the

36

The Gender Dimension in Mortuary Studies funerary procedure, allowing men to purchase items that made female mourning less exclusive and necessary:

are often observed archaeologically and may be indicative of lower female status, though other interpretations, such as patrilocal residence (see below) have been forwarded. Thus, at Holywell Row Anglo-Saxon cemetery males were more constrained in their possession of sex-linked objects than females (Pader 1982). A further indication of lower female status, suggested by Pader (1982) is the degree of similarity to sub-adult burials; when females are of lesser importance to the society their burial ritual may resemble that of the similarly unimportant sub-adults. This was also noted by Robb (1994) in an analysis of Neolithic burials in Peninsular Italy, with juveniles being buried on the same side as was used for adult females; this suggested that males who had not attained culturally-determined adulthood were buried in a manner akin to females. Examples of this can be seen ethnographically. The Kapauku Papuans place children and women (and old people) together as a category for a particular form of burial, opposed to other categories such as adult man or warrior (Pospisil 1978). The Gurungs of Nepal bury women and children, this being a cheaper form of disposal in comparison to cremation (Messerschmidt 1976:95). On Nggatokae, in the Western Solomon Islands, the Marovo women and children did not have their skulls placed separately from the rest of the body, unlike for men (Wall and Kuschel 1975: 59-60). In these cases the lower status of women is being emphasised by categorising them with other low-status groupings with regard to mortuary treatment. However, in some cases the similarity between female and child burials may not relate to any perceived lower status of the former. Instead, the similarity may result from the close physical and symbolic connection between women and children that exists in all societies and which may be particularly important in the mortuary domain, given the emphasis often found here on fertility within the society. It would be dangerous, therefore, to generalise too far about the similarity between female/child burial treatments.

The commodification of mortuary ritual, in addition to indicating the penetrations of a cash economy, also signals an important shift in the way in which social relationships are expressed and social obligations are fulfilled (ibid., 195). In most societies women have an important role in mourning, emphasising the fact that the society can continue to reproduce itself despite the death of one of its members (Monnig 1967: 138). However, the actual esteem in which women will be held when it comes to status recognition will be dependent on the importance of economic and demographic factors. In the early Neolithic of central Europe, when land was first being settled, reproduction was important because labour, not land, was scarce (Hodder 1984). Later, when land became a more limiting resource, more importance was placed on restricting the number of potential heirs, and there was less emphasis on the importance of women as reproducers (ibid., 62-3). In the Early Bronze Age of Denmark there is evidence of the increased status of women as population density increases, possibly reflecting a changing role in the economy with new agricultural practices (Randsborg 1974). Dommasnes (1992) suggests that the decrease in the number of female burials in Norway from the Early Roman period to the Viking age reflects a decline in female status relating to changes in social structure and/or subsistence economy; religious/ideological factors could also be responsible, possibly with a shift from a fertility cult to one based more on warrior status being responsible for declining female status (ibid.). Some changes in the relative standing of male and female may be more apparent than real, relating to general changes in attitude or technology. Thus in the early Unetice male burials seem to be poorer, with few tools or weaponry deposited. O'Brien (1978) explains this as resulting from a change in ritual reducing the need to include such items, and also because they used up more copper than female items such as jewelry, thus being considered too expensive to waste in burial. Male status was not considered to have changed, therefore. The Early Bronze Age of Slovakia sees a decrease in the number of poor female burials in the later period, which S.E. Shennan (1982) suggests could be because of increased availability of metal, allowing cheaper production of ornamentation. A decline in wealth in male graves in Late Bronze Age Denmark may reflect changes in inheritance rather than an active change in male status (S0rensen 1991: 42) and the rise of female wealth display at this time was possibly related to the formalisation of male status positions, with attention being switched to the importance of females in creating alliances and their role in production (Kristiansen 1984).

Related to this, and of great theoretical importance, is the assumption that in a non-egalitarian society the grave good distinction between male and female will be low (e.g. King 1978, Wright 1978). This kind of generalisation is difficult to uphold, particularly when it seems clear from some of the evidence presented above that the symbolic representation of gender roles through grave good form may be considered important in societies which are non-egalitarian in nature. As with many aspects of mortuary analysis, and as has been emphasised before, this is very dependent on the historical circumstances that have shaped, and continue to shape the burial practice within a society. The Anglo-Saxon cemeteries analysed above could not be termed egalitarian in structure, despite the fact that there is a clear differentiation between male and female grave goods. Suggesting that a ranked society will have particular artefacts that cross-cut sex divisions, as Wright (1978) does, ignores the potential for high-status male/female positions to have separate methods of symbolisation, utilising valuable artefacts that still retain the ever-present and ever-important gender distinction.

Interestingly, some aspects of change in burial could be instigated through female practice because of the very fact that they were considered of lesser status, and their burial procedure was less constrained than more formalised male procedure. The rise of the nocturnal burial in eighteenth century England was to a large degree brought about initially by female funerals of this form, which were tolerated by the College of Arms because the lower female status did not require as strict a following of the heraldic tradition (Gittings 1984: 189). Less constraint in female dimensions of burial

3.5 Post-marital

residence rules

Attempts to discern what kind of post-marital residence rules are at work within a particular archaeological society are frequently based on an analysis of burial patterning, comparing that for males and females. Examples of such attempts would be Saxe (1971), Lane and Sublett (1972), and 37

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

van de Velde (1979). Van de Velde (1979) suggested that patrilocal residence would be found in situations were male activities like hunting and fighting were of importance to a community, while if female activities were important matrilocal patterning would be more likely. He suggested that at Elsoo the likely pattern was matrilocal, based on the pottery decorations - the separate distribution of female graves with curvilinearly-decorated pottery and rectilinearly decorated pottery was seen as being indicative of this (ibid., 43). Males, on the other hand, were buried with grave goods from both groupings. Lane and Sublett (1972) analysed five Seneca cemeteries in New York and, on the basis of the relative morphological variability of males and females within and between cemeteries, concluded that there was a patrilocal pattern of residence. Saxe (1971) concluded that at Wadi Halfa there was patrilocal residence and went as far as to say that patrilocality was "probably commensurate with the very existence of a cemetery" (48). This pattern was suggested because of the greater variability seen in females for positioning - in particular, the degree of flex between the spinal column and femur was less than 45 degrees for identified males, but much more variable for females, with only one being as tightly flexed as the males were (ibid., 47).

associated with the attainment of marriageable age, rather than anything related to post-marital residence patterns. The assumption that greater female variability in mortuary characteristics represents patrilocal residence is, therefore, a flawed one. As noted above, female variability may relate to lesser social standing and subsequently less constraint or care involved in mortuary disposal. An additional possibility, in terms of artefactual inclusion, is that females may be more susceptible to fashion variations (Pader 1982) than males, within and between time periods. Introducing chronology, the possibility also exists of changing male/female status (as discussed above) resulting in greater variability in one sex as opposed to the other, when a broad time period is considered. Another fundamental assumption, underlying such interpretations like Saxe's, is that a female residing in her husband's village will be buried, either by her own people or according to the tradition of her own people. While this may occur in some cases, it is not difficult to find examples that contradict this ethnographically. The Pedi of the Transvaal, for example, have patrilocal residence, and females are buried by her affines (though permission is requiredfrom her own kin group) (Monnig 1967: 138); the same situation exists for the Mandari of Sudan (Buxton 1973: 114). There is no reason to believe that an incoming female will be buried in the manner of her own village, particularly when burial is by the husband's family who may only be familiar with, or ritually constrained to, a particular burial form. In fact burial by close relatives is not allowed in some societies, such as the LoDagaa (Goody 1962).

Not surprisingly, given the dubiousness of the evidence, doubts have been expressed by other archaeologists about any such attempts to reconstruct post-marital residence rules from archaeological data - for example, Milisauskas (1978: 11820), Pader (1982), Pardoe (1988: 12-13) and Hodder (1990: 68). Milisauskas, for example, commented that speculations about LBK society being matrilocal were "frequently based on very general ethnographic analogy" (1978: 118). Criticisms can be levelled at some of the particular examples above. Eggert (1979) criticised van de Velde's pottery-based reconstructions, saying that the pattern could be explained by chronological patterns rather than any kinship groupings. Bishop (1979) also criticised the fact that van de Velde had ignored settlement evidence which may have helped in determining post-marital patterning. Saxe's interpretation of the evidence also seems questionable. Female variability in mortuary practice may reflect less care as a consequence of lower status, rather than being the product of a more varied set of traditions than for males - basically, it may not have been considered as important to bind female legs as it clearly was for males. Saxe recognises the possibility of a status difference at work here, but is unconvincing in his arguments against it when he states that this interpretation must explain why "adult males were apparently more important and were therefore treated with more rigor ...this leaves the question of why they should be more important unanswered" (Saxe 1971: 48). This is particularly unconvincing when Saxe seems to have given the answer to this very question on just the previous page, when discussing the relative importance of individuals buried in pairs. Those buried on the outside of the pair are, he says, " ...individuals who would tend to have higher status in a hunting and gathering, egalitarian society e.g. males as against females of similar ages ..." (ibid., 47). Saxe's argument would receive some degree of support if a differentiation in treatment between females above and below marrying age could be detected, particularly if unmarried females could be demonstrated as having a closer affinity to males in their mortuary treatment than those who were married and "outsiders". However, the age evidence is inadequate to demonstrate this, and in any case such a differentiation could relate to female age symbolism

Perhaps the most damning examination of attempts to reconstruct post-marital residence rules is by Allen and Richardson (1971), and it is worth repeating the main points of their argument here. They emphasised the fact that even ethnographically it may be difficult to determine residence patterns, and even study of the same area by different anthropologists may give contrasting results (ibid., 44). Many factors may be involved in determining the residence pattern, economy and property ownership being the most important, and there often is a great contrast between what the rules are and what actually is done in practice: there is "great disparity between residence rules and the actual choice of residence within a specific social group" (ibid., 46). Attempts at reconstructing residence rules through ceramic analysis are also criticised by Allen and Richardson. Suggestions that varied pottery decoration reflect patrilocal residence (because of the movement of females with their own particular patterns) while more defined patterns represent matrilocal residence (since the female potters stay together) are dubious because of other possible explanations, and also because artefacts such as pottery need not necessarily have always been produced by one sex or the other, and in fact both male and female may have been involved in the manufacture of a particular artefact (ibid., 50). They conclude that the difficulties of determining residence rules in living, let alone archaeological contexts, together with the "low information content" involved in these studies, would make it better for archaeologists to concentrate on more productive areas ofresearch (ibid., 51). Certainly, the ethnographic literature reveals varied and sometimes casual attitudes regarding choice of residence following marriage. For the Merina, Bloch (1971) notes, the traditional pattern is patrilocal but in practice various other forms are followed. For one village, for example, the figures 38

The Gender Dimension in Mortuary Studies for marriage patterns were: 46% virilocal, 35% uxorilcal and 19% neolocal/unknown (ibid., 191). Thus a substantial minority of couples were residing with the female's family, despite the supposed rule of patrilocality. The Yoruba of Nigeria have patrilocal marriage, but in the case of a poor man marrying into a wealthy family, residence may be with her family (Bascom 1969). Even when the latter occurs, the man will be buried in his own compound rather than that of the wife's family. A similar patrilocal/matrilocal ambiguity exists for the Tanala of Madagascar for the wealthy family of a woman (Linton 1933). Some societies, such as the Grand Valley Dani (Heider 1979), lack any particular form of postmarital residence, while elsewhere a combination of both main forms may occur; thus the Lamba of former Rhodesia have marriage initially as matrilocal, but at a later stage the couple will move to the man's village (Doke 1931).

3) The relative status of males and females may change through time, but so may other aspects of society that do not involve status changes, though g1vmg this impression in burial practice. Economical developments may have a differential impact on the grave goods placed with males and females, which can give the impression of increased/decreasedstatus for either sex. Other arenas of display may become alternatives to mortuary display, such as the domestic context, or hoard deposition, and these other arenas may be utilised differently by the sexes, resulting in apparent status differences in burial. 4) Attempts to reconstruct post-marital residence patterns seem futile, given the complexities that exist in living societies, and the ambiguous ways in which the archaeological burial data can be interpreted.

Thus it is clear that, in reality, clear rules defining postmarital residence may not exist, making archaeological attempts to discover them particularly dubious. The assumptions that underlie attempts at these reconstructions seem weak when examined within their own context, and when compared to ethnographic evidence. Attempts at identifying particular artefact patterns seems dubious and over-simplistic, particularly when it is considered that females may be incoming from numerous different regions, not just one or two. As with other aspects of mortuary analysis, it seems impossible to specify particular rules of interpretation that will be applicable in all circumstances when attempting to determine the pattern of post-marital residence, and it seems that the only approach that has any possibility of success is a detailed integration of all the evidence available, both burial and non-burial. Even then there is little certainty of a correct answer.

3.6

The number of possible processes at work, together with the potential for their complex interaction in any one situation, and the further blurring effects of chronology, illustrates the severe limitations in attempting to explain a particular patterning within a cemetery in terms of any single process, particularly so when it is envisaged that this process may be at work and comparable across different time periods and in different regions, as is often attempted archaeologically. The conclusion must therefore be that generalisations about the "meaning" and significance of gender distinctions within a funerary context are difficult, and an attempt at understanding such distinctions can only be approached utilising all the information that is available about that society from every aspect of the archaeological record, rather than attempting such reconstructions on the basis of the mortuary practice alone.

Conclusion

In conclusion, the gender dimension is one that is intimately

associated with all aspects of funerary procedures, not solely in terms of the deceased person, but also with respect to gender relations in the living society. A number of points can be made based on the ethnographic, historical and archaeological evidence: 1) The symbolisms distinguishing males and females in

burial may have significance as to their idealized roles or importance in the living society. Some of these symbolisms, however, need not relate to the contemporary society, but may be anachronistic survivals. They may also be ritually controlled so that they do not necessarily accurately represent reality, but rather the interests of those who dominate in society. Examination of other aspects of the archaeological record, such as the domestic context and hoards, may help clarify the reality of the situation. 2) Determining the relative status of male and females, based on grave wealth, is complicated by various factors, such as differing attitudes, use of different contexts, fashion variations and differing economical activities. The "value" of a particular object may differ for males and females, and may not have the same symbolic or functional significance.

39

Chapter 4 The Horizontal Dimension in Mortuary Studies 4.1 Introduction

since such groupings have particular beliefs about death and burial that could translate into our own contemporary idea of "religion". Ethnic grouping is possibly another category that could be included under the term, though in itself this term may be somewhat ambiguous archaeologically.

The horizontal dimension in mortuary practice is one which is often ill-defined, and frequently inadequately considered, being neglected in favour of a focus on purely status aspects of burial. This has not always been the case; Chapman and Randsborg (1981) noted that in the first half of the twentieth century the reverse was true, with groupings such as clans being the main focus of interest. This point is clearly stated by Tainter and Cordy (1977: 97) when they comment on methods of analysing vertical differentiation, but note "no comparable criterion has been developed for monitoring structural differentiation along the horizontal dimension". Binford (1971: 221-222) seems to be making a connection between social complexity and the degree of horizontal differentiation when he notes "that other things being equal, the heterogeneity in mortuary practice which is characteristic of a single socio-cultural unit would vary directly with the complexity of the status hierarchy, as well as with the complexity of the overall organization of the society with regard to membership units and other forms of sodalities" (emphasis added). There is no demonstrated connection between vertical and horizontal complexity, hence the scenario may exist where a society has complex horizontal organisation, yet may not be complex in vertical terms. If this were so, "heterogeneity in mortuary practice" could be found in a society that was not "complex" (in the usual, implied vertical sense). This alone justifies an interest in horizontal divisions, but clearly there is an impression that horizontal groupings are of lesser importance in the study of social structure. As O'Shea emphasises, "...the evidence for the kinds of horizontal differentiation employed by a given society is a question of considerable anthropological interest" (1984: 20), though O'Shea himself suggests that the detection of horizontal groupings might be best attempted in non-funerary contexts.

4.2

The archaeological differentiation

study

of

horizontal

Most archaeological identification of horizontal differentiation relates to tenuous statements about the presence of lineages or clans, or other "groupings". The uncertainties with which these are sometimes identified demonstrates the problems in making firm conclusions about such groupings based solely on funerary practice. Inevitably many of the distinctions that mark the funerary procedures of different horizontal groupings will be lost archaeologically, and there is no reason to believe that redundancy in burial attributes will always be such that the non-preserved elements will be reflected in more archaeologicallyrecoverable aspects - though this sometimes happens, for example, when the orientation of the grave reveals a clan distinction that may be lost because of non-preservation of a clan totem in the grave, if one existed. Perhaps one of the best studies of horizontal differentiation has been by O'Shea (1981, 1984), in which he demonstrated the limitations in both ethno-historical and archaeological reconstructions of horizontal differentiation, when examining Plains Indian societies. O'Shea was particularly concerned with the transformations between the mortuary record and the archaeological record, and how this transformation could differentially affect social distinctions. Horizontal symbolisms tended to be in a form which would preserve less readily than other symbolisms in the mortuary domain. Thus, for example, the Pawnee were known to have horizontal differentiation in their society, but this was not revealed in the mortuary practices by either archaeological or ethno-historical analysis. O'Shea concluded that "horizontal distinctions should be expressed through channels of 'neutral' value. Hence, 'unvaluable' tokens such as clothing, coiffure, symbolically distinctive artefacts, and elements of body posture and orientation, should be common indicators of horizontal differences" (1981: 49-50). These, therefore, are likely to be ambiguous in their meaning or non-preserving in the first instance (ibid., 50). A further problem in identifying horizontal divisions is the fact that these divisions are not as distinct as vertical divisions because they have a normal age/sex distribution, so that "horizontal social units are often indistinguishable from a wholly random or idiosyncratic occurrence" (ibid., 50). This point is not strictly true, however, if we take the term "horizontal differentiation" as incorporating (as O'Shea does) societies, which will frequently be skewed in terms of age, sex, or both. O'Shea further demonstrated the ambiguity of horizontal divisions when he presented a hypothetical example in which there were three clan groupings buried in a cemetery, each with a distinct totem, two of which did not survive in the archaeological record; the preservation of the remaining clan's

Different definitions have been given for "horizontal differentiation", and the same term has been used in different contexts, emphasising how loosely it is applied in practice. O'Shea (1981) uses the term as referring to "kin-based organisation" (41), though contradicts himself by also including within this definition what he calls "societies (nonkin based)" (42). He also uses the term in a particularly broad sense when he incorporates within it age and gender patterning (e.g. O'Shea 1984: 131), which are really different dimensions altogether. Chapman (1980) defined the term as incorporating "e.g. descent groups, societies", but in a later publication (1990) uses the same term in a different context, to suggest the degree of craft specialisation in a society. Whittle (1988: 171) avoided the anthropologically-loaded terms such as clan or lineage altogether by using the term "interest groups". This lack of clear definition emphasises the broadness of the term, but also the problem in mortuary analysis of using terms which are vague in their meaning, in general. The term usually is applied to kin groups such as lineages, clans and moieties, but also applies to non-kin groupings such as societies. A further category that might be added would be religion, which in more modem contexts could take the place of divisions such as "clan" or "tribe", 40

The Horizantal Dimension in Mortuary Studies totem could give the impression of a two-rank social system, which would be completely misleading (ibid.). This also demonstrates the potential ambiguity of status markers, considering O'Shea's suggestion that clan symbols should be of fairly limited material value.

some of the kinship terminology used as being "so vague as to be meaningless" (ibid., 53), which again emphasises the problems of definition.

There is one potential criticism of O'Shea's work. He notes that horizontal divisions are "known" to be present in particular Indian societies, yet are not always recorded in the ethno-historical accounts of mortuary practices, nor are they recovered from archaeological analysis of burials. One obvious conclusion here, apart from the failure of archaeology or ethno-history to properly account for the full mortuary practices, could be that these divisions, even though present in the society, were not symbolised in the mortuary practices. Therefore failure to detect them would reflect a failure to symbolise aspects of social structure in burial, rather than a problem with post-depositional processes making archaeological reconstruction incomplete. There can be no automatic assumption that all aspects of social structure are considered equally worthy of symbolisation in burial. This may depend on local circumstances, particularly the importance of these groupings in the functioning of the society. The assumption that horizontal symbolisms will rarely be preserved in the archaeological record has also been questioned by some researchers such as Fisher (1995), who suggested that differences in dress or coiffure will often leave some kind of material trace (ornamentation, dress fasteners etc). Van de Velde (1979), in his much-quoted paper on the Elsloo Linear Pottery cemetery, identified a number of modes of production that might be expected in a Neolithic social structure ("mode of production" is defined here as "any institutionalized entity of relations and forces of production that has at least some autonomy/independence and an ideological charter" (ibid., 39). The second of these was the lineage mode of production, which consisted of "local kin segments, each consisting of a number of households, arranged in importance according to some ordering principle in the kin-descent sphere" (ibid., 40). These lineages, Van re Velde suggests, should be visible in a cemetery as groups of burials, and differences in wealth. From his analysis he concluded that there was a "matrilineal duality" present, marked by rectilinear/curvilinear pottery, in that females will be buried with the goods of her own kingroup, while a male may be buried with a mix of goods. Female curvilinearly marked graves are mostly in the south-east of the cemetery, while female rectilinearly-marked graves are mostly in the north-west, though there were exceptions to this which Van de Velde admitted "loom large", though he found ways to explain them (ibid., 44). A number of reviewers have heavily criticised van de Velde's analysis, including his interpretations of a "lineage mode of production" present. Eggert (1979) particularly attacked his approach, and suggested that the pottery distribution was more likely to be a product of chronology than anything, given the distribution of the rectilinear/curvilinear pottery types by phase (49). He particularly noted the concentration of graves with mixed pottery in phase 2, six out of eight cases. The geographical distribution of pottery types also was not as clear as was suggested by Van de Velde. Haglund (1979) criticised Van de Velde for not considering how grave goods actually were placed in the burial, in particular how exactly "mixed" grave goods are produced. He also describes

Morris (1987) discusses the nature of kinship in eleventh to sixth century B .C. Greece. He identified groupings within cemeteries as representing family plots, and estimated the kind of contributing populations that would be involved. A kinship basis to burial is considered certain during the period considered, and beyond, though he notes that at a later stage other principles could be involved, such as the presence of collegiae (burying clubs) (ibid., 90). The literary evidence suggests that in 4th century B.C. Athens the descent groups consisted of 3-4 generations and were called oikos. Each burial group would be formed from a number of families of varying status, the relative standing and wealth of which would be frequently changing (ibid., 90-1). Because the size of the descent groups varies, the size of the burying groups will similarly be varied (ibid., 91). Although Morris suggests that fluctuations in the size of the cemeteries through the period studied reflect exclusion/inclusion processes based on rank, he also suggests other explanations based on kinship which are not supported here but, none-theless, are worth considering as processes that could be at work in other circumstances; for example, variation in the size of the cemeteries, and plots within them, could be brought about by kinship changes, or kinship remained constant but was not symbolised spatially in all periods (ibid., 92). Although these are discounted here because of conflicting evidence, their very suggestion emphasises the changing nature of the burial record with regard to kinship groupings, both in real social terms (i.e. actual changes in kinship form) and in terms of representation of kinship in burial, the two not necessarily being inter-dependent.

4.3

Distinguishing groupings

horizontal

and

vertical

Distinguishing horizontal and vertical groupings in archaeological mortuary data can be difficult, and there are numerous examples in the literature of the two being confused, or at least uncertainly identified. Brown (1987) noted that an analysis of Havana Hopewell burials by Braun (1979) identified certain elements of burial as being related to corporate identity, in contrast to the idea, under Tainter's energy expenditure model, that they would have related to vertical status differences. Cluster analysis of the Indian Knoll burials was possibly suggestive of two different groups sharing the cemetery (Rothschild 1979), which might imply horizontal groupings, but Rothschild talks about these groups being potentially ranked lineages, clearly suggesting vertically-separated groups. Similarly, the existence of separate burial areas at the Big Village site (O'Shea 1984: 256) was considered probably to represent different corporate groupings, but the analysis also suggested possible "wealth and prerogative differences" between these groups. Determining whether artefactual inclusions in burial represent horizontal or vertical differences is clearly more difficult than theoretical definition of the terms would imply. The incorporation of mussel shells in graves at the Barcal site analysed by O'Shea (1984) could have been interpreted as symbolic of a sodality, but, equally, could have represented a rank distinction (ibid., 107). O'Shea also notes other indications of ritual positions, which could perhaps be interpreted as some form of society, though O'Shea tends to

41

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

put these as vertical distinctions. At Linwood, for example, pipe ownership was seen as marking "a hereditary position or privilege that was not directly tied to personal achievement or wealth" (ibid., 123); since the distinction was not apparently marked out by wealth, it could equally have represented a horizontal grouping in the society. Gilman (1990) similarly noted problems in determining the kind of group which artefacts symbolised. In an analysis of the social organization of Classic Mimbres burials in the south-western United States, it was noted that only 12% of burials at the Mattocks site had jewellery, and only 18% at the NAN Ranch site. Copper bells were other objects that occurred rarely and while these and jewellery could be symbolic of vertical differentiation, their lack of distinctiveness, and ease of manufacture, made this seem unlikely. Clusters of rich burials at the Mattocks site were interpreted as representing horizontal distinctions rather than vertical, because of the lack of distinct emblems that might have symbolised different ranks. The number of pottery vessels in a grave also seemed to indicate horizontal differentiation, with designs possibly marking out families/lineages, or cornrnunities (ibid.).

4.4 4.4.1

Ethnographic/archaeological examples horizontal differentiation in burial

the members of national, kinship, and other groups seize upon to assert their own identities, either against the world as a whole or else against members of similar collectives "(Goody 1962: 42). Goody likened such distinctions to school colours, or a college badge, the main factor influencing choice being "that they should be different from those adopted by similar groups" (ibid., 43). Lineage members may make contributions in the form of com or, more usually, cowrie shells, which become the property of the grave-diggers. On the funeral stand on which the corpse is placed the bows of other clan members may be displayed, and clan relics may also be shown to stress the solidarity and strength of the clan (ibid.). Other groupings may also have some involvement in the funeral - the Bagre secret society may collect money to buy funeral clothing for deceased members, for example. Ceremonial involvement in the burial by horizontal groups is not necessarily limited to the group within which the deceased person was a member, and this involvement may extend as far as the digging of the grave. For the Lodagaa, for example, burial in some areas is not by relatives but may be by members of a different patrician (Goody 1962: 67-69). The Daribi of Papua New Guinea similarly may have, on occasion, the graves of clan members dug by members of an allied clan, and if a death occurs in another clan's territory, that clan may make a small compensatory payment in the form of cloth or pearl shells (Wagner 1972: 146, 149). Among the Kaoka-speakers of Guadalcanal the corpse is prepared by women from a different clan to that of the dead person (Hogbin 1964). The Grand Valley Dani have two patrilineal moieties that both participate in funerary ritual (Heider 1979). Gifts brought to the funeral and draped over the corpse are supposed to be divided between the two moieties, so that different objects are contributed; thus, the dead man's moiety is supposed to contribute pigs, while the other moiety contribute shell bands. In reality, though, this distinction is not always followed. For the Gurungs of Nepal, clans are linked in the celebration of various mortuary ceremonies by joint contributions of food, beer, money and so on, incorporating clans who may not even be ritually involved in the burial (Messerschmidt 1976:85-6). The donations result in bonds between clans so that "a dynamic reciprocity is maintained, encouraging cooperation between clans" (ibid.).

of

Introduction

Ethnographically, the horizontal divisions in society particularly lineages and clans - can be seen to be involved in burial practice in several respects, such as compulsory attendance at the funeral, involvement in the actual ritual and burial, determining the structure of the burial, and contributing symbols representative of the group. Horizontal groups not actually related to the deceased may be involved pairs of clans, for example, may be linked in funeral friendships, in which the dead of one clan are buried by members of the other (Mair 1965). Competition between groupings can be a major factor at work in these practices, and it seems that ideas about horizontal groups being "equivalent", in a status sense at least, are unrealistic. Binford's ethnographic sample (1971) revealed that "social affiliation", as he called it, was most commonly symbolised by the location of the grave (15 examples out of 40), followed by orientation (9) and treatment of the body (2). The ethnographic sample must not have been completely representative, since social affiliation is undoubtedly marked by the form of furniture in some societies - in O'Shea's Indian societies (1984) noted above, for example, a clan totem may be included in the burial goods. However, it is true that the ethnographic literature generally lacks references to specific items being placed in the grave, or particular characteristics of the grave (apart from orientation) being used to mark horizontal affiliations.

4.4.2 Ritual and Ceremonial

Ritual distinctions between groupings such as clans or lineages exist in other arenas not necessarily directly involved with the funerary rituals of an individual. The Ohaffia of Nigeria have a matrilineal, patrilocal kin organisation in which the patrilineage is actually the residential group (Nsugbe 1974). The founder of a patrilineage is given status recognition in theobu hut, which contains the patrilineal shrine, and the pots of male ancestors of the patrilineage; these are supposed to be arranged in order of seniority but in practice may not be, because of the order of death. Female pots, on the other hand, are kept in the bedroom of the female elder. Emily Ahem's study of the cult of the deadin the Taiwanese village of Ch'inan (1973) emphasises the difference in focus between the ancestral hall, in which ancestral tablets are placed, and the actual grave. While the grave was an arena for expressing relationships between the living and the dead, the ancestral hall was more concerned directly with the living community, and particularly social and economic distinctions (ibid., 163). The kind of spirits present at the two foci also differed; the ancestral hall was

distinctions

Ritual distinctions - even of an apparently minor nature may be considered important in funeral procedure as a means of distinguishing horizontal groups. Among the Lodagaa, patricians may be distinguished by a particular form or element of the funeral which is unique to them - for example, the removal of the corpse's burial smock before the body is interred: "It is one of those standardised forms of action that

42

The Horizantal Dimension in Mortuary Studies occupied only by ancestral spirits, while the grave was home to a variety of more dangerous spirits (ibid., 173). Ritual houses in some societies are shared by several clans, though they still maintain their separate identities. The N gaing of the Rai coast, for example, have a cult house in each settlement which, if it is a multi-clan settlement, is divided into sections for use by the different clans; each clan decorates its section with particular ornaments, and use distinct clan musical instruments (Lawrence 1965).

society may vary from region to region, and will influence burial practice. Thus, among the Tanala of Madagascar, tombs are used indiscriminately by lineages but in certain areas the tombs may be clan-based and used by up to a dozen villages (Linton 1933). The tomb, in such circumstances, is an important central point for the religious life of the clan and destruction of the clan tomb of an enemy is one tactic that may be used during warfare (ibid.). These examples illustrate the considerable importance of funerary ceremonies in certain societies for demonstrating horizontal divisions. Clearly these activities may demonstrate the unity of the groups, both at a intra-group and inter-group level. However, competition between groups is also, in some instances, an important aspect of the funeral, and inevitably could result in apparent status differences, if carried through to the archaeological record. The ability of a clan/lineage to materially symbolise its separate identity may have an impact on the involvement of the group in the mortuary domain, and the degree to which it may use archaeologicallyrecoverable methods to distinguish itself. In the same way that alternative methods of status display may have an effect on grave good inclusions in burial, distinguishing high and low status, the availability of cult houses, shrines, mortuary totems etc., and the amount of ceremonial display used in funerals, may diminish the need for material symbolism in individual burials.

Some societies erect monuments following death, to mark clan solidarity. The Orokaiva of Papua New Guinea have various mortuary ceremonies following the burial of a clan member, one of which involves the erection of a tabu post, the naterai, in the middle of the village (Williams 1930). The post is made from the particular type of wood that is symbolic of the clan, and it marks the beginning of a food tabu, which ends some time later with a feast and the dismantling of the post (ibid., 226-7). Mementoes to the deceased person may be placed on a platform beside the post. One interesting point is that the post has intricate carvings, but these do not, as might be suspected, relate to any clan emblems, since different forms may be used by the same clan group. Rather, it seems that the patterns relate more to the imagination of the carpenter who produced the post (ibid., 226-7). This emphasises the need for archaeologists to be wary when attempting to associate particular patternings (on pottery, for example) with particular horizontal groupings; such patterns may have no particular social meaning.

4.4.3 Orientation and Positioning of the Corpse An important issue is the degree of competition that exists between nominally equivalent groupings such as clans/lineages, which will be a factor in funerary display; funerals provide not just an occasion for strengthening ties between groups, but also provide a focus for emphasising differences. In rural China Hsu (1949) noted that there is competition between clans for status regarding graveyards and temples, with it being particularly desired to have something which other clans do not, such as a special form of temple within the graveyard. Spirit banners displayed at the head of the funeral processions represent the deceased's clan, while banners may also be donated by clan members, and the number of these is indicative of the status or esteem of the dead, and also the standing of the clan (ibid.). Competition between clans or moieties may even form an explicit component of the mortuary ritual. The Laymi of Bolivia have ritual battles fought on various festivals, such as All Saints' Day, symbolising opposition between moieties (Harris 1982). For the Mandari of Sudan, on the death of a chief, the representatives of other clans and chiefdoms arrive at the dead man's homestead, and a mock battle is enacted in which the chiefs grave is defended. The symbolic battle represents the unity of the chiefdom in the face of death, and having a mock challenge, it was suggested, may also avert a real one (Buxton 1973: 139). The Mandari also provide an example of changing clan status not being reflected in mortuary treatment. The Jokari clan formerly constituted a chieftainship, but decline in numbers led to amalgamation with the Mokido, who were numerically dominant and took over the chieftainship (ibid., 132). The clan head of the Jokari, however, was still given a funeral which accorded with that of a chief, despite the clan's changed status (ibid.). In this instance the time lapse between changed status and a formerly appropriate mortuary treatment would give a misleading impression of the then current social structure. The degree of competition between clans in a particular 43

Orientation would seem to be a good, archaeologicallyvisible indicator of social affiliation, since the only other characteristic of the social persona it distinguished in Binford's sample (1971) was sex; however, it was only used in 9 out of 28 cases, where social affiliation was distinguished, so a uniform orientation need not indicate an absence of horizontal divisions. Some uses of orientation to distinguish horizontal groups would not be visible to the archaeologist, since they can relate to pre-interment phases of mortuary ritual; in Banaras, for example, certain rules of positioning on the cremation pyre are followed only by certain religious communities (Parry 1994). Different factors may influence which particular orientations are chosen. Among the Konso of Ethiopia the orientation of the body in some areas depends on the direction from which the person's ancestors are supposed to have come from, for example, the north-west or the north-east (Hallpike 1972: 157-8). Hallpike was told that he would in theory be buried facing towards England (ibid., 158). In other regions, though, it seemed that different orientation rules could be in operation - in one area people were buried facing their homes, while in another they were supposed to be buried facing west (ibid.). The Lugabara of Uganda have corpses orientated towards two sacred mountains, depending on clan (Middleton 1982). Similarly among the Hadza of Tanzania the body faces towards a particular mountain, but for people whose descent may be slightly different or mixed a different orientation may be used (Woodburn 1982). These examples are clear-cut and visible to the archaeologist, particularly when the orientation point - for example, a mountain - is quite obvious. In other instances, the method behind the orientation may be less easily detectable, and may be unrelated to the presence of different groupings within the society. On the islands off the coast of Malekula, a number

Theoretical and Quantitative Approaches to the Study of Mortuary Practice of factors influence the orientation and positioning of the body (Layard 1942). The body traditionally was buried in a squatting position but at the time Layard was writing this was changing in favour of interment in an extended position; this was supposedly because of fears that in the squatting position the head would fall forward, which was considered undesirable (ibid., 531-2). Layard considered this custom to have diffused from elsewhere in the islands, resulting in a variety of positionings in the body in areas where the two forms have inter-mixed. Choice, though, was still involved one man chose to be buried in a sitting position while his brothers intended to be buried in extended position (ibid., 533). This point also emphasises the fact that there need not be a rapid chronological transition between one form of burial characteristic and the next, because of varying degrees of conservativeness. This is not always considered when interpreting mortuary practice patterns. At the Mohr site, for example, Gruber (1971: 73) discounted the possibility that the dichotomy of burial positions into flexed and extended was due to chronology "since in one of the cases of burial intrusion the flexed burial is obviously the later and in the other it is earlier". The example given above illustrates that this should be absolutely no reason for discounting what may, in fact, be a general chronological trend muddied by conservative behaviour.

confused, resulting in the corpse accidently being placed in the grave the wrong way round. Another possibility, identified from grave inclusions, was that these represented winter burial, though it is not clear why this should have resulted in a reverse orientation (ibid., 27). In the Mariupoltype Neolithic cemeteries of the Dneiper basin, Telegin and Potekhina (1987: 105) considered a few deviant positionings to be due to accident also.

4.4.4

Spatial

distinctions

Spatial distinctions also provide a clear method of distinguishing horizontal groups, emphasising the "separateness" of these, though there may be potential confusion with vertical groups. The Gurungs of Nepal used separate clusters for clan pyres and graves, for example (Messerschmidt 1976). Moiety divisions may also find reflection in cemetery organisation. The arrangement of the graveyard for the Laymi of Bolivia reflects moiety and subdivisions of moieties within the society (Harris 1982). Traditionally among the Haida, individuals from different moieties could not be buried in the same place, and an analysis of tombstones between 1877 and 1968 revealed definite clan groupings (Blackman 1973). The use of space to mark horizontal divisions may extend to the provision of separate cemeteries - the individual patrilineages among the Lodagga have a separate cemetery for their members, for example (Goody 1962), and the five discrete burial areas at Leavenworth, O'Shea (1984: 218) suggested, could represent horizontal groupings. Spatial distinctions may exist at different stages in the mortuary practice, if there is a twostage burial procedure. For the Kaoka-speakers of Guadalcanal, clan tradition (amongst other factors) may determine where the corpse is buried - whether beneath the floor of the house, buried at sea, exposed on a rock, and so on (Hogbin 1964). At a later stage the skull is removed, cleaned and placed in a shrine, which should be that of the sub-clan, but may actually be that of the father's sub-clan (ibid.). In this example, where the skull is to be later placed in a clan shrine, the need to emphasise clan ties at the original burial may be reduced since these will be ultimately expressed by the physical relocation of the skull in the shrine. In some examples, although horizontal groupings may in theory be spatially separated, the practice can be quite different. Ucko (1969) gives the example of the Egbira of Nigeria, for which there was a colonial policy of separating different religious groupings within a cemetery; the gravediggers, however, were most concerned with finding the easiest location to construct the grave, thus blurring any such distinction.

Layard noted that the position of the body subsequently affected the orientation of the body relative to the lodge in the cemetery. If the body was squatting, it should face the lodge. If the body was extended, then it could be orientated either with the feet towards the lodge, or arranged so that it lay across the width of its burial hut (Layard 1942: 534). Since orientation was relative to the lodge, which could move, a variety of different orientations were possible, and Layard comments: "An archaeologist, coming after both Lodge and the huts over the graves had rotted away, would have difficulty in finding a common rule for the orientation of bodies which appear to be buried facing almost every conceivable direction, though such a rule, as we have seen, exists" (534). This example raises two points. Firstly, the rules used in the orientation of the body may be difficult to reconstruct archaeologically, and even apparent randomness may have a rule hidden behind. Secondly, the use of two or more different positions/orientations within a single cemetery need not always represent a horizontal differentiation, as may often be thought, even if other explanations (e.g. gender or chronological distinctions) are rejected Instead, as in this case, there may be different preferences which are unrelated to such groupings. Other factors, such as chronology or different beliefs, may produce different orientations. Ucko (1969), for example, notes how the Ashanti have a particular rule about the orientation of the body, but different interpretations of the rule results in two opposing directions being used within a single community, unconnected with horizontal differentiation. Changes in orientation may be because of alignment with external structures such as roads or buildings which have also changed (Kj12ilbye-biddle1992); without an appreciation of the chronological differentiation, different horizontal groups might be suggested. In the Birka cemetery a significant number of graves were orientated in a reverse manner to the usual orientation and, though Graslund (1980: 26-7) did not consider it probable in this instance, she noted the possibility that a reversed orientation could be due to the head and feet ends of an undifferentiated coffin being

Archaeologically, the existence of different spatial groupings within a cemetery area often suggests the existence of horizontal groupings, though, as noted, there may be confusion with status groups or chronological change. Mainfort (1985) identified several rows of burials at the Fletcher Native American site as being clan or lineagerelated, supported by ethno-historical evidence for the existence of several clan groupings in the population (558). The westernmost row was the wealthiest, and was positioned closest to the Algonquin afterworld (west), suggesting that it represented the highest ranking clan or lineage, possibly through better trading contacts with Europeans (ibid., 571-3). Mainfort suggests that identification of these rows as corporate groupings is supported by the fact that they each 44

The Horizantal Dimension in Mortuary Studies have a complete representation of sex and age groupings, though he comments himself that the population curves for most would not be normal (ibid., 571). In fact, the demographic make-up of the rows is unusual and could partly explain any differences in wealth, rather than explaining wealth in terms of status differentials. For example, row 1 wealth, compared to other groups, can be explained to some extent by the fact that it has the second highest number of individuals (18, compared to 8 individuals in row 4), and a smaller number of children than three of the other rows. These two factors inevitably will enlarge the wealth of the row, and highlight the need to consider demographic differences when drawing conclusions about the relative status of groupings in a cemetery. Mainfort also concluded that the decrease in wealth in the rows from west to east further emphasises differential status (ibid., 571-2). However, this again can be explained purely in terms of numbers Row 1 has 18 individuals, row 2 has 16, row 3 13 and row 4 has 8. The suspicion is that there may be a chronological factor at work here, particularly given Mainfort's comments about the differential distribution of grave goods between rows which do not seem to relate to status differences - for example, vermillion is not found at all in row 4 but occurs in all the others (twice as frequently in row 3 than in row 1). Mainfort also notes that female burials in the cemetery have "more horizontal differentiation symbolised" than males (ibid., 574) but is vague as to what exactly this encompasses, and is slightly contradictory in noting later that female graves "exhibit less well-structured behaviour", which could be an alternative explanation for the apparent greater horizontal differentiation.

groups were seen as representing the burial places of descent groups, supported by ethnohistorical evidence, and it is suggested that these were low ranking, and that co-resident affines were disposed of as crevice burials (ibid., 98). Tainter gives an analogous example from the South Point cemetery, where the dense central area of burials was surrounded by more scattered interments; the latter were seen as being the burials of affines. (ibid., 98-9). Regarding the main cemetery, six clusters of platforms were identified, each with a variety of platform types and hence ranks, as was to be expected with the Hawaiian status system (ibid., 102). The total number of descent groups, therefore, seemed to be nine, which conflicted with the number of residential clusters identified, four. Tainter explained this either because not all the platform clusters were used simultaneously, or certain of the residential clusters had more than one descent group (ibid., 102). The point about the clusters not all being contemporary could be troublesome, since this factor may give a misleading impression of the number of descent groups that is actually present.

4.4.5 Structural

distinctions

Ethnographically, there are a few examples of horizontal affiliation influencing the structure of the grave. In the Haida mortuary practice, noted above, the placing of a headstone on a grave apparently was not just determined by the deceased person's status, but also depended on the resources of his clan (Blackman 1973). The size of burial mounds can in some instances also be related to horizontal linkages rather than strictly vertical status. Dillehay (1990) describes an example of this with regard to the Mapuche of Chile. Here burial mounds are restricted to the most important individuals in society, such as lineage leaders, but the construction of the mound is not a single event; instead there are a number of mound capping rituals which gradually build up the size of the mound. The size of the mound consequently does not reflect the political importance of the chief, but instead relates to the length of time for which he was in office, and the number of relatives that were involved in adding to the mound; reportedly each adult relative of the deceased was supposed to add a certain amount of soil to the mound, so that the mound size in effect symbolises the size of the kin group. Again, the inter-mixing of horizontal and vertical dimensions is apparent.

Therefore, there are some problems with Mainfort's analysis of these groupings, but he does raise some interesting points and possibilities. Firstly, there is the fact that clans may be ranked relative to each other rather than necessarily being of equivalent status. It seems that the hereditary chief in Central Algonquin society was a position open to only particular clans (ibid., 558-9), though "the differences between clans was not great and may have been employed only on ceremonial occasion". This latter point opens a further perspective: the fact that there may have existed small differences in clan groupings that were exaggerated on ceremonial occasions, such as burial, giving the archaeologist a distorted viewpoint of the social structure. This is particularly relevant given Mainfort's comments about the competition between clans (ibid., 559). Competition between horizontal groupings within a particular society is clearly an important influence on the structure of the mortuary domain and how it changes through time. A further related point raised is the potential for rapid change in the relationships and symbolic interaction between groupings such as clans, in response to external factors such as economical change. The fur trade produced a new kind of social formation in which previously distinct clan villages were amalgamated into multi-clan settlements which would have "strained traditional mechanisms for conflict resolution and created a crisis in political organization and leadership" (ibid.). These points emphasise the dynamic relationships that characterise clan structures.

Occasionally, structural details may be apparent in archaeologically recovered burials that suggest horizontal groups. These tend to relate to distinct structures, such as mounds or tombs, rather than details in individual burials. Anderson Beck (1995) suggested that the use of different coloured clays in individual burial mounds in the Copena mortuary complex, along the Tennessee river, could symbolise such divisions. The Effigy Mound tradition of Wisconsin consists of mounds in animal and geometric form, which are usually interpreted as totemic symbols of corporate groups - though as Goldstein (1995) emphasised, they cannot be considered as being purely "mortuary" sites. They also appeared to relate to different natural resources, with the orientation of the mound relating to the resources controlled by particular groups. The pairing of some mounds also potentially related to relationships between horizontal groupings (ibid., 118-120).

Another archaeological analysis which dealt particularly with spatial differentiation was Tainter's (1976) analysis of the Kaloko cemetery, Hawaii. The cemetery was divided into discrete sub-areas which were interpreted as representing different corporate descent groups (ibid., 97). Three cave 45

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

The study of megalithic tombs in Britain, and other areas, often is viewed from the perspective of horizontal groupings such as clans and lineages, and as such they represent a clear structural representation of these groups. Tombs are often viewed as emphasising the communal nature of society at the expense of individual differences in status. The spatial distribution of such tombs may reflect differences in status between the descent groups represented - for example, at Los Millares in Spain the highest ranking groups, as indicated by the most prestigious grave goods, were found closer to the settlement, and were a focus for the location of other tombs (Chapman 1990). That large tombs should be a product of a clan-type social structure need not necessarily be the case. Bloch (1971: 44-5) considered the Merina of Madagascar not to be arranged into clans but rather to form groupings which he termed "demes". Bloch's account of the Merina makes it clear that the choice of tomb for burial can be very loose; there need not be any strict rules as to who should be buried where. A man can be placed in the tomb of his father, his mother or his wife, while the same options apply to women; even a grandparent's tomb may be used which does not include the parents of the deceased (ibid., 115-6). Affection for a particular person (e.g. father versus mother) seems to be a major consideration in the choice (ibid., 117). Therefore we should be wary of equating a particular tomb rigidly with a descent group such as a lineage. A further point of interest is the fact that the tombs are symbolic of a past form of social structure - deme members "consider they retain the corporateness of the past, irrespective of the much more complex reality of the present" (ibid., 72). Once again this illustrates the fact that burial practice may relate to a previous, inappropriate form of society rather than necessarily representing the situation as it is at that point in time - by being buried in these tombs with his relatives a man associates himself "with the lost society of the ancestors who lived in Malagasy times" (ibid., 112).

suggesting that there may be a differentiation here, though it is not noted whether this applies to other clan groupings as well. Occasionally other references are found to grave goods that distinguish clan, but they are fairly rare. This paucity may either be due to the fact that material inclusions were not noted or interpreted as such, or, alternatively, did not exist in the first instance, with horizontal affiliation being marked by more external means, such as ceremony involving clan members. This point is important when interpreting the "meaning" of particular burial characteristics. Archaeologists rarely consider who was actually involved in the placing of goods in the grave, and their particular motivation and, additionally, how involvement in burial may fluctuate between social groupings - for example, high status versus low status burials. There may not exist a need to symbolise clan affiliation in burial in a material sense because of other methods of distinction utilised, with the placing of grave goods being more directly linked to the family of the deceased. These other methods principally seem to be those methods indicated by Binford's sample: location, orientation and treatment of the body. Additionally, ceremonial involvement of clan members, as noted above, seems to be common, and in itself could form a very major and prominent part of the funerary proceedings, yet being almost invisible to the archaeologist. One particular archaeological situation, which may involve materially symbolising horizontal affiliation, is the presence of particular animal bones in megalithic tombs. These are often interpreted as being some kind of clan totems - for example, the seven or more oxen skulls found at Bole's barrow, or the barrow at Beckhampton from which human bones were not recovered, but which had three ox skulls arranged along its axis (Burl 1981: 54). Orkneys tombs with different species present are also interpreted as having clan totems (Hutton 1991: 63; Burl 1981) - for example, the skulls of 24 dogs were recovered from Cuween (Burl 1981: 115). There are dangers in identifying animal bones as totems of clan-groupings, though. These remains may be here for various other reasons - as remains of a funeral feast, as symbols of control over agriculture or the natural world in general (cf. Hodder 1990), or in some other ritual capacity. Munro (1962: 23), for example, describes how the Ainu of Japan used a variety of different animal skulls for divination or as "spirit protectors", which may be placed on the wall of a house, or even carried on journeys for protection; there does not appear to be any distinction in these between kin groupings.

4.4.6 Treatment of the Corpse Treatment of the body is another distinction that may define clans/lineages, but references to this in the ethnographic literature tend to be more scanty, at least in terms of archaeologically-detectable treatments. Among the Reddis of India, the form of the burial varies depending upon clan. Thus in one particular village three clans cremate the dead, while burial is more customary for the other clans, though clearly other factors influence the choice, such as individual religious beliefs (von Ffuer-Haimendorf and von FfuerHaimendorf 1945). In some areas of Gudalcanal whether the deceased is inhumed or cremated will depend on clan tradition (Hogbin 1964). Other aspects such as preparation of the body, tattooing and styling of hair are more commonly found in the literature, but are generally of low or zero visibility to the archaeologist, as O'Shea (1984) noted. Differences in treatment of the corpse may also relate to religious persuasions (see section 4.5.3).

4.4.7

Artefactual

4.5 Other types of horizontal groups 4.5.1

Introduction

Most of the examples so far have related to horizontal groupings in the sense of those which are kin-based lineages, clans and moieties. References to non-kin based groupings being distinguished in burial treatment are less common, though such groups are often described in general discussions by ethnographers. There may be inter-linkages between horizontal and vertical groupings, in the sense that progression through the ranks of a society may be dependent to some degree on wealth. In the Seniang region of Malekula, New Hebrides, the mortuary treatment of males relates in a large part to the grades that may have been achieved in societies during life. These grades affect the

inclusions

Mentions of material inclusions in the grave to mark groupings such as clans are scarce in the ethnographic literature, though clan resources are often displayed during funerals (see section 4.4.2). Ashton (1967: 106) notes that among the Tlokoa clan of the Basuto, South Africa, a few beads "proper to the deceased's clan" are placed in the grave, 46

The Horizantal Dimension in Mortuary Studies funeral procedure in various ways. The degree of elaboration of the bier on which the deceased is carried, the elaboration of his ramabaramp effigy, the ornaments placed on the corpse, the colour and designs painted on the body, the planting of particular shrubs and the ritual destruction of other trees, and the particular ceremonies followed all relate to the particular grades achieved by the dead man in the Nimangki and Nalawan societies (Deacon 1934: 519). The description given of the burial of one prestigious old man gives an idea of the kind of impact that these societies would have on material inclusions in burial:

notes the possibility of "post-mortem" sodalities, defined, for example, by a particular cause of death, such as drowning, or in war (ibid., 41). This seems to be using the word "sodality" in a rather loose sense. He noted that the distinction between inhumation and cremation cuts across the kinship zones he identifies in the cemetery and could possibly have an explanation in terms of some non-kinship division - such as a hunting group, though he notes that the presence of females in such a group then becomes difficult to explain; in the end he states a preference for some kin-based division (ibid., 46). Another suggested non-kin sodality was that represented by a small number of deviantly-orientated burials (not explicable by other patternings such as sex), only one member of which he suggests was alive at one time. A priest or "warden" is suggested as a possibility, but without any substantiation (ibid., 46).

On both arms they put the boars' tusk bracelets numbering twenty or more, which he had acquired when purchasing entrance to numerous Nimangki ranks. To these were added the armlets of turtle-shell, which are the insignia of anyone who has attained the rank of Amel Ndarlamp or higher in the Nimangki; and on his head was placed a spider's web head-dress, another sign of high Nimangki status. The netel muluwun, the mark of men of Muluwun rank in the Nimangki, they bound round his head and fastened another to his belt. In his hair were placed the hawk feathers of his Nimangki grades, and also hibiscus flowers (singgeul) which are the symbol for the big curved tusker boars (imap grade) which he killed as he ascended in the Nimangki. Below the knee they bound the leg-band neliwis, the badge of the Nimangki which bears this name; and over the shoulders, crossing on the chest, they fastened two mats, nimban mbarmbep, the right to which he had purchased on entering Nevet Nambar. (ibid., 521).

Another form of grouping that may be present are different community groupings. Goldstein (1976), in the analysis of Moss and Schild Mississippian sites, suggested that at Schild Knoll B there may have been two simultaneous interment programmes, representing different community groupings. The non-chamel burials may have been an out-lying community that were brought to this site for burial, but who did not qualify for chamel processing. This idea is supported by the fact that there is no single site nearby that could have supplied the mortuary population. In addition, at Moss four rows of burials were identified and interpreted as representing family or extended family groupings; while three of the rows were of mixed sex and age, the most northernmost was all male. Goldstein thought that the fact there were two sets of rows - in each set the rows were parallel to each other, but this did not apply between the sets - may have indicated the presence of two sets of descent units. Binford (1972: 411) also suggested community groupings at Galley Pond Mound, as there were two main orientations present - north-south and east-west. The orientation of local houses and mortuary structure was north-south, so this orientation was taken as representing a local community, with the alternative orientation representing either another community or a different segment of the local community. Two moieties were presumed to be symbolised (ibid.).

This example, although particularly complex and clearly linked to wealth/status, demonstrates the potential importance of the symbolism of such societies - as separated from rank grades as such, in an ascribed sense - in the archaeologically-recoverable elements of burial. On rare occasions groupings are identified archaeologically that possibly are not kin-based but relate to some sort of sodalities within the society. Milisauskas (1978: 167) talks of certain grave goods in middle Neolithic European burials as possibly "the symbols of office held in a sodality or voluntary tribal association", though does not give particular examples. Whalen (1983) identified a minority group (possibly male) of flexed burials at the Tomaltepec early Formative site which he commented could be "a kinship unit, a suprahousehold group or association of another sort, or an unrelated set of individuals whose burial position recognizes some common experience or characteristic" (34-5), but the data was inadequate to decide which of these was appropriate. At Oleneostrovski Mesolithic cemetery the presence of effigy figures in some graves, of both sexes and various ages, was interpreted as possibly an inherited ritual position (O'Shea and Zvelebil 1985: 19), but whether or not this could be considered some kind of horizontal grouping as such is unclear. O'Shea and Zvelebil also noted two subgroupings within the cemetery, defined by separate areas and effigy forms, but were unsure whether this dichotomy represented separate clans, lineages or bands (ibid., 16-17).

Some societies may be formed for and directly involved in funerary practices. Some Cantonese societies have old people societies, the members of which have a obligation to attend the funerals of other members (Watson 1982). The obligation is passed on to descendants. The Merina also have tomb associations that are responsible for the upkeep of their family tomb (Bloch 1971) and Brandes (1981) observed how subscription to burial insurance companies, with improved wages, had allowed more conspicuous funerals for a wider range of people in a small town in Andalusia. Burial clubs were also common in Victorian Britain (Morley 1971). Thus, apart from the fact that mortuary ritual may be a reflection of the existence of horizontal groupings, some of these groupings may owe their existence in the first place to the need for prominent display in funeral.

4.5.2 Ethnic groups

Van de Velde (1979), in the analysis of the Elsloo Linear Pottery cemetery, notes the possibility of pan-tribal sodalities being present, possibly identified by specific components in a principal components analysis; he also

A further kind of grouping that may be symbolically distinguished in burial - again it is not clear if this should really be called a horizontal grouping as such, especially when status differences may be involved - are ethnic groups. 47

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

Anderson Beck (1995: 168) defined the term "ethnic group" as referring to "the socio-ideological network among affiliated villages in terms of the local group and in terms of regional systems", and considered it to represent part of an individual's social persona. The difficulties of detecting ethnic groups in the archaeological record has been commented upon by Palumbo (1987), who certainly seemed to consider ethnic groups to represent "horizontal differentiation"; mortuary patterning in Jericho in the EB IV period is interpreted to some degree in ethnic terms, and the conclusion formed that mortuary differentiation is largely horizontal rather than vertical (ibid., 47, 56).

4.5.3

Religious

groups

Religious groupings, though perhaps not technically "horizontal groups" in the normal sense of the term, nonethe-less are worth considering within this context for their impact upon funerary procedures. Early studies of burial concentrated particularly on religious aspects (e.g. Frazer 1934), but social, economic and political factors are now the main areas of interest. Prior (1989: 163) has commented upon this avoidance of discussion of religion in relation to modem analyses of burial practice: "It is, perhaps, a reflection on the secularisation of western culture that most 20th-century analyses of the funeral have concentrated more on the expression of social than of religious meaning". Prior emphasises the fact that the ritual within a funeral does have religious significance (ibid., 164-5). Archaeologically, the same situation exists. Preoccupation with social and political aspects of burial has to a large extent resulted in a neglect of religious beliefs of the people concerned; in Graslund's words, it has "diverted our attention from the study of graves and mortuary rites in their primary function, that is, as the medium of transition of the deceased to an afterlife existence" (Graslund 1994: 15). The placing of objects with the corpse does not simply fulfill any notions of duty-status relationships, but may directly relate to particular beliefs concerning the fate of the deceased, and potential influence (positive or negative) upon the living. The fundamental provision of grave goods at all will often relate not to social or economic factors, but to religious beliefs; in societies such as our own where there is no concept of the soul remaining in the body until it is decayed, provision of goods is usually limited, since the spirit will have departed at the point of death (ibid., 19). Different items placed in the grave can have mystical or cosmological associations - as can be the case with metal objects, because of the process by which they are manufactured (Kraig 1978). Exclusion of metal objects from burial because of such associations can be seen in a variety of societies, while in other cases metal objects may be included because of ritual associations; in China, for example, copper was traditionally associated with death, and metal objects in the grave forged a connection with the tigerGod, which could help protect the deceased (Mackenzie 1986: 39, 233).

Harke (1991) has particularly drawn attention to the presence of ethnic divisions in archaeological burial data, examining the significance of early Anglo-Saxon weapon burials, usually interpreted as "warrior burials". The fact that these weapons were found with extremes of age, and with individuals suffering from diseases that would have prevented them using weapons (ibid.,153) suggested other explanations. Harke noted that those buried with weapons were 2-5 cm taller than the others, which could suggest an elite, but incidents of disease seemed to be equivalent, and the height difference seemed to disappear in the seventh century, even though the hierarchical structure of society remained (ibid., 154). The best explanation seemed to be to do with ethnic origins - Anglo-Saxon men averaged 4 cm taller than Roman-Britons and Pre-Roman Celts, and it seemed likely that weapons burials represented families of Germanic descent, with greater wealth (ibid., 154-5). Thus the weapon burial was a method of marking ethnic affiliation in a society composed of a number of ethnic groupings (ibid., 155). In the seventh century, when the height difference disappeared, it seems that weapons burial was no longer a means of symbolising ethnic differences (ibid., 163-4), emphasising the changing roles that symbols may play through time. Other researchers have also suggested the presence of different ethnic groupings within a single cemetery- Skomal (1980), for example, suggested that at Tiszapolgar-Basatanya the presence of a small number of graves atypically orientated west-east represented members of another ethnic group maintaining their individual identity. Another form of differentiation, more vaguely defined than "ethnic group", which may influence burial practice, is the general "orientation" of beliefs an individual may have. The Merina, for example, have what Bloch (1971) calls two sets of principles: the way of the Malagasy (past) and that of the Vazaha (foreigners) (ibid., 9). Variation in dress amongst the Merina may relate to the particular set of principles the person is aligning themselves with (ibid., 9-10), and this in a sense could represent a kind of dichotomy of groupings which could possibly be recognised archaeologically; whether these could be called "horizontal" groupings as such is debatable, but it does emphasise the variety of factors that may result in differentiation in the archaeological record, and the looseness of the term "horizontal differentiation". Opposing sets of beliefs within a society could technically be included in the term since they may potentially cut across vertical divisions, defining invisible groupings that do not reflect status differences. Dubisch (1989), in a study of contemporary Greek cemeteries on Tinos, noted a similar phenomenon; the city cemetery studied had two representations of "Greekness", Hellenic and Romeic, the former reflecting Western interest in the Ancient Greeks.

It is particularly important to consider that grave goods may not be personal possessions but may in some instances be intended as offerings to deities or spirits (Kraig 1978: 16). Merrifield (1987) comments on the fact that ornaments in some graves seem as though they would have been difficult to wear, and may have been offerings to deities. Certain types of paired grave goods, such as bronze spoons in some Iron Age burials, were also interpreted as offerings to a "lord and lady" of the underworld (ibid., 67). Additionally, some goods may be included to facilitate the journey of the deceased to the other world. James (1962: 130) suggests, for example, that the inclusion of cowrie shells in prehistoric graves was for "promoting and facilitating birth and revivifying the dead in a process of rebirth". Similarly, the placing of life-size figures in some Egyptian tombs was "to ensure the continuation of the existence of the occupant should the body disintegrate or become unrecognizable" (ibid., 163). BlochSmith (1992) notes that the certain types of jewellery placed in Judahite graves seemed to function as protection for the dead, rather than items of personal adornment. Grave goods also may function in maintaining the well-being of the

48

The Horizantal Dimension in Mortuary Studies living. Female pillar figurines in Judahite burials are interpreted as attempts to bring luck to child-bearing women in the surviving community (Bloch-Smith 1992: 98). Fear of the dead may also be an important consideration. The Tobelo of the Northern Moluccas place coins with the body, relating to the belief that death results from a separation of the deceased's body and his "image". Ritual is used to expel the image from society, and as a consequence the deceased may search for another image, possibly causing a second death (Platenkamp 1992: 79-80). To prevent this, coins are placed on the eyes, breast and hands to provide a substitute image (ibid.).

Even the apparently straight-forward detection of Christian versus Pagan burials is more difficult than might be thought (Woodward 1992: 96); an east-west orientation may equally be pagan, and some Christian burials may have other orientations, such as north-south (Philpott 1991: 239). Philpott (1991: 227) queried whether Christians in fourth century Britain used a distinctive rite to mark their religion, though he suggests that the idea of resurrection may have helped promote the use of coffins and stone-lining (ibid., 238). Religious affiliation may only be nominal, reflecting more political alignment; thus Geake (1992: 91) suggested that seventh-eighth century Anglo-Saxon burials lacking grave goods may have been demonstrating affiliation with Christianity, and west/southern Europe, while more pagan type burials emphasised independence, or links with northern Europe. Graslund (1980: 85) noted that in northern Europe in the Viking age the presence of both pagan and Christian goods in the one grave may reflect a fear of breaking with tradition or possibly the existence of different religious beliefs within the one family; also, it could reflect trading links with both the Christian and Pagan worlds. The "use" of religion is clearly an important aspect of burial ritual, rather than just being religion for religion's sake. Neolithic long mounds in western Europe were a focus of ritual that allowed leaders to use "higher authorities" to give legitimacy to their leadership (Hodder 1984: 64), and there are numerous ethnographic examples demonstrating how religion and ritual is controlled within the society by elders to increase their power and prestige. Therefore, religion is a complex area when examining mortuary practices, and may be inter-twined with other social aspects, making it sometimes difficult to analyse at "face value".

The distinctions used to separate other types of horizontal groups may also be apply to religious groupings. There may be different treatments of the body, even in quite subtle ways. Thus contemporary Protestant undertakers would be more concerned with embalming and application of cosmetics than would Catholic undertakers (Prior 1989: 160). The former aversion of the Catholic religion to cremation, as contrary to the idea of resurrection, is another example of bodily treatment relating to religious belief; contradictions may exist here, however - Morley (1971) notes an oddity in that cremation was popular in Italy for a long time, despite its Catholicism, probably because of association with Roman practice. The structure of the grave in some ethnographic studies is often seen as being a consequence of religious influences - the presence of a side-chamber in some graves of the Bantu of North Kavirondo is suggested as being a Muslem influence (Wagner 1949). Spatial differentiation, into separate areas or separate areas of cemeteries, also can be clearly seen in contemporary society, though Ucko (1969) has noted the problems sometimes inherent in attempting to differentiate different religious groupings in one cemetery.

4.6

As with other horizontal groupings, conflict and competition may exist between religious groups in attempts to have funerary procedure follow their own particular interpretations. Thus in some areas of Kenya Christians may attempt to enforce Christian burial by actively domineering a funeral, in terms of numbers, and taking up prominent positions in the procession (D. Parkin 1992: 20-21). In a particular area this competition (resulting from members of a dispersed family having different religions) could sometimes result in alternative funerals being held (ibid., 21-2). Huntington and Metcalf (1979) note that among the Nyakyusa of Tanzania the introduction of Christianity was seen as a threat to the established religion, which was based on the fear of spirits and ensured a careful following of ritual; the reassurance that Christianity supplied, in its concept of heaven, resulted in less care in ritual practice. There may also exist contradictions between the ideal of particular religious beliefs, and the reality of burial practice. Thus, in the Ciskei region of south Africa some Christian burials incorporated what Raum and de Jager (1972: 204) called "magical notions", such as the inclusion of the deceased's church ticket in the coffin, and in the same area non-Christians could be buried by Christians; distinction is made in that the ritual may be performed by church elders (without a minster necessarily in attendance) who do not wear the usual ceremonial clothing, and a cross is not placed on the coffin (ibid., 203).

Conclusions

In conclusion, the ethnographic examples demonstrate that horizontal affiliations can be an important factor in funerary procedures, particularly in a ritual sense. A number of points can be made: 1) The detection of horizontally-separated groups from mortuary data can be potentially troublesome. For a start, the category is difficult to separate from vertically-differentiated groups, both in theoretical and practical terms. The term "horizontal group" itself is vague and potentially incorporates may different things - kin-groups, societies, religious differences, different communities, different orientations (e.g. "modem" versus "traditional") and so on. All these may be signalled in similar ways, so the archaeologist should be wary of jumping to conclusions, perhaps concluding that "clans" or "lineages" are present when other groupings could explain the patterning observed. 2) Competition between horizontal groups, and how they choose to compete, are crucial factors in determining elements of the mortuary symbolism. In some instances ritual demonstration of solidarity and group power may be sufficient in the funerary procedure, negating the need for more materially-recoverable symbolisms. This is particularly so since the ritual itself may directly involve group members, extending the funeral beyond the boundaries of the immediate family. Ritual is very visible, all-embracing method of making public statements of the sort that may be desired by horizontal groupings. Additionally, the display of clan relics, weapons and general resources may be a substitute for the placing of such items in the grave, and has

Archaeological detection of religious groupings can be difficult, and such groupings may be intertwined, or indistinguishable from the kind of groups mentioned above. 49

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

the advantages of focussing attention on the continuity and importance of the group, as opposed to the individual - by placing of items in the grave the individual, not the group, becomes emphasised and the items are lost from group ownership. Furthermore, the use of orientation, positioning and spatial differentiation can clearly enough symbolise such horizontal affiliations without the need for redundant symbolism via grave inclusions.

demonstrate that this practical separation is difficult, making interpretations expressed purely in "vertical" terms very dubious.

3) Competition between horizontal groups may, in some instances, result in increased emphasis on the physical side of burial, and particularly in the construction of imposing burial structures - as in the case of megalithic tombs, though in this case the burial structure itself provides a continuing focus for lineage/clan ceremony that is less applicable to individual burial within a cemetery. However, even with individual burial, the ethnography makes it clear that the over-all wealth placed with an individual of a particular group relates, to some extent, to the importance of that group, so the idea that horizontal groups should be symbolised by "neutral" grave goods is over-simplistic; while totemic items in themselves may be of limited value, the general wealth of the grave contents can express status differences between horizontal groups. 4) Religious beliefs and affiliations can have an important impact upon archaeologically-visible burial, and could be misinterpreted in terms of ideological control by an "elite" group. A decline in grave goods could relate to a change in belief, rather than economic changes or social manipulation, and apparent wealth differences within a cemetery could relate to the existence of different beliefs within the society, rather than social differences. Competition between groups with different beliefs may also find expression in burial ritual, and in some instances grave structure may represent a compromise between opposing beliefs. 5) The connection between horizontal and vertical complexity is not demonstrated archaeologically. Ethnographically there may be complex grade systems in societies in relatively "uncomplex" societies, and there is a clear emphasis among horizontal groupings of maintaining distinctions in mortuary practice. The existence of several such groups in a society, with each perhaps using different symbolisms, could give the impression of a "complex" society that may be unrelated to the degree of vertical differentiation. Therefore, the archaeological "neglect" of horizontal divisions in mortuary practice is unwarranted, from both theoretical and practical perspectives. In a theoretical sense, these groupings are important because of the impact they may have on burial, particularly in terms of competition for status. Previous theoretical stances with regard to this have tended to view horizontal symbolism in burial as "passive", and hence of not particular concern, where as the ethnographic evidence makes it clear that this is not the case. In addition, horizontal differentiation potentially covers a wide variety of groupings and "beliefs" which should be considered when interpreting burial data, rather than focusing interpretations upon vertical groupings and associated ideologies. In a practical sense, even if the focus of interest is vertical differentiation, there is a clear need to dissect "vertical" and "horizontal" symbolisms, if this is at all possible, by being aware of the kind of archaeological correlates that both have. The examples given earlier, where there was confusion as to whether particular symbolisms represented vertical or horizontal distinctions, 50

Chapter 5 The Vertical Dimension in Mortuary Studies 5.1 Introduction the physical burial is limited. This attitude is interlinked with religious beliefs (see section 4.5.3). The physical burial itself is not the only formal aspect of a funeral, and the careful regulation of pre-burial ritual is widely observable throughout the world, and through history; in ancient Greece, for example, there were clearly defined stages prior to interment (the prothesis, or laying out of the body, and the ekphora, the bringing of the body to the place of burial) (Garland 1985: 21).

The analysis of vertical status relationships from burial data is one of the areas of mortuary analysis that has received the most consistent attention, to the extent that other social dimensions have, in some situations, almost been neglected. Most of the archaeological theory discussed in chapter 1, for example, specifically relates to the detection (or misrepresentation) of vertical status relations in burial data. This narrow focus has resulted in an under-development of theory regarding other social dimensions, while consensus about how vertical differentiation should be detected is not always apparent. The separate treatment of vertical differentiation as a dimension also ignores the fact that it may be cross-cut by other dimensions, such as age, sex, "wealth" (as distinct from rank), and horizontal groupings, all of which interact and may in the analysis be difficult to segregate. The notion that different ranks within a society form neat, self-contained units, differing only in the materialisation of their status differences is overly simplistic; these groupings will not always be neatly defined, and may differ not only in material ways but also in attitudes and strategies regarding death and burial.

Clearly an emphasis on non-burial distinctions can result in the burial itself being relatively simple, and no longer the main focus of attention. Gittings (1984: 178-9), for example, noted how in aristocratic burials of the Medieval period the principal mourners often left the church before the actual interment of the body. The public persona of the aristocrat was the main interest, and was recognised in the funeral ceremonies, with the private persona and the burial of the corpse itself subsequently of little importance (ibid.). Also in this period, the use of royal effigies in many instances superceded the significance of the corpse itself. The effigy could be dressed in coronation robes, while the body of the king or queen could be dressed in much simpler garments (Finucane 1980). The effigy clearly became a substitute for the real person, and subsequently the latter received less attention than might otherwise have been appropriate. This demonstrates that the visibility of the corpse is of importance; if the corpse is not seen, there is little point in having elaborate clothing or material inclusions, as a demonstration of status. Attitudes to death, as always, will also be influential in this respect; Graslund (1980: 15) notes how in the Middle Ages, prior to the Reformation, the conceptualisation of death as a form of sleep resulted in the dead being buried naked, while post-Reformation, the dead were buried in their best clothing.

Archaeologically, ranks represented in burial data are usually sought in terms of notable differences in wealth (or "energy expenditure", if other mortuary features are present), or through the occurrence of symbols which can be interpreted as representing ranks. Demographic factors are also important, as noted in chapter 2 (e.g. the presence of wealthy child burials). These approaches owe much to parallels with ethnographic societies, though often this evidence seems to be used selectively, to conform with particular theories. In general the ethnographic record is inadequate as regards full, comprehensive descriptions of mortuary procedures for a particular society, except where this has been a specific aspect of study (e.g. Bloch 1971). Ethnographic descriptions are useful, though, in drawing attention to the variety of means of marking rank distinctions in different societies, together with the types of social processes involved, which are often more complex than commonly accounted for in archaeological theory.

5.2 Ceremonial/ritual

Ethnographically, various ceremonial activities may emphasise rank differences, both before and after burial. These encompass a number of distinctions, such as the laying out of the body, the particular form of ritual, the distribution of gifts/food, and so on. Some typical examples are:

distinctions

1) The use of an "economy of ritual" (Metcalf 1981), in which different items have the same ritual significance, while also marking out differences in wealth. The Mashona of former Rhodesia, after burial, kill either an ox or a goat for the mourners, depending on the wealth of the family (Gelfand 1956: 244), and the Pedi of the Transvaal kill a black bull at the burial of a chief, or a goat for poorer people or children (Monnig 1967: 139).

Ceremony and ritual form a large and important category of funerary distinction regarding status or wealth, clearly observable ethnographically, but practically invisible to the archaeologist. In fact, the concentration on the ceremonial aspects of burial seen in the ethnographic literature, although possibly reflecting the interest or bias of the anthropologist, also emphasises the importance of non-material methods of signalling status. Brown (1995: 15-16) emphasises this point, when he comments that " ...the total funeral rite, not the physical disposal of the dead, is the appropriate frame of reference for generalization. Observed regularities apply to this rite and not to the component visible archaeologically as the physical disposal of the corpse". Many societies reinforce social differences through the ceremonies associated with burial, and to a great extent this is true of contemporary Western society, where material symbolisation of status in

2) Goods are commonly given away at funerals as a demonstration of status; for example the distribution of grave goods as prizes awarded during funeral games in Ancient Greece (Morris 1992: 107). The Grand Valley Dani of New Guinea mark status at funerals though the distribution of gifts, such as shell bands; a low status old man may have only four shell bands, while a young warrior killed in battle may have as many as forty 51

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

(Hogbin 1964). Finger mutilation of the deceased's relatives may be another method of recognising status, and is in fact considered to be equivalent to normal funeral goods. In Hawaii bodily mutilation by relatives is limited to chiefly funerals (Sahlins 1958: 22). Destruction of property is another highly-visible method of material consumption. In Samoa, the death of a chief may be marked by the killing of his herds, the destruction of canoes and so on (Williamson 1933: 240-1), while among the Dahome, the death of a king may be marked by the smashing of furniture in his house (Wood 1868). A more Western example would be the breaking of staves of office at royal funerals.

usually only available to the wealthy (Roth 1896:162-3). The Kavirondo Bantu used delay in burial to reflect status; infants could be buried within hours, an ordinary person on the day after death, an elder on the second or third day, and a clan-head up until the fourth day. A further distinction was the time of burial, with higher-status individuals buried in the late afternoon, instead of the morning (Wagner 1949). This latter method of ceremonial distinction is similar to the use of nocturnal burial by the aristocracy of eighteenth century England, though, inevitably, status distinction was only one of a variety of reasons for the adoption of this mortuary practice; Gittings (1984:191), for example, emphasised the fact that nocturnal burial allowed a more personal, and less political form of burial.

3) There may be subtle ceremonial distinctions that separate individuals of different status. Among the Lambas, a chief receives similar burial to ordinary people, but the funeral ceremony contains a number of ritual differences: for example, the chiefs body is placed in the burial hut in a stretched position, as opposed to the normal trussing of the body (Doke 1931). One of the funeral ceremonies of the Grand Valley Dani, performed to scare off ghosts, also has subtle distinctions; for an important man this ritual may involve shooting an arrow into a bundle of grass, while for less important individuals the bundle of grass may be stabbed or hit with a spear or club (Hogbin 1964).

Surprisingly, long periods of mourning are not automatically correlated with high status. A chief of the Dinka is distinguished by having a shorter period of mourning than for a normal individual; the typical mourning period is a year, while a chief is mourned only for a month (Deng 1972). The function of the mourning period will vary from society to society, and long periods may not necessarily relate to respect, but may be tied to other attitudes; thus in India the Brahmins are considered to be purer, and the pollution of death can therefore be more rapidly removed, resulting in shorter, simpler burial rituals (Bayly 1980: 157-8). Lower castes could attempt to raise their status by emulation of these shorter rituals (ibid.). Among the Berawan, burying an individual rapidly also correlates with status, since only the wealthiest would have sufficient stocks of food for a good funeral ceremony (Huntington and Metcalf 1979: 135). This particular aspect may result in ambiguity in the reasons why bodies are kept for particular periods of time before burial. Among the Tanala, the disposal of a king could be differentiated by keeping the body until decomposed, so that only the bones are placed directly in the clan tomb; however, this distinction could be confused with the temporary burial of individuals whose families had insufficient resources for a good funeral ceremony (Linton 1933). In this latter case, when sufficient food had been accumulated, the funeral procedure could be observed and the individual (now decomposed to bones) could be placed in the tomb (ibid.). These two examples - the king and the poor individual clearly represent status extremes but their mortuary treatments, though of a different nature and with different intentions, result in the same state of the deceased suggestive of additional energy expenditure - possibly distinguishing them archaeologically from other individuals, without identifying their polar statuses.

4) Ritual re-enactment of the roles of the dead person, indicative of the esteem in which they were held, is a prominent element in many mortuary rituals. The Bantu of north Kavirondo, near Lake Victoria, have ceremonial cattle drives acknowledging the importance of certain men, together with mock battles performed by age-mates, the scale of which reflects the standing of the deceased individual (Wagner 1949). The Mandari of Sudan, at the funeral of a chief, stage a symbolic defence of the homestead, representing the vulnerability of the chiefdom, if the chief was a particularly good leader (Buxton 1973: 134). 5) The process of bringing the body to the burial site also contains status distinctions, either in the method by which the corpse is brought - for example, the carrying of a chiefs body in a canoe (Doke 1931) - or by having a number of formal stops on the way to the cemetery (e.g. Reichel-Dolmatoff and Reichel-Dolmatoff 1961: 385). The above represent only some commonly-encountered ceremonial means of symbolising status following death. The length of time for which the body was placed on display, and the period of mourning, provide other means of distinction. Keeping the corpse of important individuals for prolonged periods relates not just to status, but may also be politically motivated. In Thailand, the cremation of a 14 / MALE /AGE> 14 / AGE> 14/ AGE> 14/ MALE /AGE< 15 / FEMALE /AGE< 15 / AGE 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /SHAMAN I MALE /SHAMAN I WEALTH= 1 / AGE>0 I FEMALE IAGE> 39 I MALE /AGE> 39 / CLAN= 1 / CLAN=2/

************************************************************************ TABLE 7.3 Summary of rule occurrence in Model IA

************************************************************************

Rule 1 Rule 2 Rule 3

Artefact

Number

Percentage

SCRAPER

71 burials 54 burials 125 burials

( 35.5 %) ( 27 %) ( 62.5 %)

BLADE PLAINPOT

157

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************ TABLE 7.3 (Continued)

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRlNG PENDANTA PENDANTB

59 burials 39 burials 36 burials 14 burials 17 burials 22 burials 21 burials 33 burials 21 burials 21 burials 17 burials 17 burials 3 burials 3 burials 38 burials 102 burials 49 burials 31 burials 114 burials 86 burials

( 29.5 %) ( 19.5 %) ( 18 %) ( 7 %) ( 8.5 %) ( 11 %) ( 10.5 %) ( 16.5 %) ( 10.5 %) ( 10.5 %) ( 8.5 %) ( 8.5 %) ( 1.5 %) ( 1.5 %) ( 19 %) ( 51 %) ( 24.5 %) ( 15.5 %) ( 57 %) ( 43 %)

Total burials with artefacts: 200

************************************************************************************** TABLE 7 .4

Rules for artefact distribution in Model 2A

**************************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Artefact

Probability

Social identities

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRlNG TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRlNG PENDANTA PENDANTB CHARCOAL

100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob

RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK=

1/ 1/ 1 /MALE /AGE> 14 / 1 /FEMALE /AGE> 14 / 1 /AGE> 14 /MALE/WEALTH= 1 / 1 /WEALTH= 1 / 1 /AGE> 14 /MALE /WEALTH= 2 / 1 /MALE /WEALTH= 1 / 1 /MALE / 1 /MALE /WEALTH= 1 / 1/ 1 /WEALTH= 2 / 1 /FEMALE /WEALTH= 1 /AGE> 14 / 1 /FEMALE /WEALTH= 1 / 1 /FEMALE /AGE> 14 / 1 /MALE/ AGE> 14 /LEADER/ 1 /MALE/AGE> 14 /LEADER/ 1 /MALE/ AGE> 14 /LEADER/ RANK= 2 /FEMALE /AGE> 14 / RANK= 2 /MALE /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /MALE /AGE< 15 / RANK= 2 /FEMALE /AGE< 15 / RANK= 2 /AGE< 15 /WEALTH= 2 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 39 I RANK= 2 /MALE /AGE> 39 / CLAN= 1 / CLAN= 2/ AGE>0 I

************************************************************************ TABLE 7.5 Summary of rule occurrence in Model 2A

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule

1 2 3 4 5

OCHRE DECPOT JUG CUP COPPAXE

53 burials 53 burials 15 burials 19 burials 6 burials

( 26.5 %) ( 26.5 %) ( 7 .5 %) ( 9.5 %) ( 3 %)

158

Appendix 1 ************************************************************************** TABLE 7.5 (Continued)

**************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Artefact

Number

Percentage

CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

19 burials 9 burials 10 burials 24 burials 10 burials 53 burials 34 burials 7 burials 9 burials 19 burials 2 burials 2 burials 2 burials 46 burials 54 burials 100 burials 46 burials 12 burials 35 burials 10 burials 15 burials 24 burials 14 burials 22 burials 14 burials 14 burials 15 burials 15 burials 26 burials 32 burials 99 burials 101 burials 105 burials

( 9.5 %) ( 4.5 %) ( 5 %) ( 12 %) ( 5 %) ( 26.5 %) ( 17 %) ( 3.5 %) ( 4.5 %) ( 9.5 %) ( 1 %) ( 1 %) ( 1 %) ( 23 %) ( 27 %) ( 50 %) ( 23 %) ( 6 %) ( 17.5 %) ( 5 %) ( 7.5 %) ( 12 %) ( 7 %) ( 11 %) ( 7 %) ( 7 %) ( 7.5 %) ( 7.5 %) ( 13 %) ( 16 %) ( 49.5 %) ( 50.5 %) ( 52.5 %)

Total burials with artefacts: 200

************************************************************************************** TABLE 7 .6

Rules for artefact distribution in Model 3A

************************************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

Probability 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob

Social identities RANK= 1 / RANK= 1 / RANK= 1 /MALE /AGE> 14 / RANK= 1 /FEMALE/ AGE> 14 / RANK= 1 /AGE> 14 /MALE /WEALTH= 1 / RANK= 1 /WEAL TH= 1 / RANK= 1 /AGE> 14 /MALE /WEALTH= 2 / RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 /MALE/ RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 / RANK= 1 /WEAL TH= 2 / RANK= 1 /FEMALE /WEALTH= 1 /AGE> 14 / RANK= 1 /FEMALE /WEAL TH= 1 / RANK= 1 /FEMALE /AGE> 14 / RANK= 1 /MALE /AGE> 14 /LEADER/ RANK= 1 /MALE /AGE> 14 /LEADER/ RANK= 1 /MALE /AGE> 14 /LEADER/ FEMALE /AGE> 14 / MALE /AGE> 14 / AGE>14/ AGE>14/ MALE /AGE< 15 / FEMALE /AGE< 15 / RANK= 2 /AGE< 15 /WEALTH= 2 / RANK= 1 /AGE< 15 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / FEMALE I AGE> 39 I MALE /AGE> 39 / CLAN= 1 / CLAN=2/ AGE>0 I

159

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************ TABLE 7 .7 Summary of rule occurrence in Model 3A

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRlNG TUSK 11 COPPERPN 12 CPBEAD 13 CPSPIRAL 14 CPBRCLT 15 COPPAWL 16 MACE 17 MANDIBLE 18 FIGURINE 19 SCRAPER 20 BLADE 21 PLAINPOT 22 BONEPIN 23 STBEAD 24 SHBEAD 25 BOWL 26 BOWL 27 ARROW 28 ARROW 29 BONEAWL 30 BONEAWL 31 SHBRCLT 32 TEETH 33 STONEAXE 34 ANIMAL 35 SHELLS 36 BNRlNG 37 PENDANTA 38 PENDANTB 39 CHARCOAL

53 burials 53 burials 15 burials 19 burials 6 burials 19 burials 9 burials 10 burials 24 burials 10 burials 53 burials 34 burials 7 burials 9 burials 19 burials 2 burials 2 burials 2 burials 65 burials 69 burials 134 burials 66 burials 21 burials 45 burials 10 burials 19 burials 21 burials 33 burials 21 burials 34 burials 21 burials 21 burials 21 burials 21 burials 37 burials 42 burials 99 burials 101 burials 100 burials

( 26.5 %) ( 26.5 %) ( 7.5 %) ( 9.5 %) ( 3 %) ( 9.5 %) ( 4.5 %) ( 5 %) ( 12 %) ( 5 %) ( 26.5 %) ( 17 %) ( 3.5 %) ( 4.5 %) ( 9.5 %) ( 1 %) ( 1 %) ( 1 %) ( 32.5 %) ( 34.5 %) ( 67 %) ( 33 %) ( 10.5 %) ( 22.5 %) ( 5 %) ( 9.5 %) ( 10.5 %) ( 16.5 %) ( 10.5 %) ( 17 %) ( 10.5 %) ( 10.5 %) ( 10.5 %) ( 10.5 %) ( 18.5 %) ( 21 %) ( 49.5 %) ( 50.5 %) ( 50 %)

Total burials with artefacts: 200

**************************************************************************** TABLE 7 .8 Rules for artefact distribution in Model 4A

****************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Artefact

Probability

Social identities

SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRlNG PENDANTA PENDANTB

70 % prob 70 % prob 70 % prob 50 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 100 % prob 100 % prob 70 % prob 50 % prob 70 % prob 70 % prob 70 % prob 70 % prob

FEMALE /AGE> 14 / MALE /AGE> 14 / AGE> 14 / AGE> 14 / MALE /AGE< 15 / FEMALE /AGE< 15 / AGE< 15 /WEALTH= 2 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /SHAMAN I MALE /SHAMAN I WEALTH= 1 / AGE>0 I FEMALE /AGE> 39 I MALE IAGE> 39 I CLAN= 1 / CLAN= 2/

************************************************************************* TABLE 7 .9 Summary of rule occurrence in Model 4A

*************************************************************************

Rule 1 Rule 2

Artefact

Number

Percentage

SCRAPER BLADE

42 burials 37 burials

( 21.11 %) ( 18.59 %)

160

Appendix 1 ************************************************************************ TABLE 7.9 (Continued)

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING PENDANTA PENDANTB

92 burials 59 burials 26 burials 21 burials 10 burials 11 burials 17 burials 14 burials 23 burials 17 burials 15 burials 14 burials 14 burials 3 burials 3 burials 29 burials 102 burials 31 burials 19 burials 87 burials 49 burials

( 46.23 %) ( 29.65 %) ( 13.07 %) ( 10.55 %) ( 5.03 %) ( 5.53 %) ( 8.54 %) ( 7.04 %) (11.56%) ( 8.54 %) ( 7.54 %) ( 7.04 %) ( 7.04 %) ( 1.51 %) ( 1.51 %) ( 14.57 %) ( 51.26 %) ( 15.58 %) ( 9.55 %) ( 43.72 %) ( 24.62 %)

Total burials with artefacts: 199

**************************************************************************** TABLE 7.10 Rules for artefact distribution in Model 4B

****************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Artefact

Probability

Social identities

SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING PENDANTA PENDANTB

40 % prob 40 % prob 40 % prob 50 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 100 % prob 100 % prob 40 % prob 50 % prob 40 % prob 40 % prob 40 % prob 40 % prob

FEMALE /AGE> 14 / MALE /AGE> 14 / AGE> 14/ AGE> 14/ MALE /AGE< 15 / FEMALE /AGE< 15 / AGE 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /SHAMAN I MALE /SHAMAN/ WEALTH= 1 / AGE>0 I FEMALE IAGE> 39 I MALE /AGE> 39 / CLAN= 1 / CLAN=2/

************************************************************************ TABLE 7.11 Summary of rule occurrence in Model 4B

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS

22 burials 26 burials 51 burials 59 burials 9 burials 12 burials 7 burials 5 burials 9 burials 6 burials 12 burials 12 burials 11 burials 10 burials 7 burials 3 burials 3 burials 20 burials 102 burials 16 burials

( 12.09 %) ( 14.29 %) ( 28.02 %) ( 32.42 %) ( 4.95 %) ( 6.59 %) ( 3.85 %) ( 2.75 %) ( 4.95 %) ( 3.3 %) ( 6.59 %) ( 6.59 %) ( 6.04 %) ( 5.49 %) ( 3.85 %) ( 1.65 %) ( 1.65 %) ( 10.99 %) ( 56.04 %) ( 8.79 %)

161

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************ TABLE 7 .11 (Continued)

************************************************************************

Rule 21 Rule 22 Rule 23

Artefact

Number

Percentage

BNRlNG PENDANTA PENDANTB

15 burials 46 burials 25 burials

( 8.24 %) ( 25.27 %) ( 13.74 %)

Total burials with artefacts: 182

************************************************************************************ TABLE 7 .12

Rules for artefact distribution in Model 4C

************************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Artefact

Probability

Social identities

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRlNG TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRlNG PENDANTA PENDANTB CHARCOAL

70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 100 % prob 100 % prob 100 % prob 70 % prob 70 % prob 70 % prob 50 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 70 % prob 50 % prob

RANK= I/ RANK= 1 / RANK= I /MALE /AGE> 14 / RANK= I /FEMALE /AGE> 14 / RANK= 1 /AGE> 14 /MALE/WEALTH= 1 / RANK= I /WEALTH= I/ RANK= I /AGE> 14 /MALE /WEALTH= 2 / RANK= 1 /MALE /WEALTH= 1 / RANK= I /MALE / RANK= I /MALE /WEALTH= I / RANK= 1 / RANK= I /WEALTH= 2 / RANK= I /FEMALE /WEALTH= I /AGE> 14 / RANK= 1 /FEMALE /WEALTH= 1 / RANK= I /FEMALE /AGE> 14 / RANK= I /MALE/ AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= I /MALE/ AGE> 14 /LEADER/ RANK= 2 /FEMALE /AGE> 14 / RANK= 2 /MALE /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /MALE /AGE< 15 / RANK= 2 /FEMALE /AGE< 15 / RANK= 2 /AGE< 15 /WEALTH= 2 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= I/ RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= I/ RANK= 2 /FEMALE /AGE> 14 /WEALTH= I/ RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= I/ RANK= 2 /FEMALE /AGE> 39 / RANK= 2 /MALE /AGE> 39 / CLAN= I/ CLAN= 2/ AGE>0 I

************************************************************************ TABLE 7 .13 Summary of rule occurrence in Model 4C

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRlNG TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD

34 burials 35 burials 12 burials 11 burials 4 burials 16 burials 9 burials 8 burials 22 burials 8 burials 39 burials 26 burials 5 burials 7 burials 13 burials 2 burials 2 burials 2 burials 31 burials 38 burials 77 burials 46 burials 7 burials

( 17.09 %) ( 17.59 %) ( 6.03 %) ( 5.53 %) ( 2.01 %) ( 8.04 %) ( 4.52 %) ( 4.02 %) ( 11.06 %) ( 4.02 %) ( 19.6 %) ( 13.07 %) ( 2.51 %) ( 3.52 %) ( 6.53 %) ( 1.01 %) ( 1.01 %) ( 1.01 %) ( 15.58 %) ( 19.1 %) ( 38.69 %) ( 23.12 %) ( 3.52 %)

162

Appendix 1 ************************************************************************ TABLE 7.13 (Continued)

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

25 burials 8 burials 11 burials 15 burials 9 burials 13 burials 11 burials 9 burials 12 burials 10 burials 19 burials 22 burials 66 burials 65 burials 105 burials

( 12.56 %) ( 4.02 %) ( 5.53 %) ( 7.54 %) ( 4.52 %) ( 6.53 %) ( 5.53 %) ( 4.52 %) ( 6.03 %) ( 5.03 %) ( 9.55 %) ( 11.06 %) ( 33.17 %) ( 32.66 %) ( 52.76 %)

Total burials with artefacts: 199

********************************************************************************** TABLE 7 .14

Rules for artefact distribution in Model 4D

**********************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4

5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Artefact

Probability

Social identities

OCHRE DECPOT

40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 100 % prob 100 % prob 100 % prob 40 % prob 40 % prob 40 % prob 50 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 40 % prob 50 % prob

RANK= 1 / RANK= 1 / RANK= 1 /MALE /AGE> 14 / RANK= 1 /FEMALE/ AGE> 14 / RANK= I /AGE> 14 /MALE/WEALTH= 1 / RANK= 1 /WEAL TH= 1 / RANK= 1 /AGE> 14 /MALE /WEALTH= 2 / RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 /MALE/ RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 / RANK= 1 /WEAL TH= 2 / RANK= 1 /FEMALE /WEALTH= 1 /AGE> 14 / RANK= 1 /FEMALE /WEAL TH= 1 / RANK= 1 /FEMALE/ AGE> 14 / RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 2 /FEMALE/ AGE> 14 / RANK=2/MALE/AGE>14/ RANK= 2 /AGE> 14 / RANK= 2 /AGE> 14 / RANK=2/MALE/AGE 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE/ AGE> 39 / RANK= 2 /MALE /AGE> 39 / CLAN= 1 / CLAN= 2/ AGE>0 I

JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE

FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

************************************************************************ TABLE 7.15 Summary of rule occurrence in Model 4D

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4

5 6 7 8 9 10 11

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN

18 burials 17 burials 7 burials 6 burials 2 burials 8 burials 3 burials 3 burials 15 burials 5 burials 25 burials

( 9.47 %) ( 8.95 %) ( 3.68 %) ( 3.16 %) ( 1.05 %) ( 4.21 %) ( 1.58 %) ( 1.58 %) ( 7.89 %) ( 2.63 %) ( 13.16 %)

163

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************

TABLE 7.15 (Continued)

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRlNG PENDANTA PENDANTB CHARCOAL

( 6.32 %) ( 2.11 %) ( 2.11 %) ( 4.74 %) ( 1.05 %) ( 1.05 %) ( 1.05 %) ( 10 %) ( 11.05 %) ( 23.16 %) ( 24.21 %) (1.58%) (7.37%) ( 2.11 %) ( 3.16 %) ( 5.79 %) ( 3.68 %) ( 3.68 %) ( 3.68 %) ( 3.68 %) ( 3.16 %) ( 3.16 %) ( 5.79 %) ( 8.95 %) ( 22.11 %) ( 18.95 %) ( 55.26 %)

12 burials 4 burials 4 burials 9 burials 2 burials 2 burials 2 burials 19 burials 21 burials 44 burials 46 burials 3 burials 14 burials 4 burials 6 burials 11 burials 7 burials 7 burials 7 burials 7 burials 6 burials 6 burials 11 burials 17 burials 42 burials 36 burials 105 burials

Total burials with artefacts: 190

*************************************************************************** TABLE 7.16

Rules for artefact distribution in Model SA

*************************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

SCRAPER SCRAPER BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH STONEAXE STONEAXE ANIMAL MACE MANDIBLE DECPOT DECPOT CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRlNG BNRlNG PENDANTA PENDANTB

Probability

---- -----------

Social identities -- -- --- -- -- --- -- -- ---

70 % prob 30 % prob 70 % prob 30 % prob 70 % prob 30 % prob 50 % prob 70 % prob 30 % prob 70 % prob 30 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 70 % prob 40 % prob 100 % prob 70 % prob 40 % prob 100 % prob 100 % prob 100 % prob 70 % prob 40 % prob 75 % prob 50 % prob 25 % prob 70 % prob 30 % prob 70 % prob 30 % prob 50 % prob 50 % prob

FEMALE /AGE> 14 / MALE /AGE> 14 / MALE /AGE> 14 / FEMALE /AGE> 14 / AGE> 14 / AGE< 15 / AGE> 14 / MALE /AGE< 15 / MALE /AGE> 14 /WEALTH= 3 / FEMALE /AGE< 15 / FEMALE /AGE> 14 /WEALTH= 3 / AGE< 15 /WEALTH= 2 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / MALE /AGE> 14 /WEALTH= 1 / MALE /SHAMAN I MALE /SHAMAN I WEALTH= 1 / WEALTH= 2/ AGE> 39 I AGE> 14 /AGE< 40 / AGE< 15 / FEMALE /AGE> 39 I MALE IAGE> 39 I MALE IAGE> 39 I FEMALE /AGE> 39 I CLAN= 1 / CLAN= 2/

************************************************************************ TABLE 7 .17 Summary of rule occurrence in Model SA

************************************************************************

Rule 1 Rule 2

Artefact

Number

Percentage

SCRAPER SCRAPER

48 burials 17 burials

( 24.87 %) ( 8.81 %)

164

Appendix 1 ************************************************************************ TABLE 7.17 (Continued)

************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Artefact

Number

Percentage

BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH STONEAXE STONEAXE ANIMAL MACE MANDIBLE DECPOT DECPOT CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING PENDANTA PENDANTB

41 burials 25 burials 82 burials 10 burials 63 burials 31 burials 3 burials 23 burials 2 burials 14 burials 17 burials 22 burials 21 burials 33 burials 12 burials 10 burials 21 burials 12 burials 8 burials 17 burials 3 burials 3 burials 29 burials 29 burials 63 burials 19 burials 16 burials 35 burials 8 burials 27 burials 16 burials 64 burials 44 burials

( 21.24 %) ( 12.95 %) ( 42.49 %) ( 5.18 %) ( 32.64 %) ( 16.06 %) ( 1.55 %) ( 11.92 %) ( 1.04 %) ( 7.25 %) ( 8.81 %) ( 11.4 %) ( 10.88 %) ( 17.1 %) ( 6.22 %) ( 5.18 %) ( 10.88 %) ( 6.22 %) ( 4.15 %) ( 8.81 %) ( 1.55 %) ( 1.55 %) ( 15.03 %) ( 15.03 %) ( 32.64 %) ( 9.84 %) ( 8.29 %) ( 18.13 %) ( 4.15 %) ( 13.99 %) ( 8.29 %) ( 33.16 %) ( 22.8 %)

Total burials with artefacts: 193

************************************************************************************* TABLE 7 .18

Rules for artefact distribution in Model SB

*************************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Artefact

Probability

Social identities

OCHRE OCHRE DECPOT

70 % prob 30 % prob 70 % prob 40 % prob 70 % prob 30 % prob 70 % prob 30 % prob 100 % prob 30 % prob 100 % prob 70 % prob 70 % prob 30 % prob 70 % prob 30 % prob 100 % prob 70 % prob 30 % prob 100 % prob 100 % prob 100 % prob 70 % prob 30 % prob 100 % prob 100 % prob 100 % prob 70 % prob 30 % prob 70 % prob 30 % prob 70 % prob 30 % prob 50 % prob 70 % prob 30 % prob 70 % prob 30 % prob 100 % prob 100 % prob 100 % prob

RANK= 1 / RANK= 2 /WEAL TH= 1 / RANK= 1 / RANK= 2 /WEAL TH= 1 / RANK= 1 /MALE /AGE> 14 / RANK= 1 /MALE /AGE< 15 /WEALTH= 1 / RANK= 1 /FEMALE/ AGE> 14 / RANK= 1 /FEMALE /AGE< 15 /WEALTH= 1 / RANK=l/AGE> 14 /MALE /WEALTH= 1 / RANK=l/AGE> 14 /FEMALE /WEALTH= 1 / RANK= 1 /WEAL TH= 1 / RANK= 1 /AGE> 14 /MALE /WEALTH= 2 / RANK= 1 /MALE /WEAL TH= 1 / RANK= 2 /MALE /WEAL TH= 1 / RANK= 1 /MALE/ RANK= 2 /MALE /WEAL TH= 1 / RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 / RANK= 2 /WEAL TH= 1 / RANK= 1 /WEAL TH= 2 / RANK= 1 /FEMALE /WEALTH= 1 /AGE> 14 / RANK= 1 /FEMALE /WEAL TH= 1 / RANK= 1 /FEMALE /AGE> 14 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 2 /FEMALE /AGE> 14 / RANK=2/MALE/AGE>14/ RANK=2/MALE/AGE>14/ RANK= 2 /FEMALE /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /AGE< 15 / RANK= 2 /AGE> 14 / RANK=2/MALE/AGE 14 /WEALTH= 3 / RANK= 2 /FEMALE /AGE< 15 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 3 / RANK= 2 /AGE< 15 /WEALTH= 2 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 /

DECPOT JUG JUG CUP CUP COPPAXE COPPAXE

CPPLATE CPBLADE CPBLADE CPBLADE CPRING CPRING TUSK COPPERPN COPPERPN

CPBEAD CPSPIRAL CPBRCLT COPPAWL COPPAWL

MACE MANDIBLE

FIGURINE SCRAPER SCRAPER

BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD

STBEAD SHBEAD SHBEAD BOWL ARROW ARROW

165

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************************* TABLE 7.18 (Continued)

************************************************************************************* Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58

BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH STONEAXE STONEAXE ANIMAL CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING PENDANTA PENDANTB

Probability

---------------

Social identities -- -- --- -- -- --- -- -- ---

100 % prob 100 % prob 70 % prob 40 % prob 100 % prob 70 % prob 40 % prob 100 % prob 75 % prob 50 % prob 25 % prob 70 % prob 30 % prob 70 % prob 30 % prob 50 % prob 50 % prob

RANK= 2 /FEMALE /AGE> 14 /WEALTH= I / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= I/ RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= I/ RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / AGE> 39 I AGE> 14 /AGE< 40 I AGE< 15 / RANK= 2 /FEMALE /AGE> 39 / RANK= 2 /MALE /AGE> 39 / RANK= 2 /MALE /AGE> 39 / RANK= 2 /FEMALE /AGE> 39 / CLAN= 1 / CLAN= 2/

************************************************************************* TABLE 7 .19 Summary of rule occurrence in Model SB

************************************************************************* Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58

OCHRE OCHRE DECPOT DECPOT JUG JUG CUP CUP COPPAXE COPPAXE CPPLATE CPBLADE CPBLADE CPBLADE CPRING CPRING TUSK COPPERPN COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL COPPAWL MACE MANDIBLE FIGURINE SCRAPER SCRAPER BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH STONEAXE STONEAXE ANIMAL CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING PENDANTA PENDANTB

40 burials 11 burials 35 burials 10 burials 10 burials 2 burials 14 burials 1 burials 6 burials 1 burials 19 burials 6 burials 9 burials 7 burials 21 burials 4 burials 10 burials 36 burials 10 burials 34 burials 7 burials 9 burials 16 burials 2 burials 2 burials 2 burials 2 burials 36 burials 16 burials 37 burials 14 burials 76 burials 18 burials 57 burials 8 burials 5 burials 25 burials 4 burials 10 burials 15 burials 24 burials 14 burials 22 burials 9 burials 9 burials 14 burials 8 burials 10 burials 15 burials 55 burials 30 burials 17 burials 13 burials 12 burials 16 burials 13 burials 43 burials 50 burials

( 20.1 %) ( 5.53 %) ( 17.59 %) ( 5.03 %) ( 5.03 %) ( 1.01 %) ( 7.04 %) ( .5 %) ( 3.02 %) ( .5 %) ( 9.55 %) ( 3.02 %) ( 4.52 %) ( 3.52 %) ( 10.55 %) ( 2.01 %) ( 5.03 %) ( 18.09 %) ( 5.03 %) ( 17.Q9 %) ( 3.52 %) ( 4.52 %) ( 8.04 %) ( 1.01 %) ( 1.01 %) ( 1.01 %) ( 1.01 %) ( 18.Q9 %) ( 8.04 %) ( 18.59 %) ( 7.04 %) ( 38.19 %) ( 9.05 %) ( 28.64 %) ( 4.02 %) ( 2.51 %) ( 12.56 %) ( 2.01 %) ( 5.03 %) ( 7.54 %) ( 12.06 %) ( 7.04 %) ( 11.06 %) ( 4.52 %) ( 4.52 %) ( 7.04 %) ( 4.02 %) ( 5.03 %) ( 7.54 %) ( 27.64 %) ( 15.08 %) ( 8.54 %) ( 6.53 %) ( 6.03 %) ( 8.04 %) ( 6.53 %) ( 21.61 %) ( 25.13 %)

166

Appendix 1 Total burials with artefacts: 199

************************************************************************************* TABLE 7 .20 Rules for artefact distribution in Model 6A

************************************************************************************* Artefact

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1

2 3 4

5 6 7

8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

45 46 47 48 49 50 51

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE PLAINPOT DECPOT CPDISC BLADE ARROW STONEAXE CPBLADE SCRAPER SHBRCLT COPPAWL BOWL STBEAD SHBEAD SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

Probability

Social identities

100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob

RANK= 1 / RANK= 1 / RANK= 1 /MALE /AGE> 14 / RANK= 1 /FEMALE/ AGE> 14 / RANK= 1 /AGE> 14 /MALE /WEALTH= 1 / RANK= 1 /WEAL TH= 1 / RANK= 1 /AGE> 14 /MALE /WEALTH= 2 I RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 /MALE I RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 / RANK= 1 /WEAL TH= 2 / RANK= 1 /FEMALE /WEALTH= 1 /AGE> 14 / RANK= 1 /FEMALE /WEAL TH= 1 / RANK= 1 /FEMALE /AGE> 14 / RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK=2/ RANK=2/ RANK= 2 /AGE> 14 / RANK=2/MALE/AGE>14/ RANK=2/MALE/AGE>14/ RANK=2/MALE/AGE>14/ RANK=2/MALE/AGE>14/ RANK= 2 /FEMALE IAGE> 14 / RANK= 2 /FEMALE /AGE> 14 / RANK= 2 /FEMALE IAGE> 14 / RANK= 2 /AGE< 15 / RANK=2/MALE/AGE 14 / RANK=3/MALE/AGE>14/ RANK= 3 /AGE> 14 / RANK= 3 /AGE> 14 / RANK=3/MALE/AGE 14 /WEALTH= 1 / RANK= 3 /MALE /AGE> 14 /WEALTH= 2 / RANK= 3 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 3 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 3 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 3 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 3 /MALE /AGE> 14 /WEALTH= 1 / RANK= 3 /MALE /AGE> 14 /WEALTH= 1 / RANK= 3 /FEMALE IAGE> 39 I RANK= 3 /MALE /AGE> 39 I CLAN= 1 / CLAN=2/ AGE>0 I

************************************************************************* TABLE 7 .21 Summary of rule occurrence in Model 6A

************************************************************************* Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE PLAINPOT DECPOT CPDISC

33 burials 33 burials 11 burials 12 burials 4 burials 14 burials 7 burials 6 burials 17 burials 6 burials 33 burials 19 burials 7 burials 8 burials 12 burials 3 burials 3 burials 3 burials 60 burials 60 burials 39 burials

( 16.5 %) ( 16.5 %) ( 5.5 %) ( 6 %) ( 2 %) ( 7 %) ( 3.5 %) ( 3 %) ( 8.5 %) ( 3 %) ( 16.5 %) ( 9.5 %) ( 3.5 %) ( 4 %) ( 6 %) ( 1.5 %) ( 1.5 %) ( 1.5 %) ( 30 %) ( 30 %) ( 19.5 %)

167

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************ TABLE 7.21 (Continued)

************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

Artefact

Number

Percentage

BLADE ARROW STONEAXE CPBLADE SCRAPER SHBRCLT COPPAWL BOWL STBEAD SHBEAD SCRAPER BLADE PLAlNPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

19 burials 19 burials 19 burials 19 burials 20 burials 20 burials 20 burials 21 burials 15 burials 6 burials 32 burials 36 burials 68 burials 34 burials 19 burials 20 burials 8 burials 11 burials 11 burials 5 burials 15 burials 5 burials 5 burials 11 burials 11 burials 17 burials 23 burials 112 burials 88 burials 111 burials

( 9.5 %) ( 9.5 %) ( 9.5 %) ( 9.5 %) ( 10 %) ( 10 %) ( 10 %) ( 10.5 %) ( 7 .5 %) ( 3 %) ( 16 %) ( 18 %) ( 34 %) ( 17 %) ( 9.5 %) ( 10 %) ( 4 %) ( 5.5 %) ( 5.5 %) ( 2.5 %) ( 7 .5 %) ( 2.5 %) ( 2.5 %) ( 5.5 %) ( 5.5 %) ( 8.5 %) ( 11.5 %) ( 56 %) ( 44 %) ( 55.5 %)

Total burials with artefacts: 200

************************************************************************** TABLE 7 .22 Rules for artefact distribution in Model 7A

**************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Artefact

Probability

Social identities

SCRAPER BLADE PLAlNPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS PENDANTA PENDANTB

100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 100 % prob 100 % prob

FEMALE /AGE> 14 I MALE /AGE> 14 / AGE> 14 I AGE> 14 I MALE /AGE< 15 / FEMALE /AGE< 15 I AGE< 15 /WEALTH= 2 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= I/ FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= I/ FEMALE /AGE> 14 /WEALTH= I/ MALE /AGE> 14 /WEALTH= I/ MALE /AGE> 14 /WEALTH= 1 / MALE /SHAMAN I MALE /SHAMAN I WEALTH= 1 / AGE>0 I FEMALE /AGE> 39 I MALE I AGE> 39 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I CLAN= 1 / CLAN= 2/

************************************************************************* TABLE 7 .23 Summary of rule occurrence in Model 7 A

************************************************************************* Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule

I 2 3 4 5 6

SCRAPER BLADE PLAlNPOT BONEPIN STBEAD SHBEAD

71 burials 54 burials 125 burials 65 burials 39 burials 36 burials

( 35.5 %) ( 27 %) ( 62.5 %) ( 32.5 %) ( 19.5 %) ( 18 %)

168

Appendix 1 ************************************************************************ TABLE 7.23 (Continued)

************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Artefact

Number

Percentage

BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS PENDANTA PENDANTB

14 burials 17 burials 22 burials 21 burials 33 burials 21 burials 21 burials 17 burials 17 burials 3 burials 3 burials 38 burials 99 burials 49 burials 31 burials 105 burials 96 burials 100 burials 97 burials 103 burials 114 burials 86 burials

( 7 %) ( 8.5 %) ( 11 %) ( 10.5 %) ( 16.5 %) ( 10.5 %) ( 10.5 %) ( 8.5 %) ( 8.5 %) ( 1.5 %) ( 1.5 %) ( 19 %) ( 49.5 %) ( 24.5 %) ( 15.5 %) ( 52.5 %) ( 48 %) ( 50 %) ( 48.5 %) ( 51.5 %) ( 57 %) ( 43 %)

**************************************************************************** TABLE 7 .24 Rules for artefact distribution in Model 7B

**************************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS RANDOM6 RANDOM? RANDOMS RANDOM9 RANDOMl0 PENDANTA PENDANTB

Probability

Social identities

---------------

------------ ------- --

100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 100 % prob 100 % prob

FEMALE /AGE> 14 / MALE /AGE> 14 / AGE>14/ AGE> 14/ MALE /AGE< 15 / FEMALE /AGE< 15 / AGE 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /SHAMAN I MALE /SHAMAN/ WEALTH= 1 / AGE>0 I FEMALE IAGE> 39 I MALE /AGE> 39 / AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I CLAN= 1 / CLAN=2/

************************************************************************ TABLE 7 .25 Summary of rule occurrence in Model 7B

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11

SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL

71 burials 54 burials 125 burials 66 burials 39 burials 36 burials 14 burials 17 burials 22 burials 21 burials 33 burials

( ( ( ( ( ( ( ( ( ( (

169

35.5 %) 27 %) 62.5 %) 33 %) 19.5 %) 18 %) 7 %) 8.5 %) 11 %) 10.5 %) 16.5 %)

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************ TABLE 7.25 (Continued)

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING RANDOMl RANDOM2 RANDOM3 RANDOM4 RANDOMS RANDOM6 RANDOM? RANDOMS RANDOM9 RANDOMlO PENDANTA PENDANTB

21 burials 21 burials 17 burials 17 burials 3 burials 3 burials 38 burials 94 burials 49 burials 31 burials 101 burials 108 burials 101 burials 103 burials 98 burials 113 burials 87 burials 99 burials 96 burials 108 burials 114 burials 86 burials

( 10.5 %) ( 10.5 %) ( 8.5 %) ( 8.5 %) ( 1.5 %) ( 1.5 %) ( 19 %) ( 47 %) ( 24.5 %) ( 15.5 %) ( 50.5 %) ( 54 %) ( 50.5 %) ( 51.5 %) ( 49 %) ( 56.5 %) ( 43.5 %) ( 49.5 %) ( 48 %) ( 54 %) ( 57 %) ( 43 %)

Total burials with artefacts: 200

************************************************************************************ TABLE 7 .26 Rules for artefact distribution in Model 7C

************************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

I

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Artefact

Probability

Social identities

OCHRE DECPOT JUG

100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob

RANK= I/

CUP

COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB RANDOMl RANDOM2 RANDOM3 RANDOM4 RANDOMS CHARCOAL

RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK=

1/ 1 /MALE /AGE> 14 / 1 /FEMALE /AGE> 14 / 1/ AGE> 14 /MALE /WEALTH= 1 / 1 /WEALTH= 1 / 1 /AGE> 14 /MALE /WEALTH= 2 / 1 /MALE /WEALTH= 1 / 1 /MALE / 1 /MALE /WEALTH= 1 / 1/ 1 /WEALTH= 2 / I /FEMALE /WEALTH= 1 /AGE> 14 / 1 /FEMALE /WEALTH= 1 / 1 /FEMALE /AGE> 14 / RANK= I /MALE/AGE> 14 /LEADER I RANK= 1 /MALE/ AGE> 14 /LEADER/ RANK= 1 /MALE/ AGE> 14 /LEADER/ RANK= 2 /FEMALE /AGE> 14 / RANK= 2 /MALE /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /MALE /AGE< 15 / RANK= 2 /FEMALE /AGE< 15 / RANK= 2 /AGE< 15 /WEALTH= 2 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= I / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 39 / RANK= 2 /MALE /AGE> 39 / CLAN= I/ CLAN= 2/ AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I

170

Appendix 1

************************************************************************* TABLE 7 .27 Summary of rule occurrence in Model 7C

*************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1

2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Artefact

Number

Percentage

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAlNPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB RANDOMl RANDOM2 RANDOM3 RANDOM4 RANDOMS CHARCOAL

53 burials 53 burials 15 burials 19 burials 6 burials 19 burials 9 burials 10 burials 24 burials 10 burials 53 burials 34 burials 7 burials 9 burials 19 burials 2 burials 2 burials 2 burials 46 burials 54 burials 100 burials 46 burials 12 burials 35 burials 10 burials 15 burials 24 burials 14 burials 22 burials 14 burials 14 burials 15 burials 15 burials 26 burials 32 burials 99 burials 101 burials 98 burials 103 burials 108 burials 92 burials 108 burials 86 burials

( 26.5 %) ( 26.5 %) ( 7.5 %) ( 9.5 %) ( 3 %) ( 9.5 %) ( 4.5 %) ( 5 %) ( 12 %) ( 5 %) ( 26.5 %) ( 17 %) ( 3.5 %) ( 4.5 %) ( 9.5 %) ( 1 %) ( 1 %) ( 1 %) ( 23 %) ( 27 %) ( 50 %) ( 23 %) ( 6 %) ( 17.5 %) ( 5 %) ( 7.5 %) ( 12 %) ( 7 %) ( 11 %) ( 7 %) ( 7 %) ( 7.5 %) ( 7.5 %) ( 13 %) ( 16 %) ( 49.5 %) ( 50.5 %) ( 49 %) (51.5 %) ( 54 %) ( 46 %) ( 54 %) ( 43 %)

Total burials with artefacts: 200

************************************************************************************ TABLE 7 .28

Rules for artefact distribution in Model 7D

************************************************************************************ Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAlNPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT

Probability

Social identities

---------------

------------ ------- --

100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob

RANK= 1 / RANK= 1 / RANK= 1 /MALE /AGE> 14 / RANK= 1 /FEMALE IAGE> 14 / RANK= 1 /AGE> 14 /MALE /WEALTH= 1 / RANK= 1 /WEAL TH= 1 / RANK= 1 /AGE> 14 /MALE /WEALTH= 2 / RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 /MALE I RANK= 1 /MALE /WEAL TH= 1 / RANK= 1 / RANK= 1 /WEAL TH= 2 / RANK= 1 /FEMALE /WEALTH= 1 /AGE> 14 / RANK= 1 /FEMALE /WEAL TH= 1 / RANK= 1 /FEMALE /AGE> 14 / RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/AGE> 14 /LEADER/ RANK= 2 /FEMALE IAGE> 14 / RANK=2/MALE/AGE>14/ RANK= 2 /AGE> 14 / RANK= 2 /AGE> 14 / RANK=2/MALE/AGE 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 /

171

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************************** TABLE 7.28 (Continued)

************************************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS RANDOM6 RANDOM? RANDOMS RANDOM9 RANDOMlO CHARCOAL

Probability

---------------

Social identities -- -- --- -- -- --- -- -- ---

100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob 50 % prob

RANK= 2 /FEMALE /AGE> 14 /WEALTH= I / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 39 I RANK= 2 /MALE /AGE> 39 / CLAN= 1 / CLAN= 2/ AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I AGE>0 I

************************************************************************* TABLE 7 .29 Summary of rule occurrence in Model 7D

************************************************************************* Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS RANDOM6 RANDOM? RANDOMS RANDOM9 RANDOMlO CHARCOAL

53 burials 53 burials 15 burials 19 burials 6 burials 19 burials 9 burials 10 burials 24 burials 10 burials 53 burials 34 burials 7 burials 9 burials 19 burials 2 burials 2 burials 2 burials 46 burials 54 burials 100 burials 45 burials 12 burials 35 burials 10 burials 15 burials 24 burials 14 burials 22 burials 14 burials 14 burials 15 burials 15 burials 26 burials 32 burials 99 burials 101 burials 91 burials 92 burials 107 burials 107 burials 110 burials 104 burials 102 burials 100 burials 82 burials 94 burials 102 burials

( 26.5 %) ( 26.5 %) ( 7 .5 %) ( 9.5 %) ( 3 %) ( 9.5 %) ( 4.5 %) ( 5 %) ( 12 %) ( 5 %) ( 26.5 %) ( 17 %) ( 3.5 %) ( 4.5 %) ( 9.5 %) ( 1 %) ( 1 %) ( 1 %) ( 23 %) ( 27 %) ( 50 %) ( 22.5 %) ( 6 %) ( 17.5 %) ( 5 %) ( 7 .5 %) ( 12 %) ( 7 %) ( 11 %) ( 7 %) ( 7 %) ( 7.5 %) ( 7.5 %) ( 13 %)

( 16 %) ( 49.5 %) ( 50.5 %) ( 45.5 %) ( 46 %) ( 53.5 %) ( 53.5 %) ( 55 %) ( 52 %) ( 51 %) ( 50 %) ( 41 %) ( 47 %) ( 51 %)

Total burials with artefacts: 200

172

Appendix 1

************************************************************************** TABLE 7 .30 Rules for artefact distribution in Model 8A

************************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING PENDANTA PENDANTB

Probability

Social identities

100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob

FEMALE /AGE> 14 / MALE /AGE> 14 / AGE> 14/ AGE> 14/ MALE /AGE< 15 / FEMALE /AGE< 15 / AGE 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 2 / FEMALE /AGE> 14 /WEALTH= 1 / FEMALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /AGE> 14 /WEALTH= 1 / MALE /SHAMAN I MALE /SHAMAN I WEALTH= 1 / AGE>0 I FEMALE IAGE> 39 I MALE /AGE> 39 / CLAN= 1 / CLAN= 2/

************************************************************************ TABLE 7 .31 Summary of rule occurrence in Model 8A

************************************************************************ Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL MACE MANDIBLE DECPOT CHARCOAL SHELLS BNRING PENDANTA PENDANTB

( 18.5 %) ( 46.5 %) ( 65 %) ( 30 %) ( 30.5 %) ( 4.5 %) ( 7 %) ( 10.5 %) ( 24 %) ( 4 %) ( 8 %) ( 4 %) ( 4 %) ( 10.5 %) ( 10.5 %) ( 1.5 %) ( 1.5 %) ( 14.5 %) ( 48.5 %) ( 11 %) ( 28.5 %) ( 55.5 %) ( 44.5 %)

37 burials 93 burials 130 burials 60 burials 61 burials 9 burials 14 burials 21 burials 48 burials 8 burials 16 burials 8 burials 8 burials 21 burials 21 burials 3 burials 3 burials 29 burials 97 burials 22 burials 57 burials 111 burials 89 burials

Total burials with artefacts: 200

*********************************************************************************** TABLE 7.32

Rules for artefact distribution in Model 8B

*********************************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4

5 6 7 8 9 10 11 12 13 14 15

OCHRE DECPOT JUG

CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL

Probability 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob

Social identities RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK= RANK=

1/ 1/ 1 /MALE /AGE> 14 / 1 /FEMALE /AGE> 14 / 1/ AGE> 14 /MALE /WEALTH= 1 / 1 /WEAL TH= 1 / 1 /AGE> 14 /MALE /WEALTH= 2 / 1 /MALE /WEAL TH= 1 / 1 /MALE I 1 /MALE /WEAL TH= 1 / 1/ 1 /WEAL TH= 2 / 1 /FEMALE /WEALTH= 1 /AGE> 14 / 1 /FEMALE /WEAL TH= 1 / 1 /FEMALE/ AGE> 14 /

173

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************************ TABLE 7.32 (Continued)

************************************************************************************ Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

16 17 18 19 20 21 22 23 24 25 26 27

28 29 30 31 32 33 34 35 36 37 38

MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

Probability

---------------

Social identities -- -- --- -- -- --- -- -- ---

100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 100 % prob 50 % prob

RANK= I /MALE/AGE> 14 /LEADER/ RANK= 1 /MALE/ AGE> 14 /LEADER/ RANK= 1 /MALE/ AGE> 14 /LEADER/ RANK= 2 /FEMALE /AGE> 14 / RANK= 2 /MALE /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /AGE> 14 / RANK= 2 /MALE /AGE< 15 / RANK= 2 /FEMALE /AGE< 15 / RANK= 2 /AGE< 15 /WEALTH= 2 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= I / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 2 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 14 /WEALTH= I / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /MALE /AGE> 14 /WEALTH= 1 / RANK= 2 /FEMALE /AGE> 39 / RANK= 2 /MALE /AGE> 39 / CLAN= 1 / CLAN= 2/ AGE>0 I

************************************************************************* TABLE 7 .33 Summary of rule occurrence in Model 8B

************************************************************************* Artefact

Number

Percentage

-------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

OCHRE DECPOT JUG CUP COPPAXE CPPLATE CPBLADE CPBLADE CPRING TUSK COPPERPN CPBEAD CPSPIRAL CPBRCLT COPPAWL MACE MANDIBLE FIGURINE SCRAPER BLADE PLAINPOT BONEPIN STBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT TEETH STONEAXE ANIMAL SHELLS BNRING PENDANTA PENDANTB CHARCOAL

94 burials 94 burials 39 burials 24 burials 13 burials 38 burials 26 burials 19 burials 54 burials 19 burials 94 burials 56 burials 14 burials 19 burials 24 burials 4 burials 4 burials 4 burials 41 burials 42 burials 83 burials 47 burials 15 burials 8 burials 4 burials 15 burials 18 burials 7 burials 22 burials 7 burials 7 burials 15 burials 15 burials 25 burials 23 burials 112 burials 88 burials 91 burials

( 47 %) ( 47 %) ( 19.5 %) ( 12 %) ( 6.5 %) ( 19 %) ( 13 %)

( 9.5 %) ( 27 %) ( 9.5 %) ( 47 %) ( 28 %) ( 7 %) ( 9.5 %) ( 12 %) ( 2 %) ( 2 %) ( 2 %) ( 20.5 %) ( 21 %) ( 41.5 %) ( 23.5 %) ( 7.5 %) ( 4 %) ( 2 %) ( 7 .5 %) ( 9 %) ( 3.5 %) ( 11 %) ( 3.5 %) ( 3.5 %) ( 7 .5 %) ( 7.5 %) ( 12.5 %) ( 11.5 %) ( 56 %) ( 44 %) ( 45.5 %)

Total burials with artefacts: 200

***************************************************************************

TABLE 7.34 Rules for artefact distribution in Ramsauer model

***************************************************************************

Rule Rule Rule Rule

Artefact

Probability

Social identities

I

CHARM

2 3 4

MISC IRON VESSEL

15 % prob 15 % prob 20 % prob 15 % prob

AGE>0 I AGE>0 I AGE>0 I RANK=4/

174

Appendix 1 ******************************************************************************** TABLE 7.34 (Continued)

********************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

5 6 7 8 9 10

II 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Artefact

Probability

Social identities

PIN

10 % prob 10 % prob 10 % prob 75 % prob 75 % prob 75 % prob 60 % prob 75 % prob 25 % prob 25 % prob 75 % prob 30 % prob 60 % prob 70 % prob 25 % prob 50 % prob 75 % prob 40 % prob 40 % prob 75 % prob 50 % prob 40 % prob 70 % prob 40 % prob 60 % prob 70 % prob 70 % prob 50 % prob 50 % prob 30 % prob 30 % prob

AGE>0 I AGE>0 I AGE>0 I RANK= I /WEALTH= 2 /CHOICE= RANK= I /WEALTH= 2 /CHOICE= RANK= 1 /WEAL TH= 3 / RANK= I /WEAL TH= 2 / RANK> I /CHOICE= I / RANK> 1 /CHOICE= 1 / RANK> I /CHOICE= I / RANK= I /CHOICE= I /WEALTH= RANK= 1 /CHOICE= 2 /WEALTH= RANK= I /CHOICE= 2 /WEALTH= RANK> I /CHOICE= 2 / RANK> 1 /CHOICE= 2 / RANK> I /CHOICE= 2 / RANK= 2 /WEAL TH= 2 / RANK= 2 /WEAL TH= 1 / RANK>2/ RANK= 2 /WEAL TH= I / RANK>2/ RANK>2/ RANK>2/ RANK>2/ RANK>2/ RANK>2/

COFFIN KNIFE SINGSPEC SINGOTH SINGBRAC SINGBRAC SINGBRAC SINGSPEC SINGOTH

MULTBRAC MULTOTHR

MULTSPEC MULTBRAC MULTOTHR

MULTSPEC BEADS BEADS

BEADS BELT BELT LUNATE HAIRPIN

ANKLET RODLINK

JANGLE GOLD BRZRING COIL CHAIN

ox

I/ 2/

I/ 1/

I/

RANK=4I

RANK=4/ RANK=4/ RANK=4I

RANK=4/

************************************************************************ TABLE 7 .35 Summary of rule occurrence in Ramsauer model (repetition I)

************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Artefact

Number

Percentage

CHARM MISC IRON VESSEL PIN COFFIN KNIFE SINGSPEC SINGOTH SINGBRAC SINGBRAC SINGBRAC SINGSPEC SINGOTH MULTBRAC MULTOTHR MULTSPEC MULTBRAC MULTOTHR MULTSPEC BEADS BEADS BEADS BELT BELT LUNATE HAIRPIN ANKLET RODLINK JANGLE GOLD BRZRING COIL CHAIN

22 burials 26 burials 32 burials I burial 21 burials 19 burials 26 burials 8 burials 4 burials 8 burials 8 burials 35 burials 13 burials 15 burials 25 burials 8 burials 23 burials 34 burials 12 burials 26 burials 22 burials 16 burials 14 burials 37 burials 20 burials 12 burials 28 burials 11 burials 23 burials 33 burials 10 burials 6 burials 8 burials 7 burials 6 burials

(11.52%) ( 13.61 %) ( 16.75 %) ( .52 %) ( 10.99 %) ( 9.95 %) ( 13.61 %) ( 4.19 %) ( 2.09 %) ( 4.19 %) ( 4.19 %) ( 18.32 %) ( 6.81 %) ( 7.85 %) ( 13.09 %) ( 4.19 %) ( 12.04 %) ( 17.8 %) ( 6.28 %) ( 13.61 %) (11.52%) ( 8.38 %) (7.33%) ( 19.37 %) ( 10.47 %) ( 6.28 %) ( 14.66 %) ( 5.76 %) ( 12.04 %) ( 17.28 %) ( 5.24 %) ( 3.14 %) ( 4.19 %) ( 3.66 %) ( 3.14 %)

ox

Total burials with artefacts: 191

175

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

********************************************************************************** TABLE 7 .36 Summary of top Jaccard values for clustering of Model lA (random data); @ symbol indicates level in dendrogram Jaccard value is first found

********************************************************************************** SOCIAL GROUP

WARD

1 )/CLANl/ 2 )/CLAN2/ 3 )/SHAMAN/ 4 )/ADULT/ 5 )/SUBADULT/ 6 )/SUBADULT/MALE/ 7 )/SUBADULT/FEMALE/ 8 )/SUBADULT/AVERAGE/ 9 )/SUBADULT/POOR/ 10 )/ADULT/MALE/ 11 )/ADULT/FEMALE/ 12 )/WEALTHY/ 13 )/WEALTHY/FEMALE/ 14 )/WEAL THY /MALE/ 15 )/ADULT/POOR/ 16 )/ADULT/POOR/MALE/ 17 )/ADULT/POOR/FEMALE/ 18 )/ADULT/AVERAGE/ 19 )/ADULT/AVERAGE/MALE/ 20 )/ADULT/AVERAGE/FEMALE/ 21 )/MALE/ 22 )/FEMALE/ 23 )/AGED 40+/ 24 )/AGED 40+/MALE/ 25 )/AGED 40+/FEMALE/

.473@ 2 .311@ 2 .067@ 11 .477@2 .299@2 .175@ 2 .154@ 2 .087@ 11 .258@ 2 .228@2 .316@ 2 .2@2 .136@ 2 .143@ 11 .209 @4 .114@ 5 .132@ 8 .19@ 2 .167@ 6 .143@ 7 .362@ 2 .422@2 .291@ 2 .147@ 3 .224@2

AVERAGE

DIVISIVE

.575@ 7 .426@4 .25@ 5 .583 @4 .37@2 .19@ 2 .189@ 11 .107@ 14 .3@ 2 .274@ 8 .364@4 .251@ 4 .164@ 11 .097@ 3 .153@ 13 .107@ 14 .094@ 14 .236@4 .122@ 13 .128@ 3 .46@2 .549@4 .413@ 10 .171@11 .262@ 10

.382@ 2 .307@ 2 .091@ 10 .39@2 .281@ 2 .193@ 2 .177@ 2 .125@ 14 .244@2 .236@2 .282@2 .237@ 2 .187@ 2 .123@ 3 .147@ 2 .16@ 13 .133@ 10 .211 @ 2 .146@ 6 .169@ 3 .371 @ 2 .388@ 2 .341@ 2 .153@ 2 .237@2

********************************************************************************* TABLE 7 .37 Summary of top Jaccard values for clustering of Model lA (real data) @ symbol indicates level in dendrogram Jaccard value is first found

********************************************************************************* SOCIAL GROUP

WARD

1 )/CLANl/ 2 )/CLAN2/ 3 )/SHAMAN/ 4 )/ADULT/ 5 )/SUBADULT/ 6 )/SUBADULT/MALE/ 7 )/SUBADULT/FEMALE/ 8 )/SUBADULT/AVERAGE/ 9 )/SUBADULT/POOR/ 10 )/ADULT/MALE/ 11 )/ADULT/FEMALE/ 12 )/WEALTHY/ 13 )/WEALTHY/FEMALE/ 14 )/WEAL THY /MALE/ 15 )/ADULT/POOR/ 16 )/ADULT/POOR/MALE/ 17 )/ADULT/POOR/FEMALE/ 18 )/ADULT/AVERAGE/ 19 )/ADULT/AVERAGE/MALE/ 20 )/ADULT/AVERAGE/FEMALE/ 21 )/MALE/ 22 )/FEMALE/ 23 )/AGED 40+/ 24 )/AGED 40+/MALE/ 25 )/AGED 40+/FEMALE/

AVERAGE

.44@2 .395 @4 .176@ 9 1 @2** 1 @2** .52@ 2 .583@ 5 .19@ 12 .813@ 2 * 1@ 3 ** 1@ 3 ** .553@ 6 1@ 6** 1 @9 ** .278@ 9 .405 @9 .345@ 8 .458@ 6 .595@ 9 .66@6 .581 @ 3 .664@3 .64@2 .574@ 3 .69@3

.44@2 .395@ 3 .176@ 7 1 @2 ** 1 @2** .52@ 2 .583@ 5 .187@ 2 .813@ 2 * 1 @4 ** 1 @4 ** .553@ 9 1 @9 ** 1@ 7 ** .278@ 7 .562@ 13 .345@ 8 .44@2 .595@ 7 .526@ 11 .581@ 4 .664@4 .64@2 .574@4 .69@4

DIVISIVE .44@2 .327@ 2 .231 @ 12 1 @2** 1 @2** 1@ 6 ** 1@ 6** .187@ 2 .813@ 2 * 1@ 3 ** 1@ 3 ** .553@ 4 1 @4** 1@ 5 ** .281 @ 14 .6@ 14 .345@ 7 .458@4 .595@ 5 .66@4 .581 @ 3 .664@3 .64@2 .574@ 3 .69@3

******************************************************************************************************

TABLE 7.38 Highest correlations on axes for unrotated PCA (covariance matrix) of random data (model 1 social identities)

****************************************************************************************************** Positive

Negative

-----------

------------

AXIS 1 ( 9.17 %)

CLANl

( .108)

CLAN2

(-.108 )

AXIS 2 ( 8.59 %)

FEMALE POOR

( .05)

AGED 40+ MALE

(-.033 )

AXIS 3 ( 7.88 %)

AGED 40+ FEMALE

( .14)

MALEPOOR

(-.141 )

AXIS 4 ( 7.65 %)

SUBADULT

( .089)

FEMALE WEALTHY

(-.124)

AXIS 5 ( 7.02 %)

AGED 40+

( .109)

SUBADUL T FEMALE

(-.081 )

AXIS 6 ( 6.9 %)

AGED 40+ FEMALE

( .159)

SUBADULT

(-.102)

AXIS 7 ( 6.51 %)

CLANl

( .108)

CLAN2

(-.108 )

AXIS 8 ( 6.13 %)

SUBADULT MALE

( .08)

MALE WEALTHY

(-.109 )

AXIS 9 ( 5.95 %)

MALEPOOR

( .127)

FEMALE POOR

(-.116)

176

Appendix 1

************************************************************************************************** TABLE 7 .39 Highest correlations on axes for unrotated PCA (covariance matrix) of real Model lA data

************************************************************************************************** Positive

Negative

-----------

------------

AXIS 1 ( 25.83 %) ADULT FEMALE

(.886)

SUBADULT

(-.763)

AXIS 2 (21 %)

(.927)

FEMALE

(-.555)

AXIS 3 ( 15.41 %) CLAN!

(.963)

CLAN2

(-.963)

AXIS 4 ( 8.02 %)

WEALTHY

(.265)

ADULT AVERAGE

(-.235)

AXIS 5 ( 7.36 %)

WEALTHY

(.725)

ADULT AVERAGE

(-.539)

AXIS 6 ( 5.87 %)

SUBADULT FEMALE

(.799)

SUBADUL T MALE

(-.761

ADULT MALE

*************************************************************************** TABLE 8 .1 Rules for artefact distribution in preliminary model

***************************************************************************

Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Artefact

Probability

Social identities

-----------

---------------

----------------------

SCRAPER SCRAPER BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH TEETH STONEAXE STONEAXE ANIMAL ANIMAL MACE MANDIBLE DECPOT DECPOT CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS PENDANTA PENDANTB

70 % prob 30 % prob 70 % prob 30 % prob 70 % prob 30 % prob 30 % prob 70 % prob 40 % prob 70 % prob 40 % prob 70 % prob 90 % prob 70 % prob 90 % prob 70 % prob 80 % prob 40 % prob 80 % prob 40 % prob 80 % prob 40 % prob 80 % prob 40 % prob 100 % prob 100 % prob 70 % prob 40 % prob 75 % prob 50 % prob 25 % prob 70 % prob 30 % prob 70 % prob 30 % prob 70 % prob 50 % prob 30 % prob 20 % prob 10 % prob 50 % prob 50 % prob

FEMALE AGE> 14 MALE AGE> 14 MALE AGE> 14 FEMALE AGE> 14 AGE>14 AGE< 15 AGE> 14 MALE AGE< 15 MALE AGE> 14 WEALTH= 3 FEMALE AGE< 15 FEMALE AGE> 14 WEALTH= AGE< 15 WEALTH= 2 MALE AGE> 14 WEALTH= 1 MALE AGE> 14 WEALTH= 2 FEMALE AGE> 14 WEALTH= FEMALE AGE> 14 WEALTH= FEMALE AGE> 14 WEALTH= FEMALE AGE> 14 WEALTH= FEMALE AGE> 14 WEALTH= MALE AGE> 14 WEALTH= 1 MALE AGE> 14 WEALTH= 1 MALE AGE> 14 WEALTH= 2 MALE AGE> 14 WEALTH= 1 FEMALE AGE> 14 WEALTH= MALE SHAMAN MALE SHAMAN WEALTH= 1 WEALTH= 2 AGE> 39 AGE> 14 AGE< 40 AGE< 15 FEMALE AGE> 39 MALE AGE> 39 MALE AGE> 39 FEMALE AGE> 39 AGE>0 AGE>0 AGE>0 AGE>0 AGE>0 CLAN= 1 CLAN= 2

************************************************************** TABLE 8.2 Summary of rule occurrence in preliminary model (50 burials)

************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10

SCRAPER SCRAPER BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD

Number

Percentage

-----------

--------------

11 burials 7 burials 11 burials 5 burials 23 burials 5 burials 10 burials 8 burials 2 burials 6 burials

( 22 %) ( 14 %) ( 22 %) ( 10 %) ( 46 %) ( 10 %) ( 20 %) ( 16 %) ( 4 %) ( 12 %)

177

3

1 2 1 2 1

1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ******************************************************* TABLE 8.2 (Continued)

******************************************************* Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH TEETH STONEAXE STONEAXE ANIMAL ANIMAL MACE MANDIBLE DECPOT DECPOT CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS PENDANTA PENDANTB

Number

Percentage

-----------

--------------

1 burials 4 burials 5 burials 6 burials 3 burials 6 burials 1 burials 3 burials 2 burials 2 burials 3 burials 4 burials 2 burials 2 burials 1 burials 1 burials 6 burials 11 burials 13 burials 5 burials 5 burials 8 burials 3 burials 9 burials 3 burials 30 burials 24 burials 15 burials 12 burials 9 burials 11 burials 14 burials

( 2 %) ( 8 %) ( 10 %) ( 12 %) ( 6 %) ( 12 %) ( 2 %) ( 6 %) ( 4 %) ( 4 %) ( 6 %) ( 8 %) ( 4 %) ( 4 %) ( 2 %) ( 2 %) ( 12 %) ( 22 %) ( 26 %) ( 10 %) ( 10 %) ( 16 %) ( 6 %) ( 18 %) ( 6 %) ( 60 %) ( 48 %) ( 30 %) ( 24 %) ( 18 %) ( 22 %) ( 28 %)

Total burials with artefacts: 50

*************************************************************** TABLE 8 .3 Summary of rule occurrence in preliminary model ( 100 burials)

*************************************************************** Artefact Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

I 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

SCRAPER SCRAPER BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH TEETH STONEAXE STONEAXE ANIMAL ANIMAL MACE MANDIBLE DECPOT DECPOT CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS PENDANTA PENDANTB

Number

Percentage

-----------

--------------

25 burials 6 burials 23 burials 12 burials 49 burials 8 burials 20 burials 9 burials 2 burials 11 burials 4 burials 5 burials 10 burials 9 burials 7 burials 8 burials 6 burials 5 burials 7 burials 6 burials 9 burials 5 burials 11 burials 2 burials 1 burials 1 burials 18 burials 16 burials 27 burials 10 burials 8 burials 12 burials 6 burials 12 burials 3 burials 65 burials 42 burials 32 burials 30 burials 9 burials 21 burials 33 burials

( 25 %) ( 6 %) ( 23 %) ( 12 %) ( 49 %) ( 8 %) ( 20 %) ( 9 %) ( 2 %) ( 11 %) ( 4 %) ( 5 %) ( 10 %) ( 9 %) ( 7 %) ( 8 %) ( 6 %) ( 5 %) ( 7 %) ( 6 %) ( 9 %) ( 5 %) ( 11 %) ( 2 %) ( 1 %) ( 1 %) ( 18 %) ( 16 %) ( 27 %) ( 10 %) ( 8 %) ( 12 %) ( 6 %) ( 12 %) ( 3 %) ( 65 %) ( 42 %) ( 32 %) ( 30 %) ( 9 %) ( 21 %) ( 33 %)

178

Appendix 1

Total burials with artefacts: 100

*************************************************************** TABLE 8 .4 Summary of rule occurrence in preliminary model (200 burials)

*************************************************************** Artefact

Number

Percentage

------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

SCRAPER SCRAPER BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH TEETH STONEAXE STONEAXE ANIMAL ANIMAL MACE MANDIBLE DECPOT DECPOT CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING RANDOM! RANDOM2 RANDOM3 RANDOM4 RANDOMS PENDANTA PENDANTB

47 burials 25 burials 42 burials 23 burials 86 burials 21 burials 36 burials 28 burials 12 burials 23 burials 6 burials 7 burials 10 burials 28 burials 14 burials 24 burials 10 burials 17 burials 12 burials 3 burials 6 burials 10 burials 8 burials 5 burials 1 burials 1 burials 19 burials 32 burials 60 burials 26 burials 14 burials 24 burials 11 burials 25 burials 17 burials 137 burials 92 burials 56 burials 35 burials 25 burials 32 burials 56 burials

( 23.62 %) ( 12.56 %) (21.11%) (11.56%) ( 43.22 %) ( 10.55 %) ( 18.09 %) ( 14.07 %) ( 6.03 %) (11.56%) ( 3.02 %) ( 3.52 %) ( 5.03 %) ( 14.07 %) ( 7.04 %) ( 12.06 %) ( 5.03 %) ( 8.54 %) ( 6.03 %) ( 1.51 %) ( 3.02 %) ( 5.03 %) ( 4.02 %) ( 2.51 %) ( .5 %) ( .5 %) ( 9.55 %) ( 16.08 %) ( 30.15 %) (13.07%) ( 7.04 %) ( 12.06 %) ( 5.53 %) ( 12.56 %) ( 8.54 %) ( 68.84 %) ( 46.23 %) ( 28.14 %) ( 17.59 %) ( 12.56 %) ( 16.08 %) ( 28.14 %)

Total burials with artefacts: 199

*************************************************************** TABLE 8 .5 Summary of rule occurrence in preliminary model (400 burials)

*************************************************************** Artefact

Number

Percentage

------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

SCRAPER SCRAPER BLADE BLADE PLAINPOT PLAINPOT BONEPIN STBEAD STBEAD SHBEAD SHBEAD BOWL ARROW ARROW BONEAWL BONEAWL SHBRCLT SHBRCLT TEETH TEETH STONEAXE STONEAXE ANIMAL ANIMAL MACE MANDIBLE DECPOT DECPOT

90 burials 46 burials 107 burials 32 burials 181 burials 42 burials 80 burials 46 burials 13 burials 53 burials 17 burials 16 burials 32 burials 41 burials 26 burials 46 burials 25 burials 29 burials 25 burials 16 burials 28 burials 27 burials 24 burials 17 burials 4 burials 4 burials 43 burials 73 burials

( 22.61 %) (11.56%) ( 26.88 %) ( 8.04 %) ( 45.48 %) ( 10.55 %) ( 20.1 %) (11.56%) ( 3.27 %) ( 13.32 %) ( 4.27 %) ( 4.02 %) ( 8.04 %) ( 10.3 %) ( 6.53 %) (11.56%) ( 6.28 %) ( 7.29 %) ( 6.28 %) ( 4.02 %) ( 7.04 %) ( 6.78 %) ( 6.03 %) ( 4.27 %) ( 1.01 %) ( 1.01 %) ( 10.8 %) ( 18.34 %)

179

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************* TABLE 8.5 (Continued)

************************************************************* Artefact

Number

Percentage

------------Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule Rule

29 30 31 32 33 34 35 36 37 38 39 40 41 42

CHARCOAL CHARCOAL CHARCOAL SHELLS SHELLS BNRING BNRING RANDOMl RANDOM2 RANDOM3 RANDOM4 RANDOMS PENDANTA PENDANTB

115 burials 62 burials 31 burials 53 burials 28 burials 53 burials 20 burials 270 burials 191 burials 110 burials 92 burials 40 burials 87 burials 109 burials

( 28.89 %) ( 15.58 %) ( 7.79 %) ( 13.32 %) ( 7.04 %) ( 13.32 %) ( 5.03 %) ( 67.84 %) ( 47.99 %) ( 27.64 %) ( 23.12 %) ( 10.05 %) ( 21.86 %) ( 27.39 %)

Total burials with artefacts: 398

********************************************************************************** TABLE 8 .6 Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 1)

********************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.368@ 2 .333@ 2 .2@ 12 .765 @2 * .667 @2 .5@ 3 .389@ 3 .4@ 10 .565@ 2 .552@ 2 .435 @4 .25@ 2 .5 @9 .273 @4 .5@ 10 .2@ 8 .5@ 10 .571@ 2 .462@4 .625@ 8 .447@2 .429@ 3 .581@ 2 .462@4 .4@ 5

.5 @2 .438@ 2 .125@ 15 .818 @4 * .455@4 .6@2 .4@9 .4@ 14 .429@9 .579@ 8 .526@ 8 .259@ 6 .286@ 13 .308@ 8 .444@ 14 .333@ 5 .429@ 14 .654@6 .467@ 8 .625@ 11 .44@2 .5 @2 .69@4 .571 @ 8 .444@ 8

.455@ 2 .447@3 I @ 13 ** .44@2 .567@ 3 .417@ 9 .3@ 11 .5@ 11 .433@ 3 .611@ 2 .6@3 .357@ 2 .5 @7 .455@ 2 .318@ 4 .143@ 14 .25@ 14 .3@ 3 .4@2 .625@ 7 .415@ 3 .564@ 2 .32@2 .615@ 2 .5@ 3

********************************************************************************* TABLE 8.7 Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 2)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

I )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.357@2 .55@2 .333@ 10 .846@2* .647@2 .375@ 4 .571 @ 7 .5 @7 .444@7 .667@ 3 .778@ 3 * .353@ 3 .5 @9 .2@ 10 .32@ 3 .3@ 5 .4@ 15 .417@ 2 .5@ 6 .462@9 .6@3 .609@3 .568@ 2 .474@5 .562@ 3

.477@4 .5 @2 I@ 6 ** .822@ 2 * .471@ 5 .625@ 2 .571@11 .5@ 11 .444@ 11 .65@9 .667@9 .4@9 .5 @9 .333@ 6 .267@ 2 .222@ 9 .5@4 .5@ 6 .571@ 9 .429@9 .464@9 .523@ 3 .606@5 .444@9 .533@ 9

180

DIVISIVE .421@ 2 .353@ 2 I@ 7 ** .514@ 2 .722@ 3 * .875@ 6 * .455@ 6 .333@ 3 .556@ 6 .6@3 .762@2 * .556@4 .667@ 7 .333@ 7 .316@ 3 .312@ 3 .4@ 13 .429@4 .4@ 3 .5@ 10 .706@ 2 * .615@ 2 .4@2 .429@5 .5@ 2

Appendix 1

********************************************************************************* TABLE 8 .8 Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 3)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2)CLAN2 3 )SHAMAN 4 )ADULT 5)SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT AVERAGE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.333@ 4 .439@ 2 .2@ 14 .8@2 * .682@ 2 .714@ 6 * .7@4* .5@ 15 .538@ 4 .5@5 .556@ 3 .556@ 7 .5@ 14 .6@ 14 .364 @10 .667@ 10 .375@ 8 .484@ 2 .5@7 .357@ 3 .407@ 4 .432@ 2 .645@ 2 .4@5 .448@ 2

.351@ 3 .617@ 2 .25@ 10 .833@ 3 * .933@ 3 ** .714@ 5 * .75@ 14 * .5@ 12 .667@ 3 .531@ 3 .478@ 7 .556@ 6 .5@ 15 .429@ 6 .36@6 .5@7 .222@ 7 .458@7 .281@ 3 .316@ 7 .522@2 .429@ 2 .5@ 3 .3@ 10 .348@ 7

.364@ 7 .489@ 2 .5@ 10 .511@ 2 .6@5 .364@ 11 .556@ 7 .5@ 6 .647@ 5 .317@ 2 .4@8 .5@2 .6@6 .6@3 .412@ 4 .333@ 8 .333@ 8 .35 @2 .3@ 14 .267@ 8 .419@ 2 .419@ 2 .321 @ 2 .333@ 14 .353@ 3

********************************************************************************* TABLE 8 .9 Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 4)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2)CLAN2 3 )SHAMAN 4)ADULT 5 )SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .481 @ 3 .433@ 3 .143@ 11 .486@ 2 .455@2 .353@ 3 .308@ 5 .2@ 15 .368@ 3 .762@ 2 * .533@ 5 .727@ 6 * .333@ 11 .7@6 * .4@8 .667@ 10 .25 @5 .471@ 6 .889@ 6 * .6@5 .552@ 2 .618@ 2 .286@ 2 .4@9 .308@ 5

AVERAGE

DIVISIVE

.531 @ 2 .545@2 .071@ 10 .743@ 2 * .625@2 .333@ 2 .353@ 3 .25 @8 .458@2 .75@ 6 * .533@ 6 .389@ 6 .5@ 9 .438@ 6 .231@ 2 .5@ 5 .286@ 8 .538@ 5 .533@ 10 .667@ 9 .536@ 6 .438@ 2 .406@ 2 .389@ 6 .333@ 11

.5@ 3 .333@ 2 1 @ 15 ** .543@ 2 .52@3 .4@7 .25@3 .286@ 9 .478@ 3 .625@ 2 .314@ 2 .417@ 4 .25@ 8 .5@4 .24@3 .5@ 8 .3@7 .45@4 .667@ 6 .3@ 9 .469@ 2 .514@ 2 .409@ 4 .381 @ 2 .25@6

********************************************************************************* TABLE 8.10 Summary of top Jaccard values for clustering of preliminary model (50 burials, random seed 5)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2)CLAN2 3 )SHAMAN 4)ADULT 5)SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT AVERAGE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE

WARD .621@ 2 .656@ 2 .5@7 .966@ 2 ** .955@ 2 ** .7@3 * .733@ 3 * .5@ 15 .909@ 2 ** .652@ 4 .462@ 4 .8@7 * .2@9 1@ 7 **

AVERAGE

DIVISIVE

.621@ 2 .656@ 2 .5@ 10 .966@ 2 ** .955@ 2 ** .556@ 9 .545@ 2 1@ 5 ** .909@ 2 ** .621@ 2 .583@ 8 .8@ 10 * .2@ 14 1@ 10 **

.56@2 .694@ 2 1@ 5 ** .711@ 2 * .522@ 2 .333@ 6 .462@ 13 25@ 11 .545@2 .447@ 2 .643 @4 .444@ 3 .5@ 12 .5@ 3

181

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************* TABLE 8.10 (Continued)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.417@ 4 .25@4 .333@ 10 .571 @ 2 .474@4 .333 @4 .474@2 .44@3 .571@ 2 .429@2 .4@ 10

.4@ 12 .222@ 14 .333@ 12 .571 @ 2 .357@ 8 .455@ 8 .474@2 .375@ 2 .593 @4 .444@4 .75@ 12 *

.3 @9 .25@ 10 .4@9 .444@2 .444@ 8 .429@4 .524@ 2 .389@ 3 .405 @2 .333@ 3 .5 @9

********************************************************************************* TABLE 8 .11 Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 1)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17)ADULTPOORFEMALE 18 ) ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.355@ 2 .37@ 2 .111@ 13 .761 @2 * .63@2 .375 @4 .421@ 11 .375@ 12 .511@ 2 .568@ 3 .634@ 3 .4@ 8 .556@ 14 .667@ 8 .292@7 .417@ 10 .214@ 9 .517@ 2 .4@ 8 .457@ 3 .404@3 .527@ 3 .59@ 2 .448@3 .486@ 3

.461@ 3 .536@ 2 .053@ 9 .819 @4 * .472@ 4 .333@ 2 .462@4 .571 @ 7 .394@ 4 .5 @9 .647@ 12 .462 @9 .292@ 12 .579@ 9 .207@ 12 .273@ 6 .143@ 13 .429@6 .269@9 .429@ 12 .45@2 .526@ 2 .537 @4 .31 @9 .517@ 12

.366@ 2 .429@2 .5@ 15 .478@ 2 .535@ 3 .333@ 9 .308@ 3 .333@ 11 .439@ 3 .429@4 .486@4 .514@ 2 .583@ 8 .583@ 7 .273@ 2 .261@ 5 .214@ 12 .3 @2 .357@ 15 .333@ 4 .415@ 2 .381@ 2 .345@ 2 .308 @4 .379 @4

********************************************************************************** TABLE 8 .12 Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 2)

********************************************************************************** SOCIAL GROUP 1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17)ADULTPOORFEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .425@2 .303@ 2 .333@ 10 .836@2 * .75@ 2 * .778@ 3 * .588@ 6 .278@ 3 .568@ 2 .55@2 .458@4 .643@ 10 .25@7 .727@ 10 * .286@ 14 .375@ 14 .231@ 11 .472@4 .476@ 11 .407@5 .429@2 .415@ 3 .59@ 2 .397@ 2 .471@ 14

AVERAGE .576@ 2 .424@3 .333@ 6 .903@ 5 ** .441 @4 .882@4 * .706@ 5 * .231 @ 8 .367 @4 .553@ 12 .438@ 6 .643@ 6 .154@ 11 .727@ 6 * .267@ 10 .189@ 12 .182@ 12 .613@ 6 .436@ 12 .346@ 10 .541@ 3 .529 @4 .571 @ 12 .452@ 12 .238@ 6

182

DIVISIVE .41 @2 .324@ 2 .4@ 12 .676@ 2 .829@ 6 * .647@7 .667@9 .267@ 6 .6@6 .459@2 .377 @4 .286@ 12 .167@ 3 .4@ 12 .4@ 13 .4@ 13 .308@ 11 .444@2 .316@ 13 .261@ 3 .395@ 2 .343@ 2 .661@ 2 .4@2 .368@ 8

Appendix 1

********************************************************************************* TABLE 8.13 Summary of top Jaccard values for clustering of preliminary model ( 100 burials, random seed 3)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2)CLAN2 3 )SHAMAN 4 )ADULT 5 )SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.368@ 2 .427@ 2 .071@ 9 .845@ 2 * .725@ 2 * .706 @4 * .6@4 .333@ 8 .625@ 2 .795@ 3 * .576@ 3 .579@ 9 .2@ 12 .688@ 9 .364@ 5 .333@ 5 .28@7 .468@ 2 .341@ 3 .636@ 3 .614@ 3 .449@ 4 .538@ 2 .6@3 .333@ 3

.53@2 .514@ 5 .071@ 12 .915@ 5 ** .429@ 5 .615@ 3 .474@ 8 .333@ 7 .394@ 5 .667@ 6 .545@ 6 .579@ 12 .214@ 11 .688@ 12 .452@ 15 .231@ 15 .308@ 15 .433@ 5 .298@ 6 .526@ 11 .531 @ 6 .527@ 3 .567@ 5 .489@ 6 .4@6

.58@2 .351@ 2 .2@ 14 .55@2 .526@ 3 .75@ 5 * .636@ 4 .222@ 11 .456@ 3 .385@ 2 .639@ 3 .55@ 2 .25@ 10 .647@2 .423@ 6 .25@ 13 .368@ 6 .406@ 6 .357@ 5 .765@ 6 * .552@ 4 .565@ 2 .305@ 2 .345@ 2 .375@ 10

********************************************************************************* TABLE 8 .14 Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 4)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2)CLAN2 3 )SHAMAN 4 )ADULT 5)SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT AVERAGE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22 )FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.479@ 2 .406@ 2 .1@ 10 .828@ 2 * .761@ 2 * .625@ 3 .618@ 3 .308@ 11 .543@2 .609@ 8 .61 @2 .368@ 12 .538@ 12 .4@8 .25@ 15 .182@ 13 .273@ 15 .475@ 2 .333@ 10 .351@ 2 .378@ 8 .488@ 2 .533@ 2 .579@ 8 .429@ 4

.533@ 5 .48@2 .1@ 15 .81@ 6 * .609@ 6 .625@ 14 .395@ 6 .308@ 3 .524@ 6 .455@ 15 .683@ 15 .425@ 13 .367@ 15 .6@ 15 .176@ 13 .167@ 4 .167@ 13 .5@6 .3@9 .417@15 .367@ 2 .626@ 2 .509@ 8 .562@ 15 .342@ 15

.42@2 .429@ 2 .125@ 7 .426@2 .776@ 3 * .625@4 .649@4 .214@ 5 .571@ 3 .586@ 3 .667@ 2 .36@6 .474@ 6 .4@7 .261@ 7 .222@ 12 .2@ 10 .357@ 2 .308@ 3 .469@ 2 .493@ 2 .429@ 4 .429@ 3 .5@ 3 .429@ 6

********************************************************************************* TABLE 8.15 Summary of top Jaccard values for clustering of preliminary model (100 burials, random seed 5)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2)CLAN2 3 )SHAMAN 4)ADULT 5)SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT AVERAGE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE

.322@ 2 .539@ 2 .056@ 10 .44@2 .421@ 2 .308@ 4 .419@ 3 .273@ 15 .289@2 .622@ 2 .414@ 3 .348@ 10 .333@ 11 .444@ 10

.415@ 2 .55@ 2 1@ 8 ** .753@ 3 * .405@ 3 .333@ 2 .406@ 3 .25@5 .324@ 3 .718@ 11 * .406@ 9 .304@ 15 .2@9 .389@ 15

.325@ 2 .471@ 2 .5@ 14 .406@2 .509@ 3 .263@ 13 .318@ 12 .333@ 14 .431@ 3 .417@4 .351 @ 4 .278@2 .375@ 10 .333@ 8

183

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************* TABLE 8.15 (Continued)

********************************************************************************* 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.25@ 7 .5@ 14 .308@ 11 .261@ 2 .4@2 .269@3 .451@ 2 .636@ 2 .341@ 2 .387@ 2 .5 @4

.296@ 15 .412@ 15 .2@9 .423@ 3 .4@ 11 .278@ 12 .557@ 9 .516@ 2 .535@ 3 .471@ 11 .5 @9

.348@ 15 .667@ 15 .308@ 5 .396@ 2 .5 @4 .308 @4 .376@ 2 .464@3 .339@ 2 .318@ 11 .323 @4

********************************************************************************* TABLE 8 .16 Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed I)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 ) ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .31@ 2 .518@ 2 .038@ 14 .846@2* .718 @2 * .392@4 .537 @4 .278@ 7 .61 @2 .567@ 3 .603@ 3 .314@ 3 .366@ 3 .276@ 14 .451@ 6 .379@ 9 .36@9 .457@2 .568@ 5 .458@ 3 .583@ 3 .38@ 3 .539@ 2 .48@5 .489@ 10

AVERAGE .387@ 2 .579@ 2 .011@9 .876@4 * .671@ 4 .479@4 .342@ 2 .364@ 13 .671@ 4 .46@4 .514@ 8 .281@ 12 .184@ 12 .133@ 8 .208@4 .174@ 5 .278@ 12 .504@ 5 .267 @4 .267@ 8 .546@ 2 .422@2 .676@ 7 .355@ 12 .36@ 8

DIVISIVE .37@ 2 .407@2 .059@ 15 .581 @ 2 .714@ 3 * .379@ 6 .485 @4 .208@6 .619@ 3 .349@ 3 .547@ 2 .278@ 10 .4@ 10 .455@ 15 .439@ 8 .189@ 8 .367@ 8 .388@ 2 .389@ 8 .429@5 .5 @2 .382@ 2 .475@ 2 .306@ 5 .388@ 5

********************************************************************************* TABLE 8 .17 Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 2)

********************************************************************************* SOCIAL GROUP

WARD

I )CLAN! 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17)ADULTPOORFEMALE 18 ) ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.352@ 3 .468@2 .231@ 9 .9@ 2 ** .843@ 2 * .574@ 6 .429@3 .296@ 6 .671@ 2 .632@4 .448@2 .406@9 .433 @4 .722@9 * .362@ 8 .2@ 12 .239@ 5 .504 @2 .374@4 .357@ 4 .455 @4 .361@ 2 .579@ 2 .398 @4 .291@ 5

AVERAGE .427@2 .578@ 5 .027@ 12 .885 @9 * .692@9 .487@ 14 .532@ 14 .294@ 7 .657@ 9 .513@ 12 .444@9 .277@ 12 .126@ 12 .152@ 12 .202@9 .079@ 12 .182@ 12 .509@ 12 .301 @ 12 .219@ 12 .52@2 .475@ 2 .602@9 .345@ 12 .289@ 9

184

DIVISIVE .429@2 .449@2 .333@ 14 .43@2 .74 @4 * .622@5 .561@ 6 .25@ 10 .573 @4 .652@ 2 .476@ 3 .469@7 .643@ 9 .833 @7 * .455 @4 .45@ 11 .25@ 11 .415@ 7 .73@ 7 * .581 @ 9 .417@ 2 .618@ 2 .301@ 2 .448@2 .255@ 2

Appendix 1

********************************************************************************* TABLE 8.18 Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 3)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2)CLAN2 3 )SHAMAN 4 )ADULT 5 )SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .407@ 2 .376@ 2 .074@ 10 .827@ 2 * .714@ 2 * .429@ 4 .456@ 2 .292@ 8 .6@2 .73@ 3 * .648@ 3 .372@ 5 .533@ 5 .429@ 10 .286@ 10 .323@ 10 .267@ 14 .429@ 2 .411@3 .354@ 3 .516@ 3 .524@ 3 .504@2 .432@ 10 .5@3

AVERAGE .495@ 4 .511@ 5 .027@ 13 .811@ 7 * .5@7 .543@ 8 .34@8 .25 @8 .384@ 7 .59@ 13 .716@ 13 * .35@ 14 .447@ 14 .176@ 13 .182@ 13 .167@ 13 .316@ 14 .413@ 7 .291@ 13 .373@ 14 .491 @ 13 .574@2 .485@7 .333@ 13 .522@ 13

DIVISIVE .448@ 2 .418@ 2 .1@ 9 .397@ 2 .613@ 4 .31 @ 10 .444@ 5 .25@6 .489@ 4 .534@ 3 .691@ 2 .441@ 7 .714@ 7 * .333@ 3 .321@ 4 .231@ 8 .172@ 10 .403@ 7 .513@ 3 .692@7 .572@ 2 .42@2 .333@ 2 .357@ 3 .475@ 2

********************************************************************************* TABLE 8.19 Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 4)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2)CLAN2 3 )SHAMAN 4 )ADULT 5)SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT AVERAGE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22 )FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .375@ 2 .379@ 2 .083@ 12 .802@ 2 * .765@ 2 * .463@ 3 .386@ 2 .4@ 14 .634@2 .662@4 .484@ 4 .28@ 12 .25@ 15 .438@ 12 .3@7 .27@7 .273@ 8 .577@2 .512@ 7 .478@ 4 .442@ 4 .374@ 2 .583@ 2 .478@ 4 .412@ 4

AVERAGE .457@ 2 .521 @ 2 .067@ 8 .85@4* .722 @4 * .422@ 4 .45@ 12 .273@ 12 .583@ 4 .468@ 4 .52@ 8 .296@ 8 .182@ 11 .444@ 8 .292@ 14 .158@ 14 .172@ 14 .57@4 .373@ 14 .478@ 14 .5@ 2 .477@ 2 .625@9 .448@ 14 .44@ 14

DIVISIVE .467@ 2 .367@ 2 .2@ 14 .608@ 2 .832@ 3 * .65@6 .775 @4 * .4@ 15 .674@ 3 .578@ 2 .453@ 3 .35@ 10 .231@ 12 .636@ 10 .339@ 5 .417@ 11 .37@9 .363@ 2 .625@ 10 .32@ 3 .425@ 4 .503@ 2 .431@ 2 .375@ 10 .429@ 3

********************************************************************************** TABLE 8 .20 Summary of top Jaccard values for clustering of preliminary model (200 burials, random seed 5)

********************************************************************************** SOCIAL GROUP 1 )CLANl 2)CLAN2 3 )SHAMAN 4 )ADULT 5)SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT AVERAGE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR

WARD .379@ 2 .408@ 2 .1@ 12 .817@2* .69@2 .447@ 4 .582@ 4 .444@ 9 .697@ 2 .5@3 .437@ 2 .429@ 3 .6@3 .471 @ 8 .413@ 6

AVERAGE .462@ 2 .549@ 5 .067@ 15 .846@ 7 * .776@ 7 * .514@ 8 .607@ 8 .205@ 11 .624@7 .515@ 12 .487@ 9 .417@ 12 .577@ 12 .114@ 15 .347@ 12

185

DIVISIVE .442@ 2 .473@ 2 .071@ 12 .485@2 .649@ 3 .543@ 6 .472@ 6 .158@ 13 .587@ 4 .627@ 3 .484@ 2 .519@ 11 .824@ 11 * .429@ 12 .436@ 5

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************* TABLE 8.20 (Continued)

********************************************************************************* SOCIAL GROUP 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 ) ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .214@ 6 .242@6 .409@2 .28@ 3 .195@ 3 .468@ 3 .387@ 2 .463@ 2 .277@ 12 .248@2

AVERAGE .189@ 12 .182@ 12 .424@7 .273@ 15 .22@ 12 .487@ 2 .515@ 3 .544@9 .333@ 12 .3 @9

DIVISIVE .415@ .161@ .295@ .421@ .536@ .574@ .399@ .323@ .379@ .241@

5 4 3

5 11 2 3 3 3 2

********************************************************************************* TABLE 8 .21 Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 1)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17)ADULTPOORFEMALE 18 ) ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .384@ 2 .465@2 .071@ 10 .853 @2 * .69@2 .446@5 .587@ 5 .302@ 12 .551@ 2 .601@ 3 .579@ 3 .333@ 10 .594@ 15 .483@ 10 .427@6 .23@6 .257@ 8 .429@2 .324@4 .588@ 15 .577@ 3 .391@ 2 .519@ 2 .444@4 .398@ 3

AVERAGE

DIVISIVE

.464@4 .538@ 2 .074@ 15 .869@5* .694@5 .424@7 .596@ 7 .195@ 7 .58@ 5 .611@ 11 .554@ 11 .341 @ 15 .382@ 11 .5@ 15 .394@ 15 .206@ 15 .222@ 15 .462@ 10 .326@ 11 .466@ 11 .518@ 2 .48@2 .548@ 5 .361@ 11 .382@ 11

.419@ 2 .481@ 2 .174@ 9 .477@2 .614@4 .43@7 .554@ 5 .148@ 11 .543 @4 .518@ 3 .595@ 2 .4@ 8 .889@ 8 * .697 @9 .348 @4 .221@ 10 .292@ 10 .351@ 8 .539@ 9 .719@ 8 * .629@2 .373@ 2 .301 @2 .33@3 .417@ 2

********************************************************************************** TABLE 8 .22 Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 2)

********************************************************************************** SOCIAL GROUP

WARD

AVERAGE

1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.347@ 2 .381 @ 2 .063@ 5 .844@2* .77@ 2 * .494@4 .504@4 .304@ 8 .653@ 2 .723@ 3 * .652@ 3 .313@ 9 .5 @9 .446@5 .216@ 5 .227@ 5 .244@7 .519@ 2 .444@3 .435@ 3 .478@ 3 .442@3 .604@2 .474@3 .5@ 3

.471@ 2 .52@2 .019@ 15 .834@8* .639@ 10 .445@ 10 .309@ 10 .448@ 13 .547@ 13 .453@ 8 .462@ 15 .293@ 15 .15@ 15 .144@ 15 .163@ 8 .091@ 8 .119@ 9 .485@ 8 .25@ 8 .255@ 15 .523@ 2 .496@4 .625@ 8 .308@ 8 .347@ 15

186

DIVISIVE .411@ 2 .493@ 2 .1@ 11 .461@ 2 .65@3 .716@4 * .543@ 5 .179@15 .52@ 3 .63 @3 .574@ 2 .29@ 11 .403 @2 .581@ 11 .358@ 6 .368@ 6 .25@ 10 .261@ 2 .461@11 .466@2 .632@ 2 .439@4 .303@ 3 .4@ 3 .515@ 2

Appendix 1

********************************************************************************* TABLE 8.23 Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 3)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2)CLAN2 3 )SHAMAN 4 )ADULT 5 )SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD

AVERAGE

.377@ 2 .426@ 2 .052@ 5 .873@ 2 * .763@ 2 * .643 @4 .688@ 4 .4@ 10 .633@ 2 .556@ 3 .474@ 3 .438@ 3 .612@ 3 .338@ 5 .408@ 6 .256@ 12 .283@ 11 .419@ 2 .379@ 5 .232@ 3 .478@ 3 .406@ 2 .527@2 .347@ 5 .371@ 3

DIVISIVE

.51 @2 .485@ 3 .025@ 14 .822@ 7 * .496@ 5 .771@ 5 * .506@ 10 .16@ 10 .492@ 5 .593@ 14 .44@7 .433@ 14 .65@ 14 .182@ 14 .28@ 14 .18@ 14 .143@ 11 .373@ 7 .285@ 14 .202@ 14 .516@ 2 .549@ 5 .583@ 7 .387@ 14 .337@ 14

.439@ 2 .442@ 2 .15@ 10 .447@ 2 .603@4 .701@ 5 * .463@ 8 .146@ 7 .542@ 4 .485@ 3 .644@2 .535@ 6 .927@ 6 ** .633@ 10 .376@ 4 .281@ 7 .19@4 .387@ 6 .588@ 10 .821@ 6 * .648@ 2 .434@ 2 .283@ 2 .337@ 3 .434@ 2

********************************************************************************* TABLE 8.24 Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 4)

********************************************************************************* SOCIAL GROUP 1 )CLANl 2)CLAN2 3 )SHAMAN 4)ADULT 5 )SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD

AVERAGE

.337@ 2 .468@ 2 .075@4 .864@ 2 * .743@ 2 * .59@5 .549@ 5 .171@ 8 .592@2 .535@ 3 .507@ 3 .296 @4 .39@ 3 .533@ 4 .377@ 4 .185@ 4 .253@ 6 .438@ 2 .258@ 3 .402@ 3 .447@ 3 .444@ 2 .565@ 2 .347@ 3 .436@ 3

DIVISIVE

.504@ 2 .5@3 .03@ 14 .863@ 7 * .664@7 .5@ 10 .723@ 10 * .194@ 7 .538@ 7 .531 @ 14 .587@ 14 .318@ 15 .436@ 15 .24@ 14 .331@ 15 .142@ 15 .233@ 15 .444@ 8 .371@ 14 .37@ 15 .465@ 2 .54@5 .529@ 7 .39@ 14 .395@ 15

.445@ 2 .437@ 2 .091@ 9 .466@ 2 .523@4 .318@ 8 .415@ 8 .129@ 8 .458@ 4 .549@ 3 .568@ 2 .323@ 9 .472@ 12 .724@ 9 * .31 @4 .159@ 4 .266@ 6 .311@ 2 .641@9 .486@ 2 .594@ 2 .396@ 2 .304@ 2 .366@ 3 .403@ 2

****************************************************************************************** TABLE 8 .25 Summary of top Jaccard values for clustering of preliminary model (400 burials, random seed 5)

****************************************************************************************** WARD 1 )CLANl 2)CLAN2 3 )SHAMAN 4)ADULT 5)SUBADULT 6 ) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT AVERAGE 9) SUBADULT POOR 10 )ADULT MALE 11 )ADULT FEMALE 12)WEALTHY 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE

AVERAGE

.328@ 2 .361 @ 2 .059@ 3 .795@ 2 * .737@ 2 * .632@ 4 .41@4 .2@4 .591@ 2 .492@ 3 .599@ 3 .323@ 5 .455@5 .397@ 3

.474@ 2 .523@ 3 .2@7 .859@ 8 * .633@ 8 .432@ 12 .434@ 12 .163@ 6 .579@ 8 .471 @ 13 .438@ 8 .275@ 15 .158@ 15 .12@ 15

187

DIVISIVE .43@ 2 .46@2 .13 @9 .435@ 2 .586@ 3 .462@ 5 .461@ 7 .128@ 4 .486@ 4 .543@ 3 .637@ 2 .381@ 10 .667@ 10 .815@ 9 *

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ******************************************************************************************* TABLE 8.25 (Continued)

******************************************************************************************* .286@ 15 .472@ 15 .135@ 12 .478@ 2 .376@ 3 .354@ 3 .34@ 2 .416@ 3 .62@2 .343@ 3 .441@ 3

15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.177@ 8 .1@ 8 .086@ 13 .508@ 8 .288@ 13 .249@ 15 .529 @4 .484@2 .581@ 8 .305@ 14 .302@ 15

.297@ 12 .41@ 12 .185@ 8 .346@ 9 .63@9 .667@ 10 .639@ 2 .405 @2 .275@ 2 .388@ 3 .43@2

************************************************************************************************** TABLE 8.26 Summary of average Jaccard values for clustering of preliminary model (5 repetitions, 4 sample sizes)

************************************************************************************************** SOCIAL GROUPS

WARD

AVER

1 )CLANl

.432 .39 .365 .355

.496 .503 .446 .485

.46 .42 .431 .429

50 100 200 400

2 )CLAN2

.482 .409 .43 .42

.551 .501 .548 .513

.463 .401 .423 .463

50 100 200 400

3)SHAMAN

.275 .134 .105 .064

.389 .311 .04 .07

.9 ** .345 .153 .129

50 100 200 400

4)ADULT

.773 .742 .838 .846

.544 .507 .5 .457

50 100 200 400

5)SUBADULT

.681 .657 .746 .741

MONO

SAMPLE

* * * *

.836 .84 .854 .849

* *

.688 .471 .672 .625

.586 .635 .71 * .595

50 100 200 400

* * * *

6 ) SUBADULT MALE

.528 .558 .461 .561

.566 .558 .489 .514

.478 .524 .501 .525

50 100 200 400

7 )SUBADULTFEMALE

.54 .529 .478 .548

.524 .489 .454 .514

.405 .516 .547 .487

50 100 200 400

8 ) SUBADULT A VERA GE

.42 .313 .342 .275

.53 .339 .277 .232

.374 .274 .253 .146

50 100 200 400

9 ) SUBADULT POOR

.565 .507 .642 .604

.581 .401 .584 .547

.532 .499 .588 .51

50 100 200 400

10 )ADULT MALE

.627 .629 .618 .581

.626 .579 .509 .532

.52 .455 .548 .545

50 100 200 400

11 )ADULT FEMALE

.553 .538 .524 .562

.557 .544 .536 .496

.544 .504 .53 .604

50 100 200 400

12 )WEALTHY

.537 .468 .36 .341

.481 .483 .324 .332

.455 .398 .411 .386

50 100 200 400

13 ) WEAL THY FEMALE

.407 .375 .436 .51

.397 .245 .303 .355

.503 .37 .562 .672

50 100 200 400

14 ) WEAL THY MALE

.555 .585 .467

.502 .597 .204

.478 .473 .537

50 100 200

188

Appendix 1 *************************************************************************************************** TABLE 8.26 (Continued)

*************************************************************************************************** SOCIAL GROUPS

WARD

AVER

MONO

SAMPLE

.439

.237

.69

400

15 )ADULT POOR

.400 .288 .362 .343

.34 .28 .246 .269

.317 .341 .398 .338

50 100 200 400

16 )ADULT POOR MALE

.417 .361 .277 .274

.355 .254 .153 .144

.308 .36 .34 .288

50 100 200 400

17 )ADULT POOR FEMALE

.372 .261 .276 .234

.354 .2 .226 .161

.337 .28 .264 .237

50 100 200 400

18 )ADULT AVERAGE

.503 .439 .475 .457

.544 .48 .484 .454

.395 .381 .373 .331

50 100 200 400

19 )ADULT AVERAGE MALE

.565 .39 .429 .356

.442 .341 .301 .304

.442 .368 .536 .572

50 100 200 400

20)ADULTAVERAGEFEMALE

.475 .424 .368 .402

.498 .399 .311 .308

.424 .427 .512 .632

50 100 200 400

21 )MALE

.496 .455 .493 .464

.487 .489 .509 .51

.507 .446 .498 .628

50 100 200 400

22)FEMALE

.506 .503 .405 .42

.453 .545 .493 .51

.5 .436 .464 .409

50 100 200 400

23 )AGED 40+

.53 .518 .534 .567

.559 .544 .586 .573

.371 .416 .373 .293

50 100 200 400

24 ) AGED 40+ MALE

.433 .482 .413 .391

.43 .457 .363 .35

.418 .374 .373 .364

50 100 200 400

25 ) AGED 40+ FEMALE

.424 .444 .388 .429

.482 .399 .382 .353

.421 .375 .358 .44

50 100 200 400

************************************************************************************************************************** TABLE 8.27 Summary of highest correlations in PCNCA analyses of preliminary model (4 sample sizes, 5 repetitions; CA=correspondence analysis, PCA=principal components analysis)

************************************************************************************************************************** SAMPLE50

CAl

CA2

CA3

CA4

CAS

PCAl

PCA2

PCA3

PCA4

PCAS

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULT MALE 7 SUBAD FEMALE 8 SUBAD A VERA GE 9 SUBADULTPOOR 10 ADULT MALE 11 ADULT FEMALE 12 WEALTHY 13 WEALTHY FMALE 14 WEALTHY MALE 15 ADULT POOR 16 ADULT POOR MALE 17 ADULTPOORFMALE 18 ADULT AVERAGE

.416 -.416 .51 -.799 .799 .696 .588 -.641 .662 .463 -.56 -.41 -.327 .455 -.302 -.288 -.239 -.446

.508 -.508 -.556 -.871 .871 .811 .667 .538 .597 .55 -.457 -.566 -.553 -.497 .418 .393 -.297 -.358

-.404 .404 -.467 -.824 .824 .483 .597 .514 .574 -.568 .631 -.379 .385 -.478 .347 .41 -.315 -.346

.597 -.597 -.368 -.741 .741 .667 .506 -.465 .615 -.553 .601 -.399 .22 -.346 .384 -.322 .407 -.443

.509 -.509 .467 -.877 .877 .692 .636 .593 .817 -.646 -.698 -.444 -.185 .537 -.327 -.341 -.342 -.594

.648 -.648 .958 -.525 .525 .467 .67 .887 .463 .618 -.663 .485 -.607 .524 -.242 -.209 .283 .328

.667 -.667 .772 -.51 .51 .557 .721 .599 .441 .562 .61 .826 .836 .484 -.285 -.261 -.281 .502

-.513 .513 .959 -.607 .607 .429 .518 .518 .417 -.467 .727 .717 .663 .571 -.341 .375 -.265 -.539

-.694 .694 .96 .513 -.513 -.497 -.384 -.277 -.41 .708 -.551 -.848 -.44 -.771 -.284 -.416 .386 .566

-.535 .535 .926 .605 -.605 -.676 .591 .857 -.612 -.632 .76 .791 .431 .866 -.388 -.519 .34 .483

189

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************************************************************** TABLE 8.27 (continued)

************************************************************************************************************************** 19 20 21 22 23 24 25

ADULTAVERAGEMALE ADULT AVERAGEFMALE MALE FEMALE AGED 40+ AGED 40+ MALE AGED 40+ FEMALE

-.485 -.522 -.511 .511 -.505 -.496 -.447

.438 .316 -.574 .574 -.558 .434 -.401

-.338 .431 .573 -.573 -.487 -.408 .502

.451 .633 -.548 .548 -.336 -.35 .419

-.675 -.659 .496 -.496 -.595 -.488 -.529

.444 -.519 -.498 .498 -.498 .622 -.552

.725 -.475 -.472 .472 .563 .639 .557

-.49 .449 -.573 .573 -.63 -.605 .77

.715 -.571 -.498 .498 .559 .497 .682

.597 .71 -.573 .573 -.682 -.655 .639

SAMPLE 100

CAl

CA2

CA3

CA4

CA5

PCAl

PCA2

PCA3

PCA4

PCA5

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBAD FEMALE 8 SUBAD AVERAGE 9 SUBADULT POOR 10 ADULT MALE 11 ADULT FEMALE 12 WEALTHY 13 WEALTHY FEMALE 14 WEALTHY MALE 15 ADULT POOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULT AVERAGEMALE 20 ADULT AVER FEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FMALE

-.343 .343 .471 -.633 .633 .567 .5 .591 -.464 -.346 -.405 .347 .333 .36 -.242 -.218 .228 -.306 -.274 -.377 -.518 .518 -.382 .262 -.347

.466 -.466 -.395 -.775 .775 .59 -.606 .443 -.566 -.596 .474 -.49 .396 -.433 -.378 -.262 -.313 -.379 -.565 .384 -.556 .556 -.47 -.521 .242

-.357 .357 -.509 -.672 .672 .767 .703 .722 .518 -.472 .684 -.408 .205 -.432 -.301 .208 -.227 .327 -.253 .63 -.737 .737 -.455 -.406 .388

.348 -.348 .396 -.77 .77 .656 .668 .468 .519 .602 -.628 -.36 -.475 .376 .324 .237 .206 -.474 .488 -.405 -.569 .569 -.461 .451 -.309

-.468 .468 -.507 -.738 .738 -.53 .528 .548 .435 -.408 .529 -.403 .261 .351 -.356 -.269 -.361 -.422 .315 .356 -.696 .696 -.479 -.286 .445

.699 -.699 .966 .488 -.488 -.453 .629 -.407 -.374 .652 -.571 .67 -.784 .838 .3 .257 .197 .357 .302 .475 .421 -.421 .514 .43 .468

-.693 .693 .969 .495 -.495 -.578 .492 .598 -.431 .711 -.567 .619 -.391 .685 .3 .398 .252 .472 .492 -.486 .501 -.501 .539 .499 -.376

-.441 .441 .97 .441 -.441 .651 -.401 -.733 -.409 .575 .678 .8 .448 .818 -.303 .257 -.286 .438 .365 .757 .472 -.472 .409 .445 .391

.473 -.473 -.942 -.597 .597 -.701 .468 .481 .483 .692 -.777 -.55 -.689 .527 -.239 -.199 .246 -.385 .565 -.465 -.546 .546 -.626 .559 -.436

-.643 .643 .951 -.543 .543 -.454 .476 .671 .405 .711 .575 -.711 .595 -.729 -.227 -.39 -.33 -.402 .537 .477 .58 -.58 -.489 .502 .489

SAMPLE200

CAl

CA2

CA3

CA4

CA5

PCAl

PCA2

PCA3

PCA4

PCA5

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBAD FEMALE 8 SUBAD AVERAGE 9 SUBADULT POOR 10 ADULT MALE 11 ADULT FEMALE 12 WEALTHY 13 WEALTHY FMALE 14 WEALTHY MALE 15 ADULT POOR 16 ADPOORMALE 17 AD POOR FMALE 18 ADULT AVER 19 ADAVMALE 20 ADAVFMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FMALE

.492 -.492 .416 -.825 .825 .561 .656 .531 .721 .589 -.553 -.406 -.32 .425 -.19 .243 -.239 -.541 .493 -.425 .581 -.581 -.524 .457 -.363

.382 -.382 .366 -.777 .777 .646 .565 .455 .596 -.471 -.382 -.431 .363 -.377 -.249 -.236 .282 -.404 -.423 -.36 -.551 .551 -.46 -.422 .271

-.531 .531 .403 -.755 .755 -.569 .562 .623 -.506 .639 -.644 .346 -.41 .442 -.247 -.164 .258 -.437 .46 -.414 .712 -.712 -.429 .413 -.445

.367 -.367 .407 -.851 .851 .526 .611 .326 .712 -.457 -.482 -.393 .234 .448 .207 -.185 -.243 -.56 -.341 -.374 .691 -.691 -.547 -.338 -.378

-.535 .535 .489 -.775 .775 -.588 .509 .402 .687 .756 -.586 -.572 -.526 .394 -.512 -.305 -.374 -.406 .578 -.333 .687 -.687 -.443 .502 -.325

-.59 .59 .986 .483 -.483 -.386 -.61 -.512 .465 -.656 -.685 -.625 -.707 -.604 .275 .302 .196 .477 -.531 -.435 .464 -.464 .716 -.505 .51

.651 -.651 -.962 .564 -.564 -.429 .527 .438 .415 .444 .64 .728 .703 .691 .296 -.215 .298 -.33 .367 .436 -.503 .503 .726 .484 .402

.69 -.69 .978 -.5 .5 -.535 .479 .685 -.444 .745 .65 .665 .758 .646 -.258 -.264 .231 -.362 .53 -.356 -.571 .571 -.74 .503 -.603

.439 -.439 .96 .641 -.641 .492 -.486 .663 -.578 -.65 .738 -.699 .595 -.71 .228 .204 .256 .542 .442 .592 -.435 .435 .761 .6 .567

.679 -.679 .976 .563 -.563 -.425 .631 .687 -.53 .659 .605 .753 .807 .652 .254 -.293 .242 .397 .52 .287 -.437 .437 .729 .364 .55

SAMPLE400

CAl

CA2

CA3

CA4

CA5

PCAl

PCA2

PCA3

PCA4

PCA5

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBAD FEMALE 8 SUBADAVER 9 SUBADULT POOR 10 ADULT MALE 11 ADULT FEMALE 12 WEALTHY 13 WEALTHY FMALE 14 WEALTHY MALE 15 ADULT POOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULT AVERAGEMALE

-.486 .486 .376 -.842 .842 -.567 .554 -.594 .712 -.472 -.497 -.452 -.31 .343 .349 .284 .18 -.46 -.311

-.446 .446 .608 -.839 .839 .566 .517 .542 .675 -.479 -.557 -.449 .29 .443 .278 .209 .197 -.484 -.493

-.426 .426 .425 -.818 .818 .603 .586 .525 .668 .611 -.665 -.487 -.41 .465 -.311 -.186 -.26 -.414 .408

-.487 .487 .455 -.804 .804 .517 .619 -.609 .629 -.467 .582 -.446 .295 -.353 .246 .197 -.181 -.432 -.319

-.402 .402 -.453 -.804 .804 -.599 .524 .485 .602 .519 -.539 -.424 -.37 .381 .243 .228 .143 -.476 .408

.68 -.68 .979 -.695 .695 -.483 .575 .619 .613 .778 -.535 .741 .747 .688 -.296 -.238 .255 -.498 .498

-.67 .67 .966 .655 -.655 .644 -.433 .513 -.546 .801 .572 -.776 -.692 .621 .23 -.182 -.282 .428 .541

-.684 .684 .973 -.586 .586 -.625 .444 .667 -.564 .75 .475 .82 .784 .571 -.31 -.248 .233 -.336 .548

.704 -.704 .985 -.671 .671 .485 -.518 .385 .507 -.722 -.589 .731 .708 -.663 -.286 .188 -.22 -.438 -.465

-.655 .655 .991 .569 -.569 -.705 -.558 .423 -.491 .741 -.444 -.821 -.735 .616 .275 .189 .239 .524 .512

190

Appendix 1 ************************************************************************************************************************* TABLE 8.27 (continued)

************************************************************************************************************************* 20 21 22 23 24 25

ADLTAVERAGEFEMALE MALE FEMALE AGED 40+ AGED 40+ MALE AGED 40+ FMALE

-.437 -.66 .66 -.547 -.38 -.362

-.497 -.614 .614 -.583 -.358 -.493

-.436 .606 -.606 -.508 .462 -.493

.375 -.75 .75 -.461 -.303 .401

-.394 -.654 .654 -.471 .334 -.35

-.464 -.561 .561 -.728 .563 -.558

.472 .548 -.548 .733 .589 .624

.288 -.601 .601 .743 .566 .517

-.46 -.507 .507 .549 -.499 -.465

.386 -.574 .574 .754 .513 .426

****************************************************************************************************************** TABLE 8.28 Average point-biserial correlations for social groups in PCNCA (CA=Correspondence analysis,PCA=principal components analysis)

analyses of preliminary model (4 sample sizes)

****************************************************************************************************************** SAMPLE SIZE

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULT MALE 7 SUBADULTFEMALE 8 SUBADULT AVERAGE 9 SUBADULTPOOR 10 ADULT MALE 11 ADULT FEMALE 12 WEALTHY 13 WEALTHY FEMALE 14 WEALTHY MALE 15 ADULT POOR 16 ADULTPOORMALE 17 ADULT POOR FEMALE 18 ADULT AVERAGE 19 ADULT AVERAGE MALE 20 ADULT AVERAGEFEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

50

50

100

100

200

200

400

400

CA

PCA

CA

PCA

CA

PCA

CA

PCA

.487 .487 .474 .822 .822 .67 .599 .55 .653 .556 .589 .44 .334 .463 .356 .351 .32 .437 .477 .512 .54 .54 .496 .435 .46

.611 .611 .915 .552 .552 .525 .577 .628 .469 .597 .662 .733 .595 .643 .308 .356 .311 .484 .594 .545 .523 .523 .586 .604 .64

.396 .396 .456 .718 .718 .622 .601 .554 .5 .485 .544 .402 .334 .39 .32 .239 .267 .382 .379 .43 .615 .615 .449 .385 .346

.59 .59 .96 .513 .513 .567 .493 .578 .42 .668 .634 .67 .581 .719 .274 .3 .262 .411 .452 .532 .504 .504 .515 .487 .432

.461 .461 .416 .797 .797 .578 .581 .467 .644 .582 .529 .43 .371 .417 .281 .227 .279 .47 .459 .381 .644 .644 .481 .426 .356

.61 .61 .972 .55 .55 .453 .547 .597 .486 .631 .664 .694 .714 .661 .262 .256 .245 .422 .478 .421 .482 .482 .734 .491 .526

************************************************************************* TABLE 8.29 Top Jaccard values for Ward's method clustering of Model IA

************************************************************************* GROUP 1 2 3 4

ADULT SUBADULT ADULTMALE ADULTFEMALE 5 SUBADULT CLANl 6 SUBADULTCLAN2 7 SUBADULT MALE CLAN! 8 SUBADULT FEMALE CLANl 9 FEMALE WEALTHY 10 SUBADULTFEMALECLAN2 11 SUBADULT MALE CLAN2 12 MALE WEALTHY 13 FEMALE CLAN2 WEALTHY 14 FEMALE CLANl WEALTHY 15 ADULT MALE CLAN2 16 ADULTFEMALECLANlAVERAGE 17 ADULTFEMALECLAN2 18 SUBADULT MALE CLAN! POOR 19 ADULT MALE CLAN! 20 SUBADULTFEMALECLANlPOOR 21 SUBADULTFEMALECLAN2POOR 22 SUBADUL T MALE CLAN2 POOR

LEVEL

JACCARD 1 1 1 1 1 1 1 1 1 1 1 1 1 1 .818 .75 .733 .647 .594 .588 .583 .533

2 2 3 3

4 4

5 5 6

7 7 9

15 15 10 8 8 11

10 12 14 13

191

.449 .449 .463 .821 .821 .57 .56 .551 .657 .51 .568 .452 .335 .397 .285 .221 .192 .453 .388 .428 .657 .657 .514 .367 .42

.679 .679 .979 .635 .635 .588 .506 .521 .544 .758 .523 .778 .733 .632 .279 .209 .246 .445 .513 .414 .558 .558 .701 .546 .518

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************ TABLE 8 .30 Top J accard values for Average linkage clustering of Model lA

************************************************************************ GROUP

LEVEL

JACCARD

1 ADULT 2 SUBADULT 3 SUBADULT CLAN! 4 SUBADUL T CLAN2 5 ADULTMALE 6 ADULT FEMALE 7 SUBADULT MALE CLANl 8 SUBADUL T FEMALE CLANl 9 SUBADUL T FEMALE CLAN2 10 SUBADULT MALE CLAN2 11 MALE WEAL THY 12 FEMALE WEAL THY 13 ADULT MALE CLAN2 AVERAGE 14 ADULT MALE CLANl POOR 15 ADULT MALE CLAN! AVERAGE 16 ADULT MALE CLAN2 POOR 17 ADULTFEMALECLANl 18 ADULT MALE CLAN2 19 ADULTFEMALECLANl AVERAGE 20 ADULT FEMALE CLAN2 21 AGED 40+ FEMALE CLAN2 22 SUBADULT MALE CLANl POOR 23 ADULTMALECLANl 24 ADULTFEMALECLANI POOR 25 ADULT FEMALE CLAN2 POOR

2 2

1 1

3

I

3 4 4

1 1

5 5

1 1

6 6 7 9

I

14 13 13 14

.917

I

1 1 I

.9 .9

.857 .837 .818 .769 .733 .739

8

10 11 8

12 15 10

.647

.594 .25 .25

11

12

************************************************************************ TABLE 8 .31 Top Jaccard values for monothetic divisive clustering of Model lA

************************************************************************ GROUP

LEVEL

JACCARD

1 ADULT 2 SUBADULT 3 ADULTMALE 4 ADULT FEMALE 5 FEMALE WEALTHY 6 MALE WEALTHY 7 SUBADUL T MALE 8 SUBADULT FEMALE 9 SUBADULT MALE CLANl 10 SUBADULT MALE CLAN2 11 SUBADULTFEMALECLAN2 12 SUBADULT FEMALE CLANl 13 FEMALE CLANl WEALTHY 14 FEMALE CLAN2 WEALTHY 15 MALE CLANl WEALTHY 16 MALE CLAN2 WEALTHY 17 ADULTMALECLANl AVERAGE 18 ADULTMALECLANl POOR 19 ADULT MALE CLAN2 20 AGED 40+ FEMALE CLANl AVERAGE 21 ADULTFEMALECLANlAVERAGE 22 ADULT FEMALE CLAN2 23 SUBADULT MALE CLANl POOR 24 ADULT MALE CLANl

2 2 3 3 4

1

5

1 1

I

1 1 I

6 6

I

8 8

1 1

10 10

I

1 1

11 11

I

1 1

12 12 14 14

I

7 7

1 .818 .812 .75 .733

15

.647

9

.594

9

13

********************************************************************************* TABLE 8.32 Summary of top Jaccard values for clustering of Model IA

********************************************************************************* SOCIAL GROUP 1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17)ADULTPOORFEMALE 18 )ADULT AVERAGE

WARD .44@2 .395@ 4 .176@ 9 1 @2** I@ 2 ** .52@ 2 .583@ 5 .19@ 12 .813@ 2 * 1@ 3 ** I@ 3 ** .553@ 6 1@ 6** I@ 9 ** .278@ 9 .405 @9 .345@ 8 .458@ 6

AVERAGE .44@2 .395@ 3 .176@ 7 1 @2** I@ 2 ** .52@ 2 .583@ 5 .187@ 2 .813@ 2 * 1 @4** I@ 4 ** .553@ 9 1 @9 ** I@ 7 ** .278@ 7 .562@ 13 .345@ 8 .44@2

192

DIVISIVE .44@2 .327@ 2 .231 @ 12 1 @2** I@ 2 ** I@ 6 ** 1@ 6** .187@ 2 .813@ 2 * 1@ 3 ** I@ 3 ** .553@ 4 1 @4** I@ 5 ** .281 @ 14 .6@ 14 .345@ 7 .458@4

Appendix 1

******************************************************************************** TABLE 8.32 (Continued)

******************************************************************************** 19 )ADULT AVERAGE MALE 20 ) ADULT A VER FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.595@ 9 .66@6 .581 @ 3 .664@ 3 .64@2 .574@ 3 .69@3

.595@ 7 .526@ 11 .581 @ 4 .664@4 .64@2 .574@ 4 .69@4

.595@ 5 .66@4 .581 @ 3 .664@ 3 .64@2 .574@ 3 .69@3

************************************************************************************************** TABLE 8.33 Highest correlations on axes for correspondence analysis of Model lA

************************************************************************************************** Positive

Negative

AXIS 1 ( 20.39 %) - -- -- --- -- -- -- --- -- -- --- -

SUBADULT SUBADULT POOR SUBADUL T MALE

.978 .792 .616

ADULT AGED 40+ ADULT FEMALE

-.978 -.637 -.52

AXIS 2 ( 17.62 %) - -- -- --- -- -- -- --- -- -- --- ADULT MALE MALE AGED 40+ MALE

.772 .751 .584

ADULT FEMALE FEMALE AGED 40+ FEMALE

-.862 -.751 -.716

SUBADULT FEMALE FEMALE

.769 .601

SUBADUL T MALE MALE

-.752 -.601

AXIS 4 ( 9.63 %) - -- -- --- -- -- -- --- -- -- --- CLAN2

.794

CLANl

-.794

AXIS 5 ( 8.33 %) - -- -- --- -- -- -- --- -- -- --- CLAN!

.608

CLAN2

-.608

SUBADULT AVERAGE

.738

SUBADULT POOR

-.693

AXIS 7 ( 6.12 %) - -- -- --- -- -- -- --- -- -- --- ADULT FEMALE AVERAGE

.571

FEMALE WEALTHY

-.508

AXIS 3 ( 14.05 %) - -- -- --- -- -- -- --- -- -- --- -

AXIS 6(7.37%) - -- -- --- -- -- -- --- -- -- --- -

************************************************************************************************** TABLE 8.34 Highest correlations on axes for detrended correspondence analysis of Model lA

************************************************************************************************** Positive

Negative

AXIS 1 ( 20.39 %) - -- -- --- -- -- -- --- -- -- --- SUBADULT SUBADULT POOR SUBADUL T MALE

.971 .803 .614

ADULT AGED 40+ ADULT MALE

-.971 -.638 -.535

AXIS 2 ( 14 %) - -- -- --- -- -- -- --- -- -- --- FEMALE ADULT FEMALE SUBADULT FEMALE

.929 .557 .513

MALE SUBADUL T MALE ADULT MALE

-.929 -.586 -.521

AXIS 3 ( 10.73 %) - -- -- --- -- -- -- --- -- -- --- SUBADULT FEMALE

.599

SUBADUL T MALE

-.587

AXIS 4 ( 4.86 %) - -- -- --- -- -- -- --- -- -- --- CLAN2

.773

CLAN!

-.773

************************************************************************************************* TABLE 8.35 Highest correlations on axes for unrotated PCA (covariance matrix) of Model IA

************************************************************************************************* Positive

Negative

AXIS 1 ( 25.83 %) ADULT FEMALE AGED 40+ FEMALE ADULT

.886 .773 .763

SUBADULT SUBADULT POOR MALE

193

-.763 -.643 -.501

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************************************** TABLE 8.35 (Continued)

************************************************************************************************** Positive

Negative

AXIS 2 (21 %) ADULT MALE AGED 40+ MALE MALE WEALTHY

.927 .715 .69

FEMALE SUBADULT

-.555 -.517

.963

CLAN2

-.963

.265 .221 .217

ADULT AVERAGE

-.235

.725

ADULT AVERAGE

-.539

SUBADULT MALE MALE

-.761 -.606

AXIS 3 ( 15.41 %) CLANl AXIS 4 ( 8.02 %) WEALTHY FEMALE WEALTHY SUBADULT AVERAGE AXIS 5 ( 7.36 %) WEALTHY FEMALE WEALTHY

.543

AXIS 6 ( 5.87 %) SUBADULT FEMALE FEMALE

.799 .606

************************************************************************************************** TABLE 8.36 Highest correlations on axes for unrotated PCA (correlation matrix) of Model lA

************************************************************************************************** Positive

Negative

AXIS 1 ( 22.9 %) ADULT MALE MALE WEALTHY AGED 40+ MALE

.825 .788 .635

FEMALE ADULT FEMALE

-.634 -.569

.809 .739 .69

SUBADULT SUBADULTPOOR SUBADULT MALE

-.809 -.656 -.517

.632

CLAN2

-.632

.744

CLANl

-.744

SHAMAN ADULT FEMALE AVERAGE

-.58 -.519

.79 .609

SUBADULT MALE MALE

-.766 -.609

.486

SUBADULTPOOR

-.409

MALE WEALTHY

-.444

AXIS 2 ( 22.4 %) ADULT WEALTHY FEMALE WEALTHY AXIS 3 ( 10.5 %) CLANl AXIS 4 ( 8.8 %) CLAN2 AXIS 5 ( 7.4 %)

AXIS 6 ( 5.8 %) SUBADULT FEMALE FEMALE AXIS 7 ( 5.1 %) SUBADULT AVERAGE AXIS 8 ( 4.9 %)

************************************************************************************************** TABLE 8.37 Highest correlations on axes for Varimax-rotated PCA of Model lA

************************************************************************************************** Positive

Negative

AXIS 1 ( 22.9 %) ADULT FEMALE

.928

SUBADULT

194

-.688

Appendix 1 ************************************************************************************************** TABLE 8.37 (Continued)

************************************************************************************************** Positive AGED 40+ FEMALE ADULT FEMALE AVERAGE

Negative

.825 .725

MALE SUBADULT POOR

-.584 -.567

.855 .788 .654

SUBADULT SUBADULT POOR

-.654 -.544

.996

CLAN2

-.996

.753 .61

SUBADULT FEMALE FEMALE

-.811 -.61

AXIS 2 ( 22.4 %) ADULT MALE AGED 40+ MALE ADULT AXIS 3 ( 10.5 %) MALE WEALTHY WEALTHY

.945 .625

AXIS 4 ( 8.8 %) FEMALE WEALTHY WEALTHY

.954 .74

AXIS 5 ( 7 .4 % ) SHAMAN

.974

AXIS 6 ( 5.8 %) CLANl AXIS 7 ( 5.1 %) SUBADUL T MALE MALE AXIS 8 ( 4.9 %) SUBADULT AVERAGE

.569

************************************************************************************************** TABLE 8.38

Highest correlations on axes for Oblimin-rotated

PCA of Model lA

************************************************************************************************** Positive

Negative

AXIS 1 ( 22.9 %) MALE WEALTHY WEALTHY ADULT MALE

.987 .657 .549

AXIS 2 ( 22.4 %) ADULT FEMALE AGED 40+ FEMALE ADULT

.942 .835 .723

SUBADULT SUBADULT POOR MALE

-.723 -.591 -.59

CLANl

-.998

SHAMAN

-.998

SUBADUL T MALE MALE

-.719 -.593

SUBADULT SUBADULT POOR

-.703 -.578

AXIS 3 ( 10.5 %) FEMALE WEALTHY WEALTHY

.993 .766

AXIS 4 ( 8.8 %) CLAN2

.998

AXIS 5 ( 7 .4 %)

AXIS 6 ( 5.8 %) SUBADULT FEMALE FEMALE

.84 .593

AXIS 7 ( 5.1 %) SUBADULT AVERAGE

.617

AXIS 8 ( 4.9 %) ADULT MALE AGED 40+ MALE ADULT

.875 .782 .703

195

Theoretical and Quantitative Approaches to the Study of Mortuary Practice *********************************************************************************************** TABLE 8 .39 Summary of highest correlations in PCA/CA analyses of Model IA

***********************************************************************************************

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBADULTFEMALE 8 SUBADULT AVERAGE 9 SUBADULTPOOR 10 ADULTMALE 11 ADULTFEMALE 12 WEALTHY 13 WEALTHY FEMALE 14 WEALTHY MALE 15 ADULTPOOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULTAVERAGEMALE 20 ADULT AVERAGEFEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.794 .794 .46 -.978 .978 -.752 .769 .738 .792 .772 -.862 -.461 -.508 .523 .283 .234 .384 -.441 .432 .571 .751 -.751 -.637 .584 -.716

-.773 .773 -.182 -.971 .971 .614 .599 .393 .803 -.535 .557 -.507 .359 -.375 -.229 -.226 -.143 -.42 -.37 .328 -.929 .929 -.638 -.401 .465

.963 -.963 .303 .763 -.763 -.761 .799 -.288 -.643 .927 .886 .725 .644 .69 -.451 -.345 -.267 -.539 .476 .486 -.606 .606 .612 .715 .773

-.744 .744 -.58 .809 -.809 -.766 .79 .486 -.656 .825 .683 .739 .69 .788 -.326 .351 -.288 -.443 -.467 -.519 .634 -.634 .616 .635 .607

.996 -.996 .974 .688 -.688 .753 -.811 .569 -.567 .855 .928 .74 .954 .945 .296 .461 .319 .472 .653 .725 .61 -.61 .544 .788 .825

-.998 .998 -.998 .723 -.723 -.719 .84 .617 -.591 .875 .942 .766 .993 .987 .276 .422 .283 .446 .62 .666 -.593 .593 .58 .782 .835

********************************************************************************** TABLE 8.40 Top Jaccard values for Ward's method clustering of Model 2A

********************************************************************************** GROUP 1 RANK2 2 RANK! 3 SUBADUL T RANK2 4 ADULTRANK2 5 ADULTMALERANK2 6 ADULT FEMALE RANK2 7 SUBADULT FEMALE CLAN2 RANK2 8 SUBADUL T MALE RANK2 9 SUBADULT FEMALE CLAN! RANK2 10 MALE WEALTHY RANK2 11 MALE WEALTHY RANK! 12 FEMALE WEALTHY RANK2 13 FEMALE WEALTHY RANKl 14 ADULT FEMALE AVERAGE RANK! 15 SUBADULTCLAN2AVERAGERANKI 16 ADULTFEMALERANKl 17 SUBADULTCLANIRANK2 18 ADULT MALE CLAN2 RANK2 19 ADULT FEMALE CLANl RANK2 20 MALE AVERAGE RANK! 21 ADULTFEMALECLAN2RANK2 22 ADULT MALE CLAN! RANK2 23 MALE RANKl 24 ADULT FEMALE CLANl AVERAGE RANK2 25 AVERAGE RANK! 26 SUBADULT FEMALE CLANl POOR

LEVEL

JACCARD 1 I I 1 I I 1 I I 1 I I 1 I I .905 .875 .829 .828 .8 .773 .76 .75 .667 .647

2 2 3

3 4 4

5 6 6

10 11 12 14 14 15 7 5 8 9

15 9

8 7

12 11 13

.6

**********************************************************************************

TABLE 8.41 Top Jaccard values for Average linkage clustering of Model 2A

********************************************************************************** GROUP I RANK2 2 RANKl 3 SUBADUL T RANK2 4 ADULTRANK2 5 ADULT MALE RANK2 6 ADULT FEMALE RANK2 7 SUBADULT CLAN! RANK2 8 SUBADUL T CLAN2 RANK2 9 SUBADULT MALE CLANl RANK2 10 SUBADULT FEMALE CLAN! RANK2 11 SUBADUL T MALE CLAN2 RANK2 12 SUBADULTFEMALECLAN2RANK2 13 MALE WEALTHY RANK! 14 FEMALE WEALTHY RANKl 15 AVERAGE RANKl

LEVEL 2 2

3 3 4 4

5 5 6 6

7 7 8 9 9

196

JACCARD

Appendix 1 ********************************************************************************** TABLE 8.41 (Continued)

********************************************************************************** GROUP 16 17 18 19 20 21 22 23 24 25

LEVEL

MALE WEAL THY RANK2 FEMALE WEAL THY RANK2 FEMALE AVERAGE RANKl MALE AVERAGE RANKl ADULT FEMALE CLANl RANK2 ADULT FEMALE CLAN2 RANK2 ADULT MALE CLANl RANK2 ADULT FEMALE CLANl AVERAGE RANK2 ADULT MALE CLAN2 RANK2 ADULT FEMALE CLANl POOR

JACCARD

1 1 .909 .857 .828 .773 .76 .714 .69

12 13 14 14 11 11 12 15 10 15

.2

********************************************************************************** TABLE 8.42 Top Jaccard values for monothetic divisive clustering of Model 2A

********************************************************************************** GROUP

LEVEL

1 RANKl 2 RANK2 3 ADULT MALE RANK2 4 ADULT FEMALE RANK2 5 SUBADULT RANK2 6 FEMALE WEAL THY RANKl 7 ADULT MALE CLANl RANK2 8 ADULT MALE CLAN2 RANK2 9 SUBADULT CLAN2 RANK2 10 SUBADULT CLANl RANK2 11 ADULTFEMALECLANl RANK2 12 ADULT FEMALE CLAN2 RANK2 13 ADULT MALE AVERAGE RANKl 14 MALE WEALTHY RANKl 15 MALE CLAN2 WEALTHY RANK2 16 ADULT FEMALE AVERAGE RANKl 17 SUBADULTAVERAGERANKl 18 FEMALE CLANl WEALTHY RANK2 19 SUBADULT MALE CLANl RANK2 20 SUBADULTFEMALECLAN1RANK2 21 MALE CLAN2 WEALTHY RANKl 22 MALE CLANl WEALTHY RANKl 23 FEMALE RANK2 24 FEMALE RANKl 25 MALE RANKl 26 FEMALE AVERAGE RANKl 27 ADULT FEMALE CLANl AVERAGE RANK2

2 2 3 4 4 6 7 7

JACCARD

10 10 11 12 12 13 14 14 15 15 3 5 5

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 .871 .853 .792

6

.8

13

.667

8 8

9 9

******************************************************************************************* TABLE 8.43 Summary of top Jaccard values for clustering of Model 2A

******************************************************************************************* SOCIAL GROUP 1 )CLANl 2)CLAN2 3)LEADER 4)ADULT 5)SUBADULT 6 )AGED 40+ 7)MALE 8)FEMALE 9 )ADULT MALE 10 )ADULT FEMALE 11 )RANKl 12 )RANKl ADULT 13 )RANKl ADULT MALE 14)RANK1ADULTFEMALE 15 )RANKl SUBADULT 16 )RANKl SUBADULTMALE 17)RANK1SUBADULTFEMALE 18 ) RANKl WEALTHY 19 ) RANKl MALE WEAL THY 20 ) RANKl ADULT MALE WEAL THY 21 )RANKl SUBADULTMALEWEALTHY 22 ) RANKl FEMALE WEALTHY 23 ) RANKl ADULT FEMALE WEAL THY 24 )RANKl SUBADULTFEMALEWEALTHY 25)RANK1AVERAGE 26) RANKl MALE AVERAGE 27) RANKl ADULT MALE AVERAGE 28 )RANKl SUBADULTMALEAVERAGE 29) RANKl FEMALE A VERA GE 30 )RANKl ADULT FEMALE AVERAGE 31 )RANKl SUBADULTFEMALEAVERAGE 32) RANKl MALE 33 ) RANKl FEMALE

WARD .456@ 2 .393@ 2 .2@ 11 .746@ 3 * .712@ 3 * .479@ 3 .6@4 .46@2 .783 @4 * .708 @4 * 1 @2 ** .642@ 2 .474@ 15 .905@ 7 ** .5@ 7 .281@ 7 .583@ 15 .526@ 11 1@ 11 ** .6@ 11 .4@ 11 1 @ 14 ** .778@ 14 * .222@ 14 .647@ 11 .8@ 15 * .692@ 15 .227@ 11 .6@ 14 1@ 14 ** .7@ 15 * .75 @7 * .724@ 7 *

197

AVERAGE

DIVISIVE

.456@ 2 .393@ 2 .2@8 .746@ 3 * .712@ 3 * .479@ 3 .6@4 .46@2 .783 @4 * .708 @4 * 1 @2 ** .642@ 2 .5@ 14 .442@ 8 .358@ 2 .267@ 8 .333@ 14 .526@ 8 1@ 8 ** .6@8 .4@8 1 @9 ** .778@ 9 * .222@ 9 1 @9 ** .857@ 14 * .75@ 14 * .214@ 14 .909@ 14 ** .545@ 14 .364@ 14 .5@ 14 .674@ 8

.456@ 2 .393@ 2 .333@ 15 .552@ 2 .712 @4 * .345@ 2 .6@3 .664@ 3 .783@ 3 * .708 @4 * 1 @2 ** .642@2 .789@ 5 * .632@ 12 .684@ 12 .444@ 15 .533 @ 12 .526@ 10 1@ 10 ** 1 @ 15 ** 1@ 15 ** 1 @6 ** .778@ 6 * .222@ 6 .735@ 6 * .643@ 10 1@ 10 ** .385@ 12 .8 @6 * 1@ 12 ** .615@ 12 .792@ 5 * .853@ 5 *

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

******************************************************************************************

TABLE 8.43 (Continued)

****************************************************************************************** SOCIAL GROUP 34 )RANK2 35 ) RANK2 SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 )RANK2MALEADULT 40)RANK2ADULTFEMALE 4l)RANK2SUBADULTFEMALE 42 ) RANK2 SUBADULT MALE 43)RANK2SUBADULTPOOR 44)RANK2SUBADULTAVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 )RANK2AGED 40+FEMALE 48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEAL THY MALE 51)RANK2ADULTAVERAGE 52 ) RANK2 ADULT A VERA GE MALE 53 )RANK2ADULT AVERAGE FEMALE 54)RANK2ADULTPOOR 55 )RANK2ADULTPOORMALE 56)RANK2ADULTPOORFEMALE

WARD 1 @2** 1@ 3 ** 1@ 3 ** .818 @4 * .568 @4 1@ 4 ** 1 @4** .745@ 3 * 1@ 6 ** .787@ 3 * .312@ 13 .58@ 3 .593 @4 .565 @4 .517@ 10 1@ 12 ** 1@ 10 ** .46@3 .483@ 8 .478@4 .286@ 10 .4@ 10 .25@ 12

AVERAGE

DIVISIVE

1 @2** 1@ 3 ** 1@ 3 ** .818 @4 * .568 @4 1@ 4 ** 1 @4** .745@ 3 * .667@6 .787@ 3 * .267@5 .58@ 3 .593 @4 .565 @4 .517@ 12 1@ 13 ** 1@ 12 ** .46@ 3 .483@ 12 .478@4 .286@ 10 .4@ 10 .25@ 13

1 @2** 1 @4 ** .68@2 .818@ 3 * .871@ 3 * 1@ 3 ** 1 @4 ** .745 @4 * .667@ 14 .787 @4 * .267@ 8 .4@ 3 .593@ 3 .565@ 4 .31 @ 11 .643@ 13 .6@ 11 .316@ 3 .444@ 3 .478@ 4 .286@ 11 .4@ 11 .25@ 13

******************************************************************************************************************** TABLE 8 .44 Highest correlations on axes for correspondence analysis of Model 2A

************************************************************************************************************** Positive

Negative

AXIS 1 ( 17.88 %) RANKl ADULTRANKl AVERAGE RANKl

.962 .739 .699

RANK2 ADULTRANK2 AGED 40+ RANK2

-.962 -.78 -.508

.94 .763 .741

ADULT ADULTRANK2

-.763 -.576

.837 .764 .635

ADULT MALE RANK2 MALERANK2 MALE

-.695 -.693 -.635

.685 .684 .52

MALE WEALTHY RANKl ADULT MALE WEALTHY RANKl ADULT MALE RANKl

-.607 -.585 -.569

.6

SUBADULT MALE RANK2

-.833

.522

FEMALE WEALTHY RANKl WEALTHY RANKl ADULT FEMALE WEALTHY RANKl

-.55 -.543 -.521

.619

SUBADUL T POOR RANK2

-.68

CLANl

-.655

AXIS 2 ( 12.54 %) SUBADULT RANK2 SUBADULT SUBADULT FEMALE RANK2 AXIS 3 ( 11.9 %) ADULT FEMALE RANK2 ADULT FEMALE FEMALE AXIS 4 ( 8.95 %) FEMALE RANKl ADULT FEMALE RANKl FEMALE AXIS 5 ( 8.55 %) SUBADULT FEMALE RANK2 AXIS 6 ( 6.06 %) AVERAGE RANKl

AXIS 7 ( 5.18 %) SUBADULTAVERAGERANK2 AXIS 8 ( 4.5 %) WEALTHY RANK2 MALE WEALTHY RANK2

.689 .534

AXIS 9 ( 4.23 %) CLAN2

.655

AXIS 10 ( 3.95 %)

198

Appendix 1

***************************************************************************************************************** TABLE 8.44 (Continued)

***************************************************************************************************************** ADULT FEMALE AVERAGE RANK2 AGED 40+ FEMALE RANK2

.457 .418

FEMALE WEALTHY RANK2

-.415

LEADER FEMALE AVERAGE RANKJ

-.531 -.505

AXIS 11 ( 3.72 %)

*********************************************************************************************************** TABLE 8.45 Highest correlations on axes for detrended correspondence analysis of Model 2A

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 17.88 %) RANKl ADULTRANKl AVERA GE RANKl

.944 .699

RANK2 ADULTRANK2 AGED 40+ RANK2

-.944 -.794 -.522

.766 .735 .634

ADULTMALE RANK2 MALERANK2 ADULT MALE

-.699 -.66 -.65

.615

SUBADUL T FEMALE RANK2

-.627

.715

AXIS 2 ( 11.73 %) ADULT FEMALE RANK2 FEMALE RANK2 FEMALE AXIS 3 ( 8.15 %) ADULT FEMALE AXIS 4 ( 4.35 %) SUBADUL T FEMALE RANK2

.502

************************************************************************************************************ TABLE 8.46 Highest correlations on axes for unrotated PCA (covariance matrix) of Model 2A

************************************************************************************************************ Positive

Negative

AXIS 1 ( 28.56 %) - -- -- --- -- -- -- --- -- -- --- RANKl ADULTRANKl AVERA GE RANKl

.937 .741 .696

RANK2 ADULTRANK2 AGED 40+ RANK2

-.937 -.822 -.554

AXIS 2 ( 14.06 %) - -- -- --- -- -- -- --- -- -- --- ADULT MALE RANK2 ADULT MALE MALERANK2

.739 .686 .648

FEMALE RANK2 ADULT FEMALE RANK2 FEMALE

-.7 -.656 -.626

AXIS 3 ( 12.51 %) - -- -- --- -- -- -- --- -- -- --- CLANl

.83

CLAN2

-.83

AXIS 4 ( 9.76 %) - -- -- --- -- -- -- --- -- -- --- ADULT

.748

SUBADUL T RANK2 SUBADUL T FEMALE RANK2 SUBADULT POOR RANK2

-.881 -.81 -.757

AXIS 5 ( 6.63 %) - -- -- --- -- -- -- --- -- -- --- ADULT FEMALE RANKl

.579

FEMALE RANKl

-.503

WEALTHY RANKl FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl

-.796 -.64 -.615

AXIS 6 ( 6.19 %) - -- -- --- -- -- -- --- -- -- --- AXIS 7 ( 4.18 %) - -- -- --- -- -- -- --- -- -- --- AVERA GE RANKl

.653

AXIS 8 ( 3.36 %) - -- -- --- -- -- -- --- -- -- --- WEALTHY RANK2 MALE WEALTHY RANK2

.603 .565

199

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************************************************** TABLE 8 .47 Highest correlations on axes for unrotated PCA (correlation matrix) of Model 2A

******************************************************************************************************************** Positive

Negative

AXIS 1 ( 23 %) RANKl ADULTRANKl MALERANKl

RANK2 ADULTRANK2

-.91 -.721

.659 .632 .558

FEMALE RANKl ADULT FEMALE RANKl

-.631 -.627

.733 .701 .681

ADULT FEMALE RANK2 ADULT FEMALE FEMALE

-.737 -.676 -.664

.764

SUBADULT RANK2 SUBADULT SUBADULT FEMALE RANK2

-.809 -.764 -.674

AVERAGE RANKl MALE AVERAGE RANKl ADULT MALE AVERAGE RANKl

-.54 -.54 -.509

.803

CLAN2

-.803

.572 .557 .508

WEALTHY RANKl

-.507

.717 .583

ADULT AVERAGE RANK2

-.533

.416 .411

FEMALE WEALTHY RANK2

-.41

.788

SUBADULT FEMALE RANK2

-.542

.396 .308

ADULT FEMALE AVERAGE. RANK2 FEMALE

-.329 -.308

.91 .785

.696

AXIS 2 ( 12.8 %) LEADER ADULT MALE WEALTHY RANKl MALE WEALTHY RANKl

AXIS 3 ( 12 %) ADULT MALE RANK2 ADULT MALE MALERANK2

AXIS 4 ( 8.7 %) ADULT ADULTRANK2 ADULT FEMALE

.534 .519

AXIS 5 ( 7.6 %)

AXIS 6 ( 5.3 %) CLANl

AXIS 7 ( 4.8 %) FEMALE AVERAGE RANKl ADULT FEMALE AVERAGE RANKl AVERAGE RANKl

AXIS 8 ( 4.1 %) WEALTHY RANK2 MALE WEALTHY RANK2

AXIS 9 ( 3.6 %) AGED 40+ FEMALE RANK2 ADULTFEMALEAVERAGERANK2

AXIS 10 ( 3.2 %) SUBADULT MALE RANK2

AXIS 11 ( 2.9 %) SUBADULT MALE RANK2 MALE

************************************************************************************************************ TABLE 8.48 Highest correlations on axes for Varimax-rotated PCA of Model 2A

************************************************************************************************************ Positive

Negative

AXIS 1 ( 23 %) MALERANKl MALE WEALTHY RANKl ADULT MALE RANKl

.87 .789 .786

AXIS 2 ( 12.8 %) AVERAGE RANKl RANKl FEMALE AVERAGE RANKl

RANK2

.912 .781 .776

200

-.781

Appendix 1 *********************************************************************************************************** TABLE 8.48 (Continued)

*********************************************************************************************************** Positive

Negative

AXIS 3 ( 12 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl WEALTHY RANKl

.912 .903 .763

AXIS 4 ( 8.7 %) LEADER

.971

AXIS 5 ( 7 .6 % ) ADULT MALE RANK2 AGED 40+ MALE RANK2 ADULT MALE

.817 .751 .733

SUBADULT SUBADUL T RANK2 SUBADUL T FEMALE RANK2

-.641 -.631 -.598

CLAN2

-.991

SUBADUL T MALE RANK2

-.94

SUBADULT AVERAGE RANK2

-.203

AXIS 6 ( 5.3 %) ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2 ADULT FEMALE

.883 .834 .754

AXIS 7 ( 4.8 %) MALE WEALTHY RANK2 WEALTHY RANK2

.965 .696

AXIS 8 ( 4.1 %) FEMALE WEALTHY RANK2 WEALTHY RANK2

.964 .673

AXIS 9 ( 3.6 %) CLANl

.991

AXIS 10 ( 3.2 %)

AXIS 11 ( 2.9 %) MALE AVERAGE RANKl ADULT MALE AVERAGE RANKl

.27 .244

******************************************************************************************************************* TABLE 8.49 Highest correlations on axes for Oblimin-rotated PCA of Model 2A

******************************************************************************************************************* Positive

Negative

AXIS 1 (23 %) A VERA GE RANKl RANKl FEMALE RANKl

.89 .853 .772

RANK2

-.853

ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2 ADULT FEMALE

-.927 -.84 -.773

SUBADULT SUBADUL T RANK2 SUBADUL T FEMALE RANK2

-.694 -.668 -.659

AXIS 2 ( 12.8 %) LEADER ADULT MALE WEALTHY RANKl MALE WEALTHY RANKl

.992 .652 .506

AXIS 3 ( 12 %)

AXIS 4 ( 8.7 %) ADULT MALE RANK2 ADULTRANK2 AGED 40+ MALE RANK2

.782 .777 .702

AXIS 5 ( 7 .6 %)

201

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ******************************************************************************************************************** TABLE 8.49 (Continued)

******************************************************************************************************************** Positive FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl WEALTHY RANKl

Negative

.913 .904 .81

AXIS 6 ( 5.3 %) CLANl

.993

CLAN2

-.993

.554

MALERANKl ADULT MALE RANKl MALE WEALTHY RANKl

-.91 -.832 -.791

FEMALE WEALTHY RANK2 WEALTHY RANK2 ADULT FEMALE RANK2

-.99 -.662 -.567

MALE WEALTHY RANKl SUBADULTAVERAGERANK2 ADULT MALE WEALTHY RANKl

-.298 -.226 -.223

AXIS 7 ( 4.8 %) RANK2

AXIS 8 ( 4.1 %)

------ --------- ---------MALE WEALTHY RANK2 WEALTHY RANK2 ADULT MALE RANK2

.986 .68 .549

AXIS 9 ( 3.6 %)

------ --------- ----------

AXIS 10 ( 3.2 %)

------ --------- ---------SUBADULT MALE RANK2

.966

AXIS 11 ( 2.9 %)

------ --------- ---------MALE AVERAGE RANKl ADULT MALE AVERAGE RANKl

.244 .214

******************************************************************************************************** TABLE 8 .50 Summary of highest correlations in PCNCA analyses of Model 2A

********************************************************************************************************

I CLAN! 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANK! 12 RANK! ADULT 13 RANKlADULTMALE 14 RANK! ADULT FEMALE 15 RANK! SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEAL THY 19 RANKl MALE WEALTHY 20 RANKl ADULT MALE WEAL THY 21 RANK! SUBADMALEWEALTHY 22 RANKl FEMALE WEALTHY 23 RANK! ADULT FEMALE WEAL THY 24 RANKl SUBADFEMALEWEALTHY 25 RANKl AVERAGE 26 RANK! MALE AVERAGE 27 RANK! ADULT MALE AVERAGE 28 RANKl SUBAD MALE AVERAGE 29 RANKl FEMALE AVERAGE 30 RANK! ADULTFEMALEAVRAGE 31 RANKl SUBADULT FEMALE AVERAGE 32 RANK! MALE 33 RANK! FEMALE 34 RANK2 35 RANK2 SUBADULT 36 RANK2ADULT 37 RANK2 MALE 38 RANK2 FEMALE 39 RANK2MALEADULT

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.655 .655 -.531 -.763 .763 -.444 -.635 .635 -.635 .764 .962 .739 -.569 .684 .502 .341 .351 .552 -.607 -.585 .337 -.55 -.521 .208 .699 .425 .35 .229 .514 .486 .35 .61 .685 -.962 .94 -.78 -.693 .627 -.695

.324 -.324 .169 -.273 .273 -.143 -.634 .634 -.65 .634 .944 .715 .458 .505 .504 .335 .361 .525 .383 .291 .243 .343 .33 .151 .699 -.422 -.373 .227 .521 .39 .324 .585 .643 -.944 -.322 -.794 -.66 .735 -.699

.83 -.83 -.234 .748 -.748 .458 .626 -.626 .686 -.553 .937 .741 .492 .579 .461 .333 .303 -.796 -.463 -.375 -.277 -.64 -.615 -.197 .696 .479 .376 .293 .491 .394 .31 .612 .61 -.937 -.881 -.822 .648 -.7 .739

.803 -.803 .659 .764 -.764 .44 .664 -.664 .701 -.676 .91 .785 .62 -.627 .364 .303 .201 .669 .572 .632 -.398 .487 .495 -.167 .547 -.54 -.509 -.207 .572 .557 -.234 .696 -.631 -.91 -.809 -.721 .681 -.659 .733

.991 -.991 .971 .641 -.641 .464 .589 -.589 .733 .754 .781 .668 .786 .662 .32 .365 .284 .763 .789 .653 .433 .912 .903 .232 .912 .434 .461 .262 .776 .714 .322 .87 .727 -.781 -.631 .683 .685 -.506 .817

.993 -.993 .992 .694 -.694 .497 .545 -.545 .681 -.773 .853 .732 -.832 .713 .347 -.37 .288 .81 -.791 -.681 -.402 .913 .904 .232 .89 -.484 -.496 .256 .746 .689 .307 -.91 .772 -.853 -.668 .777 .668 -.531 .782

202

Appendix 1

******************************************************************************************************** TABLE 8.50 (Continued)

******************************************************************************************************** 40 RANK2ADULTFEMALE 41 RANK2SUBADULTFEMALE 42 RANK2SUBADULTMALE 43 RANK2 SUBADULT POOR 44RANK2SUBADULTAVERAGE 45 RANK2 AGED 40+ 46 RANK2 AGED 40+ MALE 47 RANK2AGED 40+FEMALE 48 RANK2 WEALTHY 49 RANK2 WEALTHY FEMALE 50 RANK2 WEALTHY MALE 51 RANK2ADULT AVERAGE 52 RANK2ADULT AVERAGE MALE 53 RANK2 ADULT AVERAGE FEMALE 54 RANK2ADULTPOOR 55 RANK2ADULTPOORMALE 56 RANK2ADULTPOORFEMALE

.837 .741 -.833 -.68 .629 -.508 -.546 .596 .689 .565 .534 -.473 -.415 .501 -.455 -.356 .319

.766 -.627 .429 .33 -.215 -.522 -.555 .556 -.366 .48 -.445 -.435 -.414 .466 -.256 -.223 .248

-.656 -.81 -.275 -.757 -.364 -.554 .605 -.486 .603 -.447 .565 -.453 .432 -.395 -.429 -.385 -.186

-.737 -.674 .788 -.616 -.475 -.485 .592 -.545 .717 -.598 .593 -.533 -.47 .411 -.401 -.325 .286

.883 -.598 -.94 -.475 -.381 .557 .751 .834 .696 .964 .965 .479 .678 .743 .29 .357 .384

********************************************************************************** TABLE 8.51 Top Jaccard values for Ward's method clustering of Model 3A

********************************************************************************** GROUP

LEVEL

I ADULT MALE RANK2 2 SUBADULT RANK2 3 RANKl 4 ADULT FEMALE RANK2 5 SUBADUL T FEMALE CLAN2 RANK2 6 ADULT FEMALE RANKl 7 SUBADULT MALE RANK2 8 SUBADULT FEMALE CLANl RANK2 9 ADULT MALE RANKl 10 SUBADULTRANKI 11 MALE WEAL THY RANK2 12 FEMALE WEALTHY RANK2 13 ADULT FEMALE WEALTHY RANK! 14 ADULT FEMALE AVERAGE RANKl 15 SUBADULT MALE RANKl 16 SUBADULTFEMALERANKl 17 SUBADULTCLANl RANK2 18 ADULT MALE CLAN2 RANK2 19 ADULT FEMALE CLAN! RANK2 20 ADULT FEMALE CLAN2 RANK2 21 FEMALE 22 ADULT MALE CLANl RANK2 23 MALE RANKl 24 ADULT FEMALE CLANl AVERAGE RANK2 25 ADULT FEMALE 26 SUBADULTFEMALECLANlPOOR

2 3 4 4

5 6 7 7 8 8 10

12 14 14 15 15 5 9

11 11 2 9 6

12 3 13

JACCARD 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 .875 .829 .828 .773 .753 .76 .706 .667 .657 .533

********************************************************************************** TABLE 8.52 Top Jaccard values for Average linkage clustering of Model 3A

********************************************************************************** GROUP

LEVEL

I SUBADULT RANK2 2 ADULT FEMALE 3 ADULT MALE RANK2 4 SUBADULT CLANl RANK2 5 SUBADULTCLAN2RANK2 6 ADULT MALE RANKl 7 SUBADULT RANK! 8 ADULTFEMALERANKl 9 ADULT FEMALE RANK2 10 SUBADULT MALE CLAN! RANK2 11 SUBADULTFEMALECLAN1RANK2 12 SUBADULT MALE CLAN2 RANK2 13 SUBADULT FEMALE CLAN2 RANK2 14 MALE WEAL THY RANK2 15 SUBADULT MALE RANKl 16 SUBADULTFEMALERANKl 17 FEMALE WEALTHY RANK2 18 ADULTMALEAVERAGERANKl 19 MALE CLANl WEALTHY RANKl 20 ADULT 21 ADULT FEMALE CLANl RANK2 22 ADULT FEMALE CLAN2 RANK2 23 ADULT MALE CLANl RANK2 24 MALE RANKl 25 ADULT MALE CLAN2 RANK2 26 ADULT FEMALE CLAN! AVERAGE RANK2

2 4 4

5 5 6 6 7 7 8 8 9 9

12 13 13 14 15 15 3 11 11 12 3 10

14

203

JACCARD 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 .888 .828 .773 .76 .706 .69 .667

-.927 -.659 .966 -.505 -.399 .621 .702 -.84 .68 -.99 .986 .519 .615 -.723 .308 .33 -.366

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

********************************************************************************** TABLE 8 .53 Top Jaccard values for monothetic divisive clustering of Model 3A

********************************************************************************** GROUP

LEVEL

1 RANKl 2 RANK2 3 ADULTMALERANK2 4 ADULT FEMALE RANKl 5 ADULT FEMALE RANK2 6 SUBADUL T RANK2 7 ADULTMALERANKl 8 SUBADULTRANKl 9 ADULTMALECLANl RANK2 10 ADULT MALE CLAN2 RANK2 11 ADULT FEMALE WEAL THY RANKl 12 ADULT FEMALE AVERAGE RANKl 13 MALE CLANl WEALTHY RANKl 14 ADULTMALEAVERAGERANKl 15 ADULT FEMALE CLAN2 RANK2 16 ADULTFEMALECLANl RANK2 17 SUBADULTCLAN2RANK2 18 SUBADULTCLAN1RANK2 19 SUBADULTMALERANKl 20 SUBADULT FEMALE RANKl 21 MALE CLAN2 WEALTHY RANK2 22 FEMALE CLANl WEAL THY RANK2 23 SUBADUL T MALE CLANl RANK2 24 SUBADULTFEMALECLAN1RANK2 25 FEMALE RANK2 26 MALE RANKl 27 ADULT FEMALE CLANl AVERAGE RANK2

2 2 3 4

5 5 6 6

7 7 8 8 9 9

10 10 11 11 12 12 13 14 15 15 3 4

14

JACCARD 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 .871 .706 .667

**************************************************************************************************** TABLE 8 .54 Summary of top J accard values for clustering of Model 3A

**************************************************************************************************** SOCIAL GROUP 1 )CLANl 2 )CLAN2 3)LEADER 4)ADULT 5)SUBADULT 6 )AGED 40+ 7 )MALE 8)FEMALE 9 )ADULT MALE 10 ) ADULT FEMALE 11 )RANKl 12 )RANKl ADULT 13 )RANKl ADULT MALE 14)RANK1ADULTFEMALE 15 )RANKl SUBADULT 16 )RANKl SUBADULTMALE 17 )RANKl SUBADULTFEMALE 18 )RANKl WEALTHY 19 ) RANKl MALE WEALTHY 20 ) RANKl ADULT MALE WEALTHY 21 )RANKl SUBADULTMALEWEALTHY 22 ) RANKl FEMALE WEALTHY 23 ) RANKl ADULT FEMALE WEALTHY 24 ) RANKl SUBADULT FEMALE WEALTHY 25 ) RANKl A VERA GE 26 ) RANKl MALE A VERA GE 27 )RANKl ADULT MALE AVERAGE 28 )RANKl SUBADULTMALEAVERAGE 29 ) RANKl FEMALE AVERAGE 30 )RANKl ADULT FEMALE AVERAGE 31)RANK1SUBADULTFEMALEAVERAGE 32 )RANKl MALE 33 ) RANKl FEMALE 34 )RANK2 35 ) RANK2 SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 )RANK2MALEADULT 40)RANK2ADULTFEMALE 41)RANK2SUBADULTFEMALE 42 ) RANK2 SUB ADULT MALE 43)RANK2SUBADULTPOOR 44)RANK2SUBADULTAVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 ) RANK2 AGED 40+ FEMALE

WARD .433@ 2 .411@ 2 .133@ 8 .523@ 3 .712@ 3 * .359@ 3 .6@2 .753 @2 * .783@ 2 * .708 @4 * 1 @4** .642@4 1@ 8 ** 1@ 6** 1@ 8 ** 1@ 15 ** 1@ 15 ** .368@ 14 .316@ 8 .4@ 8 .444@ 15 .778@ 14 * 1@ 14 ** .2@ 15 .642@4 .45@ 8 .6@8 .556@ 15 .6@ 14 1@ 14 ** .8@ 15 * .706@ 6 * .655@ 6 .465@2 1@ 3 ** .54@2 .818@ 2 * .568 @4 1 @2** 1@ 4 ** .745@ 3 * 1 @7 ** .787@ 3 * .294@ 13 .4@2 .593@ 2 .565 @4

204

AVERAGE

DIVISIVE

.392@ 2 .477@2 .333@ 15 .888@ 3 * .712@2 * .535@ 3 .6@4 .591 @4 .783 @4 * 1 @4** .642@ 3 .559@ 7 1@ 6** 1 @7 ** 1@ 6 ** 1@ 13 ** 1@ 13 ** .316@ 15 .6@ 15 1 @ 15 ** .444@ 13 .333@ 7 .368@ 7 .2@ 13 .478@ 3 .643@ 15 1@ 15 ** .556@ 13 .444@7 .632 @7 .8@ 13 * .706@ 3 * .655@ 7 .602@3 1@ 2 ** .84@3* .818 @4 * .568@ 7 1 @4** 1@ 7 ** .745@ 2 * .667@ 8 .787@ 2 * .267@ 5 .487@ 3 .593@ 4 .565@ 7

.456@ 2 .393@ 2 .333@ 9 .552@ 2 .712@ 5 * .345@ 2 .6@3 .664@ 3 .783@ 3 * .708@ 5 * 1 @2** .642@2 1 @6 ** 1 @4** 1@ 6 ** 1@ 12 ** 1@ 12 ** .368@ 8 .6@9 1@ 9 ** .444@ 12 .778@ 8 * 1@ 8 ** .2@ 12 .642@2 .643 @9 1 @9 ** .556@ 12 .6@8 1@ 8 ** .8@ 12 * .706@4 * .655 @4 1@ 2 ** 1@ 5 ** .68@2 .818@ 3 * .871@ 3 * 1@ 3 ** 1@ 5 ** .745@ 5 * .667@ 15 .787@ 5 * .267@ 11 .4@ 3 .593@ 3 .565@ 5

Appendix 1 **************************************************************************************************** TABLE 8.54 (Continued)

**************************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEALTHY MALE 5l)RANK2ADULTAVERAGE 52 ) RANK2 ADULT A VERA GE MALE 53)RANK2ADULTAVERAGEFEMALE 54)RANK2ADULTPOOR 55 ) RANK2 ADULT POOR MALE 56)RANK2ADULTPOORFEMALE

.517@ 10 1@ 12 ** 1@ 10 ** .316@ 2 .483@ 9 .478@4 .286@ 10 .4@ 10 .25@ 12

.517@ 12 1@ 14 ** 1@ 12 ** .387@ 3 .483@ 12 .478@ 7 .286@ 10 .4@ 10 .25@ 14

.31@ 13 .643@ 14 .6@ 13 .316@ 3 .444@3 .478@ 5 .286@ 13 .4@ 13 .25@ 14

************************************************************************************************** TABLE 8.55 Highest correlations on axes for correspondence analysis of Model 3A

************************************************************************************************** Positive

Negative

AXIS 1 ( 15.92 %) - -- -- --- -- -- -- --- -- -- --- FEMALE ADULT FEMALE FEMALE RANK2

.855 .669 .619

ADULT MALE MALE ADULT MALE RANK2

-.926 -.855 -.816

AXIS 2 ( 15.5 %) - -- -- --- -- -- -- --- -- -- --- SUBADULT SUBADUL T RANK2 SUBADUL T FEMALE RANK2

.925 .726 .593

ADULT ADULTRANK2 ADULT FEMALE

-.925 -.856 -.559

SUBADUL T FEMALE RANK2 SUBADUL T RANK2 SUBADULT POOR RANK2

.845 .776 .725

RANKl ADULTRANKl ADULT

-.682 -.558 -.506

AXIS 4 ( 8.68 %) - -- -- --- -- -- -- --- -- -- --- SUBADUL T MALE RANK2

.849

AXIS 5 ( 7.41 %) - -- -- --- -- -- -- --- -- -- --- CLAN2

.673

CLANl

-.673

AXIS 6 ( 5.91 %) - -- -- --- -- -- -- --- -- -- --- ADULT FEMALE AVERAGE RANK2 ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2

.544 .543 .541

AXIS 7 ( 5.83 %) - -- -- --- -- -- -- --- -- -- --- CLAN2

.695

CLANl

-.695

AXIS 8 ( 4.79 %) - -- -- --- -- -- -- --- -- -- --- MALE WEALTHY RANK2

.558

AXIS 9 ( 4.04 %) - -- -- --- -- -- -- --- -- -- --- WEALTHY RANK2

.637

AXIS 10 ( 3.4 %) - -- -- --- -- -- -- --- -- -- --- SUBADULT POOR RANK2

.625

SUBADULT AVERAGE RANK2

-.514

AXIS 11 ( 3.06 %) - -- -- --- -- -- -- --- -- -- --- SUBADULT MALE WEALTHY RANKl AGED 40+ FEMALE RANK2 WEALTHY RANKl

.377 .377 .345

LEADER FEMALE WEALTHY RANK2 FEMALE AVERAGE RANKl

-.353 -.336 -.319

AXIS 3 ( 12.91 %)

-------------- ----------

*********************************************************************************************************** TABLE 8.56 Highest correlations on axes for detrended correspondence analysis of Model 3A

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 15.92 %) ADULT MALE

.942

FEMALE

205

-.856

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************************************** TABLE 8.56 (Continued)

********************************************************************************************************** Positive MALE ADULT MALE RANK2

Negative

.856 .842

ADULT FEMALE FEMALE RANK2

-.603 -.598

.773 .516

ADULTRANK2 ADULT ADULT FEMALE RANK2

-.845 -.773 -.756

.843 .776 .741

RANKl ADULT ADULTRANKl

-.621 -.598 -.527

.696

CLAN2

-.696

AXIS 2 ( 11.81 %) SUBADULT SUBADULT RANK2 AXIS 3 ( 7.17 %) SUBADULT RANK2 SUBADULT FEMALE RANK2 SUBADUL T POOR RANK2 AXIS 4 ( 5 %) CLANl

******************************************************************************************************************* TABLE 8.57 Highest correlations on axes for unrotated PCA (covariance matrix) of Model 3A

******************************************************************************************************************* Positive

Negative

AXIS I ( 20.02 %) FEMALE RANK! ADULT FEMALE RANK! RANKl

.728 .725 .68

MALERANK2 RANK2 ADULT MALE RANK2

-.681 -.68 -.67

.77 .729 .707

FEMALE RANK2 RANK2 FEMALE

-.723 -.707 -.589

.952 .661 .577

ADULT ADULTRANK2 AGED 40+

-.952 -.68 -.587

.972

CLAN!

-.972

.238 .222

ADULT MALE AVERAGE RANK2

-.202

.569

WEALTHY RANKl

-.678

.495 .476 .461

ADULT FEMALE RANK2 MALE RANK! AGED 40+ FEMALE RANK2

-.481 -.456 -.427

.281 .269 .253

WEALTHY RANK2

-.25

.381 .339 .31

ADULT MALE AVERAGE RANK2 MALE MALERANK2

-.356 -.339 -.316

AXIS 2 ( 18.87 %) MALE RANK! ADULT MALE RANKl RANK! AXIS 3 ( 16.44 %) SUBADULT SUBADULT RANK2 SUBADULT FEMALE RANK2 AXIS 4 ( 11.07 %) CLAN2 AXIS 5 ( 5.59 %) MALE WEALTHY RANK! ADULT MALE WEALTHY RANK! AXIS 6 ( 4.83 %) AVERAGE RANKl AXIS 7 ( 4.18 %) FEMALE RANKl FEMALE WEALTHY RANK! ADULT FEMALE WEALTHY RANKl AXIS 8 ( 3.49 %) SUBADULT MALE RANKl ADULT POOR RANK2 SUBADULT MALE WEALTHY RANKl AXIS 9 ( 3.2 %) ADULT MALE WEALTHY RANKl FEMALE MALE WEALTHY RANK2

206

Appendix 1

******************************************************************************************************************** TABLE 8.58 Highest correlations on axes for unrotated PCA (correlation matrix) of Model 3A

******************************************************************************************************************** Positive

Negative

AXIS 1 ( 20.8 %) ADULT MALE RANK! MALERANKl ADULT MALE WEALTHY RANK!

.794 .788 .764

RANK2 FEMALE RANK2

-.618 -.513

.821 .744 .692

RANK2 MALERANK2 ADULT MALE RANK2

-.617 -.535 -.502

.825 .718

SUBADULT SUBADULT RANK!

-.825 -.691

.825

CLAN!

-.825

.447 .436 .402

ADULT FEMALE RANK2 FEMALE WEALTHY RANK2 SUBADULT MALE WEALTHY RANK!

-.484 -.454 -.424

AXIS 2 ( 18.3 %) ADULT FEMALE RANK! FEMALE RANK! ADULT FEMALE WEALTHY RANK! AXIS 3 ( 12.8 %) ADULT ADULTRANK2 AXIS 4 ( 8.7 %) LEADER

.556

AXIS 5 ( 6.5 %) FEMALE WEALTHY RANK! ADULT FEMALE WEALTHY RANK!

.606 .554

AXIS 6 ( 5.5 %) CLAN2 AXIS 7 (5 %) FEMALE RANK! FEMALE AVERAGE RANK! ADULT FEMALE A VER. RANK! AXIS 8 ( 4 %) MALE WEALTHY RANK2

.526

AXIS 9 ( 3.4 %) MALERANK2

.502

*********************************************************************************************************** TABLE 8.59 Highest correlations on axes for Varimax-rotated PCA of Model 3A

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 20.8 %)

RANK! FEMALE RANK! AVERA GE RANK!

.86 .817 .81

RANK2 ADULTRANK2 FEMALE RANK2

-.86 -.559 -.52

MALE ADULT MALE

-.556 -.519

SUBADULT SUBAD FEMALE RANK2 SUBADUL T RANK2

-.841 -.645 -.63

AXIS 2 ( 18.3 %) MALERANKl ADULT MALE RANK! MALE WEALTHY RANK!

.851 .812 .64

AXIS 3 ( 12.8 %) ADULT FEMALE ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2

.898 .748 .631

AXIS 4 ( 8.7 %) ADULT FEMALE WEALTHY RANK! FEMALE WEALTHY RANK! WEALTHY RANK!

.854 .849 .67

AXIS 5 ( 6.5 %) ADULT ADULT MALE ADULT MALE RANK2

.841 .696 .624

207

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

*********************************************************************************************************** TABLE 8.59 (Continued)

*********************************************************************************************************** Positive

Negative

AXIS 6 ( 5.5 %) LEADER

.968

AXIS 7 ( 5 %) MALE WEALTHY RANK2 ADULT MALE WEALTHY RANKl

.709 .503

AXIS 8 ( 4 %) CLANl

.994

CLAN2

-.994

AXIS 9 ( 3.4 %) SUBADULT MALE RANKl SUBADULT MALE RANK2 SUBADULT MALE WEALTHY RANKl

.671 .572 .545

******************************************************************************************************************* TABLE 8.60 Highest correlations on axes for Oblimin-rotated PCA of Model 3A

************************************************************************************************************** Positive

Negative

AXIS 1 ( 20.8 %)

------ --------- ---------MALE RANK! ADULT MALE RANKl MALE WEALTHY RANKl

.875 .853 .652

AXIS 2 ( 18.3 %)

------ --------- ---------RANKl FEMALE RANKl AVERAGE RANKl

.92 .809 .806

RANK2 ADULTRANK2

-.92 -.582

.877 .627 .597

SUBADULT SUBADULT FEMALE RANK2 SUBADULT RANK2

-.877 -.7 -.668

.643 .632 .541

ADULT FEMALE ADULT FEMALE RANK2 FEMALE

-.89 -.703 -.643

.997

CLANl

-.997

ADULT FEMALE WEALTHY RANKl FEMALE WEALTHY RANKl WEALTHY RANK!

-.874 -.856 -.686

ADULT

-.533

AXIS 3 ( 12.8 %)

------ --------- ---------ADULT ADULTRANK2 ADULT MALE AXIS 4 ( 8.7 %)

------ --------- ---------LEADER ADULT MALE WEALTHY RANKl MALE WEALTHY RANK!

.989 .64 .515

AXIS 5 ( 6.5 %)

------ --------- ---------MALE ADULT MALE ADULT MALE RANK2 AXIS 6 ( 5.5 %)

------ --------- ---------CLAN2 AXIS 7 ( 5 %)

------ --------- ----------

AXIS 8 ( 4 %)

------ --------- ---------MALE WEALTHY RANK2 ADULT MALE WEALTHY RANKl MALE WEALTHY RANK!

.674 .643 .546

AXIS 9 ( 3.4 %)

------ --------- ---------SUBADULT MALE RANKl SUBADULT MALE WEALTHY RANKl SUBADULT RANKl

.741 .603 .564

208

Appendix 1

******************************************************************************************************** TABLE 8.61 Summary of highest correlations in PCNCA analyses of Model 3A

********************************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANKl 12 RANKl ADULT 13 RANKl ADULT MALE 14 RANKl ADULT FEMALE 15 RANKl SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEALTHY 19 RANKl MALE WEALTHY 20 RANKl ADULT MALE WEALTHY 21 RANKl SUBADMALEWEALTHY 22 RANKl FEMALE WEALTHY 23 RANKl ADULT FEMALE WEALTHY 24 RANKl SUB AD FEMALE WEALTHY 25 RANKl AVERAGE 26 RANKlMALEAVERAGE 27 RANKl ADULT MALE A VERA GE 28 RANKl SUB AD MALE A VERA GE 29 RANKl FEMALE AVERAGE 30 RANKl ADULT FEMALE A VERA GE 31 RANKl SUBADFEMALEAVERAGE 32 RANKl MALE 33 RANKl FEMALE 34 RANK2 35 RANK2SUBADULT 36 RANK2ADULT 37 RANK2MALE 38 RANK2 FEMALE 39 RANK2MALEADULT 40 RANK2ADULTFEMALE 41 RANK2SUBADULTFEMALE 42 RANK2SUBADULTMALE 43 RANK2 SUBADULT POOR 44RANK2SUBADULTAVERAGE 45 RANK2 AGED 40+ 46 RANK2 AGED 40+ MALE 47 RANK2AGED 40+FEMALE 48 RANK2 WEALTHY 49 RANK2 WEALTHY FEMALE 50 RANK2 WEALTHY MALE 51 RANK2ADULT AVERAGE 52 RANK2ADULT AVERAGE MALE 53 RANK2 ADULT AVERAGE FEMALE 54 RANK2ADULTPOOR 55 RANK2ADULTPOORMALE 56 RANK2ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.695 .695 -.353 -.925 .925 -.523 -.855 .855 -.926 .669 -.682 -.558 -.375 -.378 .435 -.303 .308 -.438 -.348 -.328 .377 -.456 -.402 .242 -.46 -.337 -.312 .219 -.319 .346 .289 -.497 -.396 .682 .776 -.856 -.74 .619 -.816 -.559 .845 .849 .725 -.514 -.554 -.625 .541 .637 -.464 .558 -.461 -.497 .544 -.293 -.268 .326

.696 -.696 -.148 -.773 .773 -.446 .856 -.856 .942 -.673 -.621 -.527 -.326 -.382 .493 .311 .367 -.323 -.264 .224 .205 -.235 -.223 .173 -.477 -.284 -.242 .229 -.357 -.336 .321 -.4 -.41 .621 .843 -.845 .759 -.598 .842 -.756 .776 .475 .741 .33 -.553 .636 -.568 -.272 -.337 .477 -.502 .514 -.517 -.348 .308 -.323

-.972 .972 .287 -.952 .952 -.587 .589 -.589 -.624 .655 .707 .576 .729 .725 .571 -.379 .429 -.678 .525 .494 -.322 -.476 .466 .174 .569 .532 .519 .264 .542 .534 .389 .77 .728 -.707 .661 -.68 -.681 -.723 -.67 -.57 .577 .257 .541 .322 -.484 -.523 -.427 -.364 -.335 -.371 -.374 -.43 -.365 .269 -.229 .229

-.825 .825 .605 .825 -.825 .483 .491 -.491 -.486 .666 .618 .619 .794 .821 -.691 -.495 -.459 .583 .745 .764 -.424 .656 .692 .238 -.493 -.411 -.477 -.367 .436 .478 -.426 .788 .744 -.618 -.437 .718 -.535 -.513 -.502 -.484 -.455 .489 .338 -.26 .493 -.384 -.407 .462 -.454 .526 .339 -.32 -.394 .19 -.189 -.202

.994 -.994 .968 .841 -.841 .568 -.556 .556 .696 .898 .86 .735 .812 .749 -.438 .671 -.41 .67 .64 .543 .545 .849 .854 .192 .81 .537 .584 .402 .727 .676 -.362 .851 .817 -.86 -.63 .613 .542 -.52 .624 .748 -.645 .572 -.516 -.305 .483 .559 .631 .376 .426 .709 .405 .468 .504 .227 .234 .22

-.997 .997 .989 .877 -.877 .586 .643 -.643 .632 -.89 .92 .791 .853 .747 .564 .741 -.448 -.686 .652 .643 .603 -.856 -.874 -.201 .806 .558 .61 .443 .691 .641 -.396 .875 .809 -.92 -.668 .627 .534 -.498 .541 -.703 -.7 .536 -.551 -.319 .488 .483 -.594 .331 -.406 .674 .4 .426 -.473 .22 .203 -.203

*********************************************************************************** TABLE 8.62 Top Jaccard values for Ward's method clustering of Model 4A

*********************************************************************************** GROUP

LEVEL

----------1 ADULT 2 SUBADULT 3 MALE WEALTHY 4 ADULTMALE 5 ADULT FEMALE 6 FEMALE WEALTHY 7 SUBADULT MALE CLAN2 POOR 8 SUBADULTMALE 9 ADULTFEMALECLANl 10 SUBADULTFEMALE 11 SUBADULTAVERAGE 12 ADULT MALE AVERAGE 13 ADULT FEMALE CLAN2 14 SUBADULT MALE CLANl POOR 15 SUBADULTFEMALECLANlPOOR 16 SUBADULTFEMALECLAN2 17 SUBADULTMALECLANl

JACCARD

---------------2 2 8 3 3 9 10 5 7 5 13 12 7 13 6 6 10

209

.984 .974 .941 .839 .836 .762 .647 .6 .591 .581 .571 .536 .524 .526 .5 .48 .429

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************** TABLE 8.62 (Continued)

********************************************************************************** GROUP

18 19 20 21

LEVEL

ADULT MALE CLAN2 SUBADULT CLANl POOR SUBADULT FEMALE CLAN2 POOR AGED 40+ FEMALE CLAN2 AVERAGE

14 4

11 14

JACCARD

.423 .303 .214 .2

********************************************************************************** TABLE 8 .63 Top Jaccard values for Average linkage clustering of Model 4A

********************************************************************************** GROUP

LEVEL

1 2 3 4

MALE WEALTHY ADULT ADULT FEMALE SUBADULT CLANl 5 ADULTMALE 6 FEMALE WEALTHY 7 SUBADUL T CLAN2 8 SUBADUL T MALE CLAN2 9 SUBADUL T FEMALE CLAN2 10 ADULT FEMALE CLANl 11 ADULTMALEAVERAGE 12 ADULT FEMALE CLAN2 POOR 13 ADULT FEMALE CLAN2 14 SUBADULT CLAN2 POOR 15 AGED 40+ FEMALE CLAN2 AVERAGE 16 SUBADULTFEMALEAVERAGE 17 AGED 40+ FEMALE CLAN2 18 ADULT MALE CLAN2 AVERAGE 19 AGED 40+ FEMALE CLANl WEALTHY 20 MALECLANl WEALTHY

13 4

7 8 9

14 3

10

5 14 13 11 12 10 15 6 9

15 8 2

JACCARD .941 .928 .847 .825 .782 .773 .708 .667 .556 .532 .515 .353 .312 .226 .167 .154 .138 .125 .1 .077

********************************************************************************** TABLE 8 .64 Top Jaccard values for monothetic divisive clustering of Model 4A

********************************************************************************** GROUP

LEVEL

1 2 3 4

SUBADULT CLANl ADULT SUBADULT SUBADUL T MALE CLAN2 5 SUBADUL T CLAN2 6 FEMALE WEALTHY 7 ADULTMALE 8 ADULT FEMALE 9 SUBADUL T FEMALE CLAN2 10 SUBADULT CLANl POOR 11 MALECLANl WEALTHY 12 ADULT MALE AVERAGE 13 ADULT FEMALE CLAN2 AVERAGE 14 AGED 40+ FEMALE AVERAGE 15 ADULT FEMALE AVERAGE 16 ADULTFEMALECLANl AVERAGE 17 AGED 40+ FEMALE CLAN2 AVERAGE 18 MALE WEAL THY 19 ADULT CLANl AVERAGE 20 AGED 40+ FEMALE CLAN2 WEALTHY

10 2 2

7 6

5 3 3

11 12 9 9 6 13 8 15 15 13 10 14

JACCARD .75 .736 .692 .667 .654 .619 .574 .571 .556 .514

.5 .452 .294 .27 .245 .25 .25 .235 .147 .125

********************************************************************************** TABLE 8 .65 Summary of top J accard values for clustering of Model 4A

********************************************************************************** SOCIAL GROUP 1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY

WARD .448@2 .314@ 2 .188@ 8 .984@ 2 ** .974@ 2 ** .6@5 .581@ 5 .571 @ 13 .789@ 2 * .839@ 3 * .836@ 3 * .421@ 8

AVERAGE .634@ 3 .434@2 .188@ 13 .928 @4 ** .506@ 3 .435@ 6 .371@ 5 .2@ 10 .453@ 8 .782@9 * .847 @7 * .436@ 14

210

DIVISIVE .398 @4 .495@4 .375@ 9 .736@2 * .692@2 .41 @7 .371@ 11 .2@7 .561@ 2 .574@ 3 .571 @ 3 .342@ 5

Appendix 1 ********************************************************************************** TABLE 8.65 (Continued)

********************************************************************************** SOCIAL GROUP 13 ) WEALTHY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVER FEMALE 21 )MALE 22 )FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD

AVERAGE

.762@ 9 * .941@ 8 ** .267@ 8 .283@ 8 .207@ 9 .435@ 2 .536@ 12 .412@ 3 .515@ 3 565@ 3 .624@2 .468@ 3 .577@ 3

DIVISIVE

.773@ 14 * .941@ 13 ** .244@4 .303@ 13 .25@ 11 .425@ 4 .515@ 13 .455@ 14 .47@7 .57@7 .593@ 4 .471@ 9 .586@ 7

.619@ 5 .471@ 9 .25 @5 .226@ 9 .276@ 8 .374@ 2 .452@ 9 .397@ 5 .333@ 3 .403@ 3 .496@ 2 .348@ 3 .429@ 3

********************************************************************************** TABLE 8.66 Top Jaccard values for Ward's method clustering of Model 4B

********************************************************************************** GROUP

LEVEL

JACCARD

I ADULT 2 ADULTMALE 3 FEMALE WEALTHY 4 ADULT FEMALE 5 MALE WEALTHY 6 ADULTFEMALECLAN2 7 CLAN! 8 SUBADULTFEMALEPOOR 9 SUBADULT CLAN2 10 ADULT CLANl 11 SUBADUL T MALE 12 ADULT MALE CLAN2 13 AGED 40+ FEMALE AVERAGE 14 SUBADULT 15 AGED 40+ FEMALE CLAN2 AVERAGE 16 SUBADULT CLAN! POOR 17 ADULTFEMALECLAN2POOR 18 FEMALE CLANl 19 SUBADULT CLAN2 POOR 20 ADULT CLANl POOR 21 ADULT MALE POOR 22 ADULT POOR

2 7

.726 .615 .571 .529 .476 .421 .409 .409 .382 .346 .323 .321 .304 .286 .286 .28 .267 .27 .24 .238 .19 .132

10 7

15 10 3 5 4 8 8

11

13 2 12 6 13 14 5 14 15 9

************************************************************************* TABLE 8.67 Top Jaccard values for Average linkage clustering of Model 4B

************************************************************************* GROUP

LEVEL

I SHAMAN 2 ADULT 3 SUBADULT 4 FEMALE WEALTHY 5 ADULT FEMALE CLAN2 6 ADULTMALE 7 CLAN! 8 ADULTMALEAVERAGE 9 SUBADULT FEMALE CLAN2 10 AGED 40+ FEMALE CLAN! AVERAGE 11 SUBADUL T MALE POOR 12 SUBADULTMALECLAN2POOR 13 AGED 40+ MALE CLANl POOR 14 AGED 40+ FEMALE 15 MALE CLAN2 WEALTHY 16 MALECLANl WEALTHY

11 5 13 15

JACCARD 1 .708 .478 .429 .294 .296 .298 .296 .231 .231 .208 .2 .154 .146 .143 .077

6 9

10 11 3

7 5 7 13 12 14 2

********************************************************************************** TABLE 8.68 Top Jaccard values for monothetic divisive clustering of Model 4B

********************************************************************************** GROUP

LEVEL

I SHAMAN 2 SUBADULT 3 FEMALE 4 ADULT

3 2 2

12

211

JACCARD 1 .598 .459 .411

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************* TABLE 8.68 (Continued)

********************************************************************************* GROUP

LEVEL

5 ADULT FEMALE 6 SUBADULT CLANl POOR 7 SUBADULT POOR 8 AGED 40+ CLANl WEAL THY 9 FEMALE WEALTHY 10 ADULTMALE 11 ADULT AVERAGE 12 SUBADULT CLAN2 POOR 13 SUBADULT FEMALE CLAN2 14 ADULTMALEAVERAGE 15 WEALTHY 16 ADULT CLANl AVERAGE 17 ADULTCLAN2AVERAGE 18 SUBADULTMALECLAN2POOR 19 FEMALE CLANl AVERAGE 20 ADULT FEMALE CLANl AVERAGE 21 AGED 40+ FEMALE CLAN2 WEALTHY 22 ADULTMALEPOOR 23 SUBADULT CLAN2 24 AGED 40+ AVERAGE

5 6 4

JACCARD .321 .321 .312 .316 .286 .259 .242 .237 .231 .222

10 11 5 10 6

15 12 7 7

.208

13 14

.2

.2 .176 .161 .167 .154 .154 .15 .14

8

11 9

13 14 15

****************************************************************************************** TABLE 8.69 Summary of top Jaccard values for clustering of Model 4B

****************************************************************************************** SOCIAL GROUP 1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .538@ 2 .488@ 3 .214@ 15 .726@2 * .286@ 2 .323@ 8 .357@ 5 .2@ 8 .3 @4 .615@ 7 .529@ 7 .326@4 .571@ 10 .476@ 15 .167@ 4 .19@ 15 .182@ 13 .388@ 4 .304@ 7 .349@ 10 .421@ 7 .53@2 .532@4 .429@9 .491@ 7

AVERAGE

DIVISIVE

.566@ 2 .431@ 2 1@ 11 ** .708@ 5 * .478@ 13 .321 @ 13 .263@ 13 .213@ 13 .348@ 13 .463@ 8 .398@ 3 .284@ 8 .429@ 15 .269@9 .2@ 15 .143@ 15 .192@ 6 .33@ 8 .296@ 11 .233@ 15 .478@ 6 .541@ 2 .449@3 .311 @ 8 .273@ 3

.433@ 2 .403 @2 1 @ 12 ** .411@ 2 .598@ 3 .309@ 3 .289@ 3 .186@ 9 .464@3 .312@ 2 .321@ 5 .254@ 2 .286@ 11 .24@5 .184@ 10 .154@ 13 .138@ 7 .262@ 2 .222@ 12 .228@ 5 .378@ 2 .459@2 .313@ 2 .242@2 .242@3

************************************************************************* TABLE 8.70 Top Jaccard values for Ward's method clustering of Model 4C

************************************************************************* GROUP 1 RANKl 2 ADULTRANK2 3 SUBADUL T RANK2 4 ADULT FEMALE RANK2 5 ADULT MALE RANK2 6 FEMALE WEALTHY RANKl 7 SUBADULT CLANl RANK2 8 ADULT MALE RANKl 9 FEMALE RANKl 10 SUBADULTCLAN2RANK2 11 FEMALE AVERAGE RANKl 12 SUBADULTCLAN1AVERAGERANK2 13 SUBADULTFEMALECLAN1RANK2 14 MALE WEALTHY RANK2 15 SUBADULT 16 SUBADULT MALE CLANl POOR 17 SUBADULTFEMALECLANlPOOR 18 ADULT FEMALE CLAN2 RANK2 19 ADULT MALE AVERAGE RANK2 20 ADULT FEMALE CLANl RANK2 21 ADULTMALEPOOR 22 SUBADULT FEMALE CLAN2 POOR 23 SUBADULT FEMALE RANK2

LEVEL

JACCARD

3

1

2 3 4 4

.99

.979 .939 .926 .889 .862 .833 .829 .773 .741 .75 .652 .652 .65 .625 .571 .429 .429

12 5 7 7

5 12 13 6

15 2 6

13 8

15 14 9

.4 .3

10 11

.25 .147

212

Appendix 1

********************************************************************************* TABLE 8.71 Top Jaccard values for Average linkage clustering of Model 4C

********************************************************************************* GROUP 1 RANK2 2 RANKl 3 ADULT RANK2 4 SUBADULT RANK2 5 ADULT MALE RANK2 6 ADULT FEMALE RANK2 7 FEMALE WEAL THY RANKl 8 SUBADULT CLANl RANK2 9 MALERANKl 10 SUBADULT CLAN2 RANK2 11 FEMALE AVERAGE RANKl 12 SUBADULT CLANl POOR 13 SUBADULT CLANl AVERAGE RANK2 14 AGED 40+ MALE CLAN2 POOR 15 AGED 40+ FEMALE CLANl AVERAGE RANK2 16 AGED 40+ MALE WEALTHY RANK2 17 FEMALE CLAN2 WEALTHY RANK2 18 SUBADULTFEMALECLAN2AVERAGERANK1 19 AGED 40+ MALE CLANl WEALTHY RANK2

LEVEL 2 2

3 3 6 6

8 5

12 11 12 10 10 13 7

14 11 4

15

JACCARD 1 1 .98 .958 .909 .898 .889 .844 .84 .789 .708 .56 .538 .429 .333 .222 .2

.143 .143

********************************************************************************** TABLE 8.72 Top Jaccard values for monothetic divisive clustering of Model 4C

********************************************************************************** GROUP 1 MALE AVERAGE RANKl 2 ADULT RANK2 3 RANKl 4 MALERANKl 5 SUBADULT CLANl RANK2 6 FEMALE RANKl 7 SUBADULT RANK2 8 ADULT FEMALE RANK2 9 MALE WEALTHY RANKl 10 SUBADULT 11 FEMALEWEALTHYRANKl 12 ADULTMALERANK2 13 AGED 40+ MALE CLANl AVERAGE RANKl 14 FEMALEAVERAGERANKl 15 SUBADULT CLANl POOR 16 FEMALE WEALTHY RANK2 17 CLAN2 POOR 18 SUBADULTFEMALERANK2 19 SUBADULT CLAN2 RANK2 20 SUBADULT CLAN2 RANKl 21 WEALTHY RANK2 22 ADULT MALE CLAN2 RANK2

LEVEL

JACCARD

10

.786

2 3 7

.77

13

.677

7 4

.645 .639 .638

.736 .708

5 10 3

14 5

13 14 15 11 6 9

12 4 8

12

.6

.558 .538 .519 .5

.476 .476 .438 .426 .412 .367 .316 .286 .188

*************************************************************************************************** TABLE 8.73 Summary of top Jaccard values for clustering of Model 4C

*************************************************************************************************** SOCIAL GROUP 1 )CLANl 2)CLAN2 3)LEADER 4)ADULT 5 )SUBADULT 6 )AGED 40+ ?)MALE 8)FEMALE 9 )ADULT MALE 10 )ADULT FEMALE 11 )RANKl 12 )RANKl ADULT 13 )RANKl ADULT MALE 14)RANK1ADULTFEMALE 15 )RANKl SUBADULT 16 )RANKl SUBADULTMALE 17)RANK1SUBADULTFEMALE 18 ) RANKl WEALTHY 19 ) RANKl MALE WEAL THY 20 ) RANKl ADULT MALE WEAL THY 21 )RANKl SUBADULTMALEWEALTHY 22 ) RANKl FEMALE WEALTHY 23 ) RANKl ADULT FEMALE WEAL THY 24 )RANKl SUBADULTFEMALEWEALTHY 25 )RANKl AVERAGE

WARD .331 @ 2 .34@2 .111@7 .739@ 2 * .697@ 3 .483@ 2 .556@ 4 .432@ 2 .725 @4 * .676@4 1@ 3 ** .642@ 3 .833@ 7 * .543@ 7 .438@ 12 .2@ 12 .286@ 7 .421 @ 12 .474@ 7 .333@ 7 .158@ 7 .889@ 12 * .667@ 12 .25@ 12 .694@ 12

213

AVERAGE .452@ 2 .395@ 2 .091@ 12 .731 @ 3 * .687@ 3 .487@ 3 .549@ 6 .457@ 2 .714@ 6 * .647@ 6 1 @2 ** .654@4 .682@ 12 .429@ 12 .358@ 2 .24@ 12 .24@ 12 .421 @ 8 .455@ 12 .273@ 12 .182@ 12 .889@ 8 * .667@ 8 .25@ 8 .733@ 8 *

DIVISIVE .358@ 2 .403 @2 .333 @ 10 .575@ 2 .558@ 3 .357@ 2 .315@ 2 .453@ 2 .406@5 .481@ 5 .736@ 3 * .587@ 3 .524@ 7 .577@ 7 .261@ 3 .3 @7 .235 @4 .364@ 14 .6@ 10 .5@ 10 .25@ 10 .538@ 14 .5@ 14 .083 @4 .521 @ 3

Theoretical and Quantitative Approaches to the Study of Mortuary Practice *************************************************************************************************** TABLE 8.73 (Continued)

*************************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

.391 @ 7 .5 @7 .185@ 12 .741@ 12 * .444@ 12 .296@ 12 .75@ 7 * .829 @7 * .678@ 2 .979@ 3 ** .99@ 2 ** .758 @4 * .554@4 .926@4 ** .939 @4 ** .723@ 3 * .429@6 .766@ 3 * .6@ 13 586@2 .608@4 .531 @ 4 .405@ 15 .406@ 8 .652@ 15 .465@ 2 .451 @4 .449@4 .303@ 8 .3 @9 .4@ 8

26 ) RANKl MALE A VERA GE 27 )RANKl ADULT MALE AVERAGE 28 )RANKl SUBADULTMALEAVERAGE 29 ) RANKl FEMALE AVERAGE 30 )RANKl ADULT FEMALE AVERAGE 31)RANK1SUBADULTFEMALEAVERAGE 32 )RANKl MALE 33 ) RANKl FEMALE 34 )RANK2 35 ) RANK2 SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 )RANK2MALEADULT 40)RANK2ADULTFEMALE 41)RANK2SUBADULTFEMALE 42 ) RANK2 SUB ADULT MALE 43)RANK2SUBADULTPOOR 44)RANK2SUBADULTAVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 ) RANK2 AGED 40+ FEMALE 48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEAL THY MALE 51 )RANK2ADULT AVERAGE 52 ) RANK2 ADULT A VERA GE MALE 53)RANK2ADULTAVERAGEFEMALE 54)RANK2ADULTPOOR 55 )RANK2ADULTPOORMALE 56)RANK2ADULTPOORFEMALE

.44@ 12 .409@ 12 .13@ 12 .708@ 12 * .5@ 12 .261@ 12 .84@ 12 * .562@ 12 1 @2** .958@ 3 ** .98@ 3 ** .746@ 6 * .548@ 2 .909@ 6 ** .898@ 6 * .708@ 3 * .25@3 .75@ 3 * .467@ 10 .592@ 3 .596@ 6 .553@ 6 .283@ 3 .289@ 7 .326@ 13 .469@3 .457@ 14 .468@6 .242@3 .222@6 .2@7

DIVISIVE .786 @10* .538@ 10 .333@ 10 .476@ 14 .438@ 14 .188@ 4 .708 @7 * .645 @7 .527 @2 .639 @4 .77@ 2 * .424@ 5 .402@ 5 .519@ 5 .638@ 5 .472 @4 .276@ 13 .5@4 .278@ 15 .436@ 2 .364@ 5 .339@ 5 .3@ 5 .438@ 11 .192@ 11 .382@ 2 .333@ 5 .394@ 8 .229 @2 .265@ 5 .172@ 8

************************************************************************* TABLE 8.74 Top Jaccard values for Ward's method clustering of Model 4D

************************************************************************* GROUP 1 RANKl 2 SUBADULT CLANl RANK2 3 SUBADUL T RANK2 4 ADULT 5 MALE WEALTHY RANKl 6 RANK2 7 SUBADULT CLANl POOR 8 SUBADULT FEMALE CLANl RANK2 9 FEMALE AVERAGE RANKl 10 ADULTRANK2 11 FEMALE WEAL THY RANK2 12 ADULT FEMALE RANK2 13 SUBADULTCLAN2RANK2 14 SUBADULT FEMALE CLAN2 RANK2 15 ADULTMALERANK2 16 ADULT MALE CLAN2 RANK2 17 ADULT POOR 18 SUBADULTMALECLANl POOR 19 AGED40+FEMALERANK2 20 ADULTCLANl RANK2 21 AGED 40+ MALE CLANl RANK2

LEVEL

JACCARD

5

.821 .679 .609 .5 .5 .489 .476 .471 .474 .47 .429 .385 .35 .333 .328 .324 .306 .222 .207 .176 .176

4 3

3 14 2 6 6

15 2

13 7 4

12 7 8

9

11 13 10 12

********************************************************************************* TABLE 8.75 Top Jaccard values for Average linkage clustering of Model 4D

********************************************************************************* GROUP

LEVEL

1 RANK2 2 MALE CLAN2 AVERAGE RANKl 3 SUBADULTFEMALECLAN2RANK1 4 RANKl 5 MALE WEALTHY RANKl 6 SUBADUL T FEMALE RANK2 7 ADULT FEMALE WEALTHY RANKl 8 FEMALE WEALTHY RANK2 9 SUBADULT MALE CLAN2 RANK2 10 MALE CLAN2 WEAL THY RANK2 11 ADULTFEMALECLAN1RANK2 12 SUBADULT MALE CLANl RANK2

5 15 15 5 9 11 10 7 2

14 6

12

214

JACCARD .809 .583 .571 .54 .5 .457 .444 .429 .333 .333 .308 .267

Appendix 1 ********************************************************************************* TABLE 8.75 (Continued)

********************************************************************************* GROUP 13 14 15 16 17 18

LEVEL

JACCARD

7 3

.259 .25 .25 .176 .16 .133

FEMALECLAN1AVERAGERANK2 MALE CLAN2 WEALTHY RANKl AGED 40+ MALE CLAN2 POOR ADULT FEMALE CLANl AVERAGE RANK2 SUBADULTFEMALECLAN2 ADULT FEMALE CLAN2 AVERAGE RANK2

14 12 13 8

************************************************************************ TABLE 8.76 Top Jaccard values for monothetic divisive clustering of Model 4D

************************************************************************ GROUP 1 MALE CLAN2 RANKl 2 RANK2 3 ADULTFEMALERANKl 4 SUBADULT CLANl POOR 5 RANKl 6 SUBADULTFEMALECLAN2RANK1 7 LEADER 8 ADULTRANK2 9 ADULTMALECLANl AVERAGE RANK! 10 SUBADUL T RANK2 11 SUBADULT CLAN2 RANK2 12 FEMALE CLAN! AVERAGE RANK2 13 CLAN2 RANK2 14 CLAN2 15 ADULTCLAN2POOR 16 SUBADULTFEMALECLAN2RANK2 17 MALE CLAN2 18 FEMALE CLAN2 WEAL THY RANK2 19 AGED 40+ FEMALE AVERAGE RANK2 20 AGED 40+ CLAN2 AVERAGE 21 AGED 40+ RANK2

LEVEL

JACCARD .571 .57 .545 .526 .5 .5 .5 .46 .4 .356 .277 .273 .264 .259 .24 .211 .213 .182 .182 .179 .172

8 3

8 6 3

12 15 2

10 4 10

15 6 5 9

11 12 7 14 13 14

***************************************************************************************** TABLE 8.77 Summary of top Jaccard values for clustering of Model 4D

***************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2)CLAN2 3)LEADER 4 )ADULT 5)SUBADULT 6 )AGED 40+ 7)MALE 8)FEMALE 9 )ADULT MALE 10 )ADULT FEMALE 11 )RANKl 12 )RANK! ADULT 13 )RANK! ADULT MALE 14)RANK1ADULTFEMALE 15 )RANK! SUBADULT 16 )RANK! SUBADULTMALE 17)RANK1SUBADULTFEMALE 18 ) RANK! WEALTHY 19 ) RANK! MALE WEAL THY 20 ) RANKl ADULT MALE WEAL THY 21 )RANK! SUBADULTMALEWEALTHY 22 ) RANK! FEMALE WEALTHY 23 ) RANKl ADULT FEMALE WEAL THY 24 )RANK! SUBADULTFEMALEWEALTHY 25 )RANK! AVERAGE 26 ) RANKl MALE A VERA GE 27) RANK! ADULT MALE AVERAGE 28 )RANK! SUBADULTMALEAVERAGE 29 ) RANKl FEMALE A VERA GE 30 )RANK! ADULT FEMALE AVERAGE 31 )RANK! SUBADULTFEMALEAVERAGE 32) RANKl MALE 33 ) RANK! FEMALE 34 )RANK2 35)RANK2SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 ) RANK2 MALE ADULT 40)RANK2ADULTFEMALE

.448@ 2 .412@ 2 .143@ 14 .5@3 .452@ 3 .343@ 3 .394@ 3 .479@ 2 .309@ 3 .289@ 3 .821@ 5 * .6@ 14 .312@ 15 .39@ 14 .283@ 5 .211 @ 14 .2@ 15 .34@5 .5@ 14 .25@ 14 .286@ 14 .286@ 15 .214@ 15 .074@ 15 .683@ 14 .414@ 15 .286@ 15 .143@ 15 .474@ 15 .353@ 15 .231 @ 15 .462@ 5 .524@ 14 .489@ 2 .609@ 3 .47@2 .287@ 2 .321@ 2 .329@ 2 .385@ 7

.5@4 .505@ 2 .182@ 15 .713@ 3 * .297@ 4 .42@3 .463@ 2 .561@ 4 .367@ 3 .363@ 5 .54@5 .419@ 5 .529@ 15 .316@ 10 .265@ 5 .273@ 9 .5@ 15 .394@ 5 .5@ 9 .222@9 .5@ 9 .364@ 10 .444@ 10 .167@ 15 .359@ 9 .471@ 15 .429@ 15 .143@ 15 .222@ 15 .125@ 10 .429@ 15 .458@ 15 .297@ 9 .809@ 5 * .348@ 11 .571 @ 5 .39@ 11 .49@ 5 .354@ 11 .301 @ 5

.428@2 .435@ 2 .5@ 15 .463@ 2 .389@ 2 .304@2 .379@ 2 .485@ 2 .278@ 2 .267@2 .5@ 3 .439@ 3 .25@ 8 .545@ 8 .238@ 8 .357@ 8 .444@ 12 .257@ 3 .176@ 8 .167@ 15 .167@ 8 .263@ 8 .294@ 8 .143 @ 12 .4@3 .412@ 8 .3@ 10 .25@ 8 .333@ 8 .35@8 .375@ 12 .417@ 8 .464@ 8 .57@ 3 .356@4 .46@2 .291@ 2 .386@ 3 .333@ 2 .296@ 2

215

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ****************************************************************************************** TABLE 8.77 (Continued)

****************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

.457@ 11 .222@ 12 .324@ 13 .188@ 12 .327@ 5 .202@ 11 .17@ 5 .207@7 .429@7 .2@ 14 .276@ 5 .16@ 11 .194@ 7 .192@ 11 .129@ 11 .071@6

.294 @4 .211@ 6 .338@ 4 .111 @ 13 .351 @ 2 .238@ 2 .217@ 9 .172@ 2 .111 @ 2 .115@ 10 .278@ 2 .186@ 2 .182@ 9 .268@ 2 .173@ 2 .13@ 9

.537@ 3 .25@6 .571@ 3 .158@ 14 .346@ 2 .286@ 7 .238@ 7 .246@5 .429@ 13 .24@8 .292@2 .222@7 .237@ 9 .306@ 9 .276@9 .118@ 2

41)RANK2SUBADULTFEMALE 42 ) RANK2 SUB ADULT MALE 43)RANK2SUBADULTPOOR 44)RANK2SUBADULTAVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 ) RANK2 AGED 40+ FEMALE 48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEAL THY MALE 51 )RANK2ADULT AVERAGE 52 ) RANK2 ADULT A VERA GE MALE 53)RANK2ADULTAVERAGEFEMALE 54)RANK2ADULTPOOR 55 )RANK2ADULTPOORMALE 56)RANK2ADULTPOORFEMALE

**************************************************************************************** TABLE 8.78 Highest correlations on axes for correspondence analysis of Model 4A

**************************************************************************************** Positive

Negative

AXIS 1 ( 14.95 %) SUBADULT SUBADULTPOOR SUBADULT FEMALE

.851 .685 .547

ADULT AGED 40+

-.851 -.533

.784 .625 .548

ADULT MALE MALE MALE WEALTHY

-.62 -.548 -.516

.562

SUBADULT FEMALE

-.547

.648

CLANl

-.648

WEALTHY

-.547

.61

SUBADULTPOOR

-.516

.454

FEMALE WEALTHY

-.482

AXIS 2 ( 12.22 %) ADULT FEMALE AGED 40+ FEMALE FEMALE AXIS 3 ( 12.01 %) SUBADULT MALE AXIS 4 ( 8.99 %) CLAN2 AXIS 5 ( 7.46 %)

AXIS 6 ( 7.14 %) SUBADULT AVERAGE AXIS 7 ( 5.76 %) ADULT FEMALE AVERAGE

**************************************************************************************** TABLE 8 .79 Highest correlations on axes for detrended correspondence analysis of Model 4A

**************************************************************************************** Positive

Negative

AXIS 1 ( 14.95 %) SUBADULT SUBADULTPOOR SUBADULT FEMALE

.872 .721 .594

ADULT ADULT MALE AGED 40+

-.872 -.562 -.537

SUBADULT FEMALE

-.47

CLANl

-.538

AXIS 2 ( 9.29 %)

AXIS 3 ( 7.8 %) CLAN2

.538

AXIS 4 ( 4.81 %) FEMALE WEALTHY

.412

216

Appendix 1

***************************************************************************************** TABLE 8.80 Highest correlations on axes for unrotated PCA (covariance matrix) of Model 4A

***************************************************************************************** Positive

Negative

AXIS 1 ( 18.97 %) ADULT WEALTHY AGED 40+

.817 .563 .556

SUBADULT SUBADULT POOR SUBADUL T MALE

-.817 -.681 -.539

.695 .683 .584

ADULT MALE MALE AGED 40+ MALE

-.784 -.683 -.59

.775

CLAN2

-.775

.177 .145 .13

SUBADULT POOR CLAN!

-.146 -.145

.739 .571

ADULT AVERAGE

-.523

MALE WEALTHY

-.241

AXIS 2 ( 14.68 %) ADULT FEMALE FEMALE AGED 40+ FEMALE AXIS 3 ( 12.36 %) CLAN! AXIS 4 ( 9.57 %) SUBADULT AVERAGE CLAN2 ADULT MALE POOR AXIS 5 ( 6.48 %) WEALTHY FEMALE WEALTHY AXIS 6 ( 5.72 %)

************************************************************************************************* TABLE 8.81 Highest correlations on axes for unrotated PCA (correlation matrix) of Model 4A

************************************************************************************************* Positive

Negative

AXIS 1 ( 18.3 %) MALE WEALTHY ADULT MALE LEADER

.803 .702 .605

SUBADULT

-.509

.79 .784 .687

SUBADULT MALE

-.518 -.517

CLAN!

-.749

ADULT FEMALE AVERAGE

-.515

SUBADULT FEMALE

-.644

MALE WEALTHY

-.452

SUBADULT POOR

-.318

AXIS 2 ( 15.8 %) FEMALE WEALTHY ADULT FEMALE AGED 40+ FEMALE AXIS 3 ( 9.2 %) LEADER

.64

AXIS 4 ( 7.2 %) CLAN2

.749

AXIS 5 ( 6.9 %)

AXIS 6 ( 5.4 %) SUBADUL T MALE

.585

AXIS 7 ( 5.1 %)

AXIS 8 ( 4.9 %)

217

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

***************************************************************************************** TABLE 8.82 Highest correlations on axes for Varimax-rotated PCA of Model 4A

***************************************************************************************** Positive

Negative

AXIS 1 ( 18.3 %) ADULT MALE AGED 40+ MALE ADULT

SUBADULT SUBADULTPOOR

-.635 -.505

SUBADULT MALE

-.569 -.529

.811

CLAN2

-.811

.6

SUBADULT FEMALE

-.639

.778

.648 .635

AXIS 2 ( 15.8 %) FEMALE WEALTHY WEALTHY

.889 .717

AXIS 3 ( 9.2 %) ADULT FEMALE AGED 40+ FEMALE ADULT FEMALE AVERAGE

.819 .737 .655

AXIS 4 ( 7.2 %) LEADER

.976

AXIS 5 ( 6.9 %) MALE WEALTHY WEALTHY

.879 .573

AXIS 6 ( 5.4 %) CLANl AXIS 7 ( 5.1 %) SUBADULT MALE AXIS 8 ( 4.9 %) SUBADULT AVERAGE

.39

*********************************************************************************************************** TABLE 8.83 Highest correlations on axes for Oblimin-rotated PCA of Model 4A

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 18.3 %)

------ --------- ---------ADULT MALE ADULT AGED 40+ MALE

.787 .668 .649

SUBADULT SUBADULTPOOR

-.668 -.53

.811

CLANl

-.811

.592 .544

ADULT FEMALE AGED 40+ FEMALE ADULT FEMALE AVERAGE

-.846 -.758 -.625

.607

SUBADULT FEMALE

-.632

MALE WEALTHY WEALTHY

-.946 -.596

AXIS 2 ( 15.8 %)

------ --------- ---------FEMALE WEALTHY WEALTHY

.917 .741

AXIS 3 ( 9.2 %)

------ --------- ---------LEADER

.994

AXIS 4 ( 7.2 %)

------ --------- ---------CLAN2 AXIS 5 ( 6.9 %)

------ --------- ---------SUBADULT MALE

AXIS 6 ( 5.4 %)

------ --------- ---------SUBADULT MALE AXIS 7 ( 5.1 %)

------ --------- ----------

AXIS 8 ( 4.9 %)

------ --------- ---------SUBADULT AVERAGE

.449

218

Appendix 1

*********************************************************************************************** TABLE 8.84 Summary of highest correlations in PCNCA analyses of Model 4A

***********************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBADULTFEMALE 8 SUBADULTAVERAGE 9 SUBADULT POOR 10 ADULTMALE 11 ADULTFEMALE 12 WEALTHY 13 WEALTHY FEMALE 14 WEALTHY MALE 15 ADULTPOOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULT AVERAGE MALE 20 ADULT A VERA GE FEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.648 .648 -.337 -.851 .851 .562 .547 .61 .685 -.62 .784 -.547 -.482 -.516 .239 .257 .277 -.362 .356 .474 -.548 .548 -.533 -.445 .625

-.538 .538 -.23 -.872 .872 .491 .594 .353 .721 -.562 .398 -.505 .412 -.463 -.196 -.166 -.123 -.337 -.306 .221 -.287 .287 -.537 -.391 .315

.775 -.775 -.226 .817 -.817 -.539 -.476 -.322 -.681 -.784 .695 .739 .571 -.535 -.373 -.241 -.263 -.523 -.446 -.412 -.683 .683 .556 -.59 .584

-.749 .749 .64 .518 -.518 .585 -.644 .296 -.414 .702 .784 .601 .79 .803 -.246 .319 -.285 -.419 -.472 -.515 -.517 .517 .445 .539 .687

.811 -.811 .976 .635 -.635 .6 -.639 .39 -.505 .778 .819 .717 .889 .879 .233 .345 .265 .4 .578 .655 -.529 .529 .463 .648 .737

-.811 .811 .994 .668 -.668 .607 -.632 .449 -.53 .787 -.846 .741 .917 -.946 .215 .32 -.247 -.383 .547 -.625 .544 -.544 -.486 .649 -.758

************************************************************************************************** TABLE 8.85 Highest correlations on axes for correspondence analysis of Model 4B

************************************************************************************************** Positive

Negative

AXIS 1 ( 11.53 %) SUBADULT SUBADULT FEMALE SUBADULT POOR

.653 .599 .556

ADULT

-.653

.551 .532

ADULT MALE MALE

-.57 -.532

.504

CLANl

-.504

LEADER SUBADULT AVERAGE

-.386 -.339

FEMALE WEALTHY

-.351

WEALTHY FEMALE WEALTHY

-.197 -.193

AXIS 2 ( 10.28 %) SUBADUL T MALE

.479

AXIS 3 ( 9.23 %) ADULT FEMALE FEMALE AXIS 4 ( 7.61 %) CLAN2 AXIS 5 ( 6.73 %) SUBADULT AVERAGE

.362

AXIS 6 ( 6.46 %) SUBADULT AVERAGE

.344

AXIS 7 ( 6.1 %) ADULT MALE AVERAGE

.391

AXIS 8 ( 5.8 %)

AXIS 9 ( 5.57 %) SUBADULT POOR

.345

AXIS 10 ( 5.2 %) ADULT FEMALE AVERAGE ADULT AVERAGE ADULT MALE POOR

.183 .144 .118

219

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

*********************************************************************************************************** TABLE 8 .86 Highest correlations on axes for detrended correspondence analysis of Model 4 B

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 11.53 %) SUBADULT SUBADULTPOOR SUBADULT FEMALE

.757 .627 .575

ADULT AGED 40+

-.757 -.502

SUBADULT FEMALE

-.542

FEMALE ADULT FEMALE ADULT FEMALE AVERAGE

-.396 -.33 -.314

AXIS 2 ( 8.27 %)

AXIS 3 ( 6.42 %) ADULT FEMALE AVERAGE

.365

AXIS 4 ( 5.14 %) MALE ADULT MALE ADULT MALE AVERAGE

.396 .327 .327

*************************************************************************************************** TABLE 8.87 Highest correlations on axes for unrotated PCA (covariance matrix) of Model 4B

*************************************************************************************************** Positive

Negative

AXIS 1 ( 16.35 %) ADULT

.615

SUBADULT

-.615

.217 .212

SUBADULTPOOR

-.215

.556

CLAN2

-.556

.54 .536

ADULT FEMALE FEMALE AGED 40+ FEMALE

-.618 -.54 -.528

ADULT FEMALE

-.336

ADULT AVERAGE

-.325

AXIS 2 ( 12.16 %)

WEALTHY ADULT MALE AXIS 3 ( 10.64 %) CLANl AXIS 4 ( 9.5 %) MALE ADULT MALE AXIS 5 ( 8.02 %)

AXIS 6 ( 6.06 %) WEALTHY FEMALE WEALTHY

.496 .46

AXIS 7 ( 5.25 %) WEALTHY FEMALE WEALTHY

.333 .332

************************************************************************************************** TABLE 8.88 Highest correlations on axes for unrotated PCA (correlation matrix) of Model 4B

************************************************************************************************** Positive

Negative

AXIS 1 ( 15.8 %) LEADER MALE WEALTHY ADULT MALE

.801 .701 .541

AXIS 2 ( 10.6 %) FEMALE WEALTHY ADULT FEMALE WEALTHY

.74 .61 .54

SUBADULT

AXIS 3 ( 8.1 %)

220

-.513

Appendix 1

************************************************************************************************** TABLE 8.88 (Continued)

************************************************************************************************** Positive LEADER

.509

Negative ADULT MALE

-.503

ADULT FEMALE AVERAGE

-.494

.535

CLAN2

-.535

.332 .312

SUBADULT FEMALE

-.388

AGED 40+ MALE

-.201

ADULT MALE AVERAGE

-.22

ADULT MALE POOR

-.382

AXIS 4 (7 %)

AXIS 5 ( 6.6 %) CLANl AXIS 6 ( 5.4 %) SUBADUL T MALE SUBADULT AVERAGE AXIS 7 ( 5.1 %)

AXIS 8 (5 %)

AXIS 9 ( 4.8 %)

************************************************************************************************** TABLE 8.89 Highest correlations on axes for Varimax-rotated PCA of Model 4B

************************************************************************************************** Positive

Negative

AXIS 1 ( 15.8 %) LEADER

.972

AXIS 2 ( 10.6 %) FEMALE WEAL THY WEALTHY

.812 .672

AXIS 3 ( 8.1 %) MALE WEALTHY ADULT MALE

.565 .52

AXIS 4 (7 %) AGED 40+ MALE

.534

AXIS 5 ( 6.6 %) CLANl

.576

CLAN2

-.576

SUBADULT FEMALE

-.605

ADULT AVERAGE ADULT MALE AVERAGE

-.158 -.137

AXIS 6 ( 5.4 %) SUBADULT AVERAGE

.501

AXIS 7 ( 5.1 %) AGED 40+ FEMALE ADULT FEMALE

.51 .507

AXIS 8 (5 %)

AXIS 9 ( 4.8 %) SUBADULT AVERAGE ADULT MALE POOR

.169 .154

221

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

***************************************************************************************** TABLE 8.90 Highest correlations on axes for Oblimin-rotated PCA of Model 4B

***************************************************************************************** Positive

Negative

AXIS I ( 15.8 %) LEADER MALE WEALTHY

.964 .537

AXIS 2 ( 10.6 %) FEMALE WEAL THY WEALTHY

.82 .686

AXIS 3 ( 8.1 %) MALE WEALTHY ADULT MALE

-.616 -.587

ADULT FEMALE AGED 40+ FEMALE

-.528 -.525

CLAN2

-.581

SUBADULT FEMALE

-.622

ADULT AVERAGE

-.203

AGED 40+ MALE

-.584

AXIS 4 (7 %)

AXIS 5 ( 6.6 %) CLAN!

.581

AXIS 6 ( 5.4 %) SUBADULT AVERAGE

.521

AXIS 7 ( 5.1 %)

AXIS 8 ( 5 %)

AXIS 9 ( 4.8 %)

------ --------- ----------

*********************************************************************************************** TABLE 8.91 Summary of highest correlations in PCA/CA analyses of Model 4B

***********************************************************************************************

I 2 3 4

CLAN! CLAN2 LEADER ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBADULTFEMALE 8 SUBADULT AVERAGE 9 SUBADULTPOOR 10 ADULTMALE 11 ADULTFEMALE 12 WEALTHY 13 WEALTHY FEMALE 14 WEALTHY MALE 15 ADULTPOOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULTAVERAGEMALE 20 ADULT AVERAGEFEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.504 .504 -.386 -.653 .653 .479 .599 .362 .556 -.57 .551 -.298 -.351 -.468 -.169 .118 .216 -.288 .391 .326 -.532 .532 -.416 -.423 .46

-.374 .374 -.23 -.757 .757 .392 .575 .32 .627 -.495 -.33 -.382 -.212 -.366 -.142 -.116 .22 -.314 .327 .365 .396 -.396 -.502 -.401 -.281

.556 -.556 .333 .615 -.615 -.401 -.383 -.295 -.488 .536 -.618 .496 -.485 .419 -.209 .172 -.212 -.325 .288 -.273 .54 -.54 .421 .408 -.528

.535 -.535 .801 .513 -.513 .332 -.388 .312 -.394 .541 .61 .54 .74 .701 -.229 -.382 -.262 -.365 -.397 -.494 -.348 .348 .397 .463 .5

.576 -.576 .972 .402 -.402 .467 -.605 .501 -.383 .52 .507 .672 .812 .565 .223 .329 .301 .317 .285 .315 -.392 .392 .341 .534 .51

.581 -.581 .964 -.467 .467 .487 -.622 .521 -.418 -.587 -.528 .686 .82 -.616 -.193 -.309 -.293 .326 -.304 -.311 -.417 .417 -.351 -.584 -.525

222

Appendix 1 *********************************************************************************************************** TABLE 8.92 Highest correlations on axes for correspondence analysis of Model 4C

*********************************************************************************************************** Positive

Negative

AXIS I ( 13 %) RANK! ADULT RANK! AVERAGE RANK!

.946 .704 .703

RANK2 ADULTRANK2 ADULT MALE RANK2

-.946 -.79 -.501

.801

ADULT

-.64

ADULT MALE RANK2 MALERANK2 ADULT MALE

-.633 -.589 -.581

.682 .681 .505

MALE WEALTHY RANK! ADULT MALE RANK! MALERANKl

-.539 -.528 -.522

.427 .402

SUBADULT POOR RANK2

-.432

.481

SUBADULT POOR RANK2

-.448

.434

CLAN!

-.434

FEMALE AVERAGE RANK! LEADER

-.506 -.503

AXIS 2 ( 9.85 %) SUBADUL T RANK2 SUBADUL T FEMALE RANK2 SUBADULT

.766 .64

AXIS 3 ( 8.7 %) ADULT FEMALE RANK2 ADULT FEMALE AGED 40+ FEMALE RANK2

.785 .715 .559

AXIS 4 ( 6.95 %) SUBADUL T MALE RANK2

.725

AXIS 5 ( 6.59 %) ADULT FEMALE RANK! FEMALE RANK! ADULT FEMALE AVERAGE RANK! AXIS 6 ( 5.43 %) FEMALE WEALTHY RANK! WEALTHY RANK! AXIS 7 ( 4.96 %) SUBADULT AVERAGE RANK2 AXIS 8 ( 4.22 %) CLAN2 AXIS 9 ( 3.93 %) MALE WEALTHY RANK2

.538

AXIS 10 ( 3.65 %) FEMALE WEALTHY RANK2

.423

AXIS 11 ( 3.35 %)

AXIS 12 ( 2.94 %) AGED 40+ RANK2 AGED 40+

.327 .315

********************************************************************************************************** TABLE 8.93 Highest correlations on axes for detrended correspondence analysis of Model 4C

********************************************************************************************************** Positive

Negative

AXIS 1 ( 13 %) RANK! AVERAGE RANK! ADULTRANKl

.904 .676 .669

RANK2 ADULTRANK2 ADULT MALE RANK2

AXIS 2 ( 7.42 %) ADULT FEMALE RANK2 ADULT FEMALE AGED 40+ FEMALE RANK2

.754 .632 .524

AXIS 3 ( 5.88 %)

223

-.904 -.81 -.543

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************************************** TABLE 8.93 (Continued)

********************************************************************************************************** Positive MALERANK2 MALE ADULT MALE RANK2

Negative

.654 .567 .549

FEMALE RANK2 FEMALE

-.732 -.567

.496 .427

MALE MALE AVERAGE RANKl

-.496 -.407

AXIS 4 ( 3.93 %) FEMALE FEMALE RANK2

*********************************************************************************************************** TABLE 8.94 Highest correlations on axes for unrotated PCA (covariance matrix) of Model 4C

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 20.74 %) RANKl ADULTRANKl MALERANKl

.876 .711 .662

RANK2 ADULTRANK2 AGED 40+ RANK2

-.876 -.804 -.515

.773

CLANl

-.773

.709 .653 .63

ADULT MALE RANK2 MALERANK2 ADULT MALE

-.701 -.66 -.654

.669

SUBADULT RANK2 SUBADULT FEMALE RANK2 SUBADULT

-.783 -.715 -.669

ADULT FEMALE RANK2

-.276

.72 .717 .52

ADULT MALE RANKl

-.523

.605 .502

AVERAGE RANKl

-.505

WEALTHY RANKl MALE WEALTHY RANKl

-.332 -.307

AXIS 2 ( 10.65 %) CLAN2 AXIS 3 ( 9.05 %) ADULT FEMALE RANK2 ADULT FEMALE FEMALE RANK2 AXIS 4 ( 8.47 %) ADULT

AXIS 5 ( 7.61 %)

AXIS 6 ( 4.96 %) ADULT FEMALE RANKl FEMALE RANKl ADULT FEMALE AVERAGE RANKl AXIS 7 ( 4.03 %) WEALTHY RANKl FEMALE WEALTHY RANKl AXIS 8 ( 3.72 %) WEALTHY RANK2

.308

AXIS 9 ( 3.35 %) AVERAGE RANKl

.306

********************************************************************************************************** TABLE 8.95 Highest correlations on axes for unrotated PCA (correlation matrix) of Model 4C

********************************************************************************************************** Positive

Negative

AXIS 1 ( 17.7 %) RANKl MALERANKl ADULTRANKl

.828 .75 .739

RANK2 ADULTRANK2

-.828 -.638

.667 .51

FEMALE RANKl ADULT FEMALE RANKl

-.591 -.57

.688 .657 .634

ADULT FEMALE RANK2 FEMALE RANK2 ADULT FEMALE

-.693 -.632 -.623

AXIS 2 ( 9.9 %) LEADER ADULT MALE WEALTHY RANKl AXIS 3 ( 8.6 %) ADULT MALE RANK2 ADULT MALE MALERANK2

224

Appendix 1

*********************************************************************************************************** TABLE 8.95 (Continued)

*********************************************************************************************************** Positive

Negative

AXIS 4 ( 7.2 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl

.638 .615

ADULT MALE AVERAGE RANKl MALE AVERAGE RANKl

-.577 -.549

.706

SUBADUL T RANK2 SUBADUL T FEMALE RANK2 SUBADULT

-.823 -.712 -.706

.523

CLAN

-.523

ADULT FEMALE AVERAGE RANK2

-.344

AXIS 5 ( 6.5 %) ADULT

AXIS 6 ( 4.8 %) CLANl AXIS 7 ( 4.1 %) FEMALE AVERAGE RANKl ADULT FEMALE AVERAGE RANKl

.527 .521

AXIS 8 ( 3.6 %) WEALTHY RANK2

.569

AXIS 9 ( 3.4 %) SUBADUL T MALE RANK2

.312

AXIS 10 ( 3.2 %) SUBADUL T MALE RANK2

-.555

AXIS 11 ( 3 %)

*********************************************************************************************************** TABLE 8.96 Highest correlations on axes for Varimax-rotated PCA of Model 4C

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 17.7 %) MALERANKl ADULT MALE RANKl ADULT MALE AVERAGE RANKl

.899 .853 .668

RANK2

-.618

RANK2

-.622

SUBADUL T FEMALE RANK2 SUBADUL T RANK2 SUBADULT

-.725 -.692 -.661

AXIS 2 ( 9.9 %) LEADER ADULT MALE WEALTHY RANKl

.984 .545

AXIS 3 ( 8.6 %) ADULT FEMALE RANK! FEMALE RANKl ADULT FEMALE AVERAGE RANKl

.794 .772 .745

AXIS 4 ( 7.2 %) FEMALE WEALTHY RANK! WEALTHY RANKl ADULT FEMALE WEALTHY RANK!

.87 .806 .788

AXIS 5 ( 6.5 %) ADULT ADULTRANK2 ADULT MALE RANK2

.661 .627 .552

AXIS 6 ( 4.8 %) ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2 ADULT FEMALE

.759 .736 .651

AXIS 7 ( 4.1 %) MALE WEALTHY RANK2

.899

225

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ******************************************************************************************************** TABLE 8.96 (Continued)

******************************************************************************************************** WEALTHY RANK2

.646

AXIS 8 ( 3.6 %) FEMALE WEALTHY RANK2 WEALTHY RANK2

.898 .62

AXIS 9 ( 3.4 %) CLANl

.777

CLAN2

-.777

SUBADULT MALE RANK2

-.701

AXIS 10 ( 3.2 %)

AXIS 11 ( 3 %) MALE AVERAGE RANKl

MALE WEALTHY RANKl -.228 SUBADULT MALE WEALTHY RANKl -.211

.213

********************************************************************************************************** TABLE 8.97 Highest correlations on axes for Oblimin-rotated PCA of Model 4C

********************************************************************************************************** Positive

Negative

AXIS 1 ( 17.7 %) MALERANKl ADULT MALE RANKl RANKl

.915 .873 .668

RANK2

-.668

ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2 ADULT FEMALE

-.796 -.738 -.669

.643 .606

SUBADULT FEMALE RANK2 SUBADULT RANK2 SUBADULT

-.755 -.681 -.643

.774

CLAN2

-.774

.808 .793

RANK2

-.698

MALE WEALTHY RANKl

-.351

SUBADULT MALE RANK2 SUBADUL T POOR RANK2

-.662 -.524

AXIS 2 ( 9.9 %) LEADER ADULT MALE WEALTHY RANKl MALE WEALTHY RANKl

.986 .656 .558

AXIS 3 ( 8.6 %) MALE

.504

AXIS 4 ( 7.2 %) FEMALE WEALTHY RANKl WEALTHY RANKl ADULT FEMALE WEALTHY RANKl

.88 .859 .808

AXIS 5 ( 6.5 %) ADULT ADULTRANK2 AXIS 6 ( 4.8 %) CLANl AXIS 7 ( 4.1 %) ADULT FEMALE RANKl FEMALE RANKl AVERAGE RANKl

.721

AXIS 8 ( 3.6 %) MALE WEALTHY RANK2 WEALTHY RANK2 ADULT MALE RANK2

.916 .632 .511

AXIS 9 ( 3.4 %)

AXIS 10 ( 3.2 %) ADULTRANK2

.558

226

Appendix 1 *********************************************************************************************************** TABLE 8.97 (Continued)

*********************************************************************************************************** Positive AXIS 11 ( 3 %) - -- -- --- -- -- -- --- -- -- --- FEMALE WEALTHY RANK2 WEALTHY RANK2 ADULT FEMALE RANK2

Negative

.921 .601 .544

******************************************************************************************************* TABLE 8 .98 Summary of highest correlations in PCNCA analyses of Model 4C

*******************************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANKl 12 RANKl ADULT 13 RANKl ADULT MALE 14 RANKl ADULT FEMALE 15 RANKl SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEALTHY 19 RANKl MALE WEALTHY 20 RANKl ADULT MALE WEALTHY 21 RANKl SUBADULTMALEWEALTHY 22 RANKl FEMALE WEALTHY 23 RANKl ADULT FEMALE WLTHY 24 RANKl SUBADULTFEMALEWLTHY 25 RANKl AVERAGE 26 RANKl MALE A VERA GE 27 RANKl ADULT MALE AVERAGE 28 RANKl SUB AD MALE A VERA GE 29 RANKlFEMALEAVERAGE 30 RANKl ADULT FEMALE AVERAGE 31 RANKl SUBADFEMALEAVERAGE 32 RANKl MALE 33 RANKl FEMALE 34 RANK2 35 RANK2SUBADULT 36 RANK2ADULT 37 RANK2MALE 38 RANK2 FEMALE 39 RANK2MALEADULT 40 RANK2ADULTFEMALE 41 RANK2SUBADULTFEMALE 42 RANK2SUBADULTMALE 43 RANK2 SUBADULT POOR 44RANK2SUBADULTAVERAGE 45 RANK2 AGED 40+ 46 RANK2 AGED 40+ MALE 47 RANK2AGED 40+FEMALE 48 RANK2 WEALTHY 49 RANK2 WEALTHY FEMALE 50 RANK2 WEALTHY MALE 51 RANK2ADULT AVERAGE 52 RANK2ADULT AVERAGE MALE 53 RANK2ADULT AVERAGE FEMALE 54 RANK2ADULTPOOR 55 RANK2ADULTPOORMALE 56 RANK2ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.434 .434 -.503 -.64 .64 -.358 -.543 .543 -.581 .715 .946 .704 -.528 .682 .521 .345 .373 .523 -.539 -.503 .254 .448 .435 .321 .703 .42 .344 .23 .522 .505 .346 .6 .681 -.946 .801 -.79 -.589 .539 -.633 .785 .766 .725 .533 .606 -.497 -.489 .559 .469 .494 .538 -.427 -.474 .488 -.289 -.359 -.27

.221 -.221 .168 .334 -.334 .182 .567 -.567 .472 .632 .904 .669 .443 .458 .503 .333 .36 .494 .378 .283 .244 .399 .344 .197 .676 -.407 -.374 .223 .502 .36 .332 .572 .605 -.904 -.313 -.81 .654 -.732 .549 .754 -.478 .267 -.161 .343 -.503 .482 .524 -.386 .447 -.304 -.432 .412 .474 -.262 -.239 .252

-.773 .773 .247 .669 -.669 .367 -.621 .621 -.654 .653 .876 .711 .559 .72 .407 .328 .236 .605 .433 .349 -.281 .502 .477 .278 .634 .473 .424 .211 .509 .52 -.261 .662 .717 -.876 -.783 -.804 -.66 .63 -.701 .709 -.715 -.257 -.653 -.361 -.515 -.6 .564 -.367 .398 -.441 -.428 -.461 .462 -.278 -.247 .239

.523 -.523 .667 .706 -.706 .407 .621 -.621 .657 -.623 .828 .739 .704 -.57 .299 .281 -.188 .661 .648 .611 .265 .638 .615 -.235 .462 -.549 -.577 .136 .527 .521 .176 .75 -.591 -.828 -.823 -.638 .634 -.632 .688 -.693 -.712 -.555 -.607 -.52 -.418 .561 -.514 .569 -.548 .609 -.385 -.435 -.371 -.338 -.329 -.181

.777 -.777 .984 .661 -.661 .429 -.455 .455 .513 .651 .622 .616 .853 .794 .263 .325 .178 .806 .605 .545 .325 .87 .788 .357 .694 .628 .668 -.153 .733 .745 .22 .899 .772 -.622 -.692 .627 .481 .513 .552 .759 -.725 -.701 -.464 -.573 .471 .481 .736 .646 .898 .899 .432 .458 .63 .265 .251 .348

.774 -.774 .986 .643 -.643 .414 .504 -.504 .483 -.669 .698 .671 .873 .808 .273 .325 .194 .859 .629 .656 .312 .88 .808 .34 .721 .628 .658 -.175 .712 .718 .22 .915 .793 -.698 -.681 .606 .498 -.579 .511 -.796 -.755 -.662 -.524 -.613 .452 .451 -.738 .632 .921 .916 .387 .387 -.617 .228 .2 -.332

*********************************************************************************************************** TABLE 8.99 Highest correlations on axes for correspondence analysis of Model 4D

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 7.94 %) RANKl ADULTRANKl AVERA GE RANKl

.915 .714 .641

RANK2 ADULTRANK2

227

-.915 -.78

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************************************** TABLE 8.99 (Continued)

********************************************************************************************************** Positive

Negative

AXIS 2 ( 6.76 %) SUBADULT RANK2 SUBADULTAVERAGERANK2 SUBADULT FEMALE RANK2

.648 .601

ADULT

-.526

.596 .585 .547

ADULT FEMALE RANK2 ADULT FEMALE FEMALE RANK2

-.619 -.564 -.547

.327 .32

SUBADULT FEMALE RANK2 SUBADUL T POOR RANK2

-.373 -.335

WEALTHY RANKl

-.432

ADULT MALE AVERAGE RANK2 SUBADULT MALE WEALTHY RANKl

-.319 -.311

.276 .248 .223

CLANl FEMALE AVERAGE RANKl ADULT FEMALE AVERAGE RANKl

-.248 -.241 -.238

.252

ADULT MALE POOR RANK2 ADULT POOR RANK2

-.28 -.217

SUBADULT MALE WEALTHY RANKl

-.255

.555

AXIS 3 ( 6.23 %) SUBADULT MALE RANK2

.461

AXIS 4 ( 5.93 %) ADULT MALE RANK2 MALERANK2 ADULT MALE AXIS 5 ( 5.32 %) SUBADULTAVERAGERANK2 SUBADULT MALE RANK2 AXIS 6 ( 4.9 %) ADULT FEMALE RANKl FEMALE RANKl

.591 .582

AXIS 7 ( 4.62 %) AVERAGE RANKl

.407

AXIS 8 ( 4.56 %) WEALTHY RANK2

.512

AXIS 9 ( 4.23 %)

AXIS 10 ( 4.06 %) SUBAD MALE WEALTHY RANKl

.428

AXIS 11 ( 3.63 %) ADULT POOR RANK2 CLAN2 LEADER AXIS 12 ( 3.46 %) MALE WEALTHY RANK2 AXIS 13 ( 3.24 %) ADULT FEMALE AVERAGE RANKl FEMALE AVERAGE RANKl

.394 .357

AXIS 14 ( 3.02 %) ADULT MALE WEALTHY RANKl

.241

AXIS 15 ( 2.86 %) ADULT POOR RANK2

.209

*********************************************************************************************************** TABLE 8.100 Highest correlations on axes for detrended correspondence analysis of Model 4D

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 7.94 %) RANKl ADULTRANKl AVERAGE RANKl

.849 .641 .594

RANK2 ADULTRANK2 ADULT MALE RANK2

AXIS 2 ( 6.26 %)

228

-.849 -.779 -.511

Appendix 1 *********************************************************************************************************** TABLE 8.100 (Continued)

*********************************************************************************************************** Positive

Negative SUBADULT AVERAGE RANK2

-.46

AXIS 3 ( 5.46 %) - -- -- --- -- -- -- --- -- -- --- ADULT FEMALE RANK2 FEMALE RANK2 ADULT FEMALE

.654 .544 .532

MALERANK2 ADULT MALE RANK2 MALE

-.59 -.583 -.504

AXIS 4 ( 4.3 %) - -- -- --- -- -- -- --- -- -- --- ADULT FEMALE FEMALE

.563 .542

MALE

-.542

******************************************************************************************************************** TABLE 8.101

Highest correlations on axes for unrotated PCA (covariance matrix) of Model 4D

******************************************************************************************************************** Positive

Negative

AXIS 1 ( 14.67 %) RANK! ADULTRANKl MALERANKl

.71 .574 .532

ADULTRANK2 RANK2

-.757 -.71

.185 .136 .125

AGED 40+ MALE RANK2 MALERANK2 ADULT MALE AVERAGE RANK2

-.146 -.135 -.125

.62

CLAN2

-.62

.424 .402

SUBADUL T RANK2

-.402

.662 .653 .639

FEMALE RANK2 FEMALE ADULT FEMALE RANK2

-.663 -.639 -.582

ADULT FEMALE

-.303

FEMALE WEALTHY RANK2

-.444

ADULT FEMALE RANK!

-.527

FEMALE WEALTHY RANK2 ADULT FEMALE RANK2

-.351 -.312

FEMALE AVERAGE RANK!

-.503

AXIS 2 ( 11.08 %) MALE AVERAGE RANK! SUBAD MALE AVERAGE RANK! ADULT MALE AVERAGE RANK! AXIS 3 ( 9.3 %) CLAN! AXIS 4 ( 7.27 %) MALERANKl ADULT MALE RANK! AXIS 5 ( 5.91 %) ADULT MALE ADULT MALE RANK2 MALE AXIS 6 ( 5.42 %)

AXIS 7 ( 4.86 %) ADULT POOR RANK2 ADULT MALE POOR RANK2

.287 .269

AXIS 8 ( 3.58 %)

AXIS 9 ( 3.37 %)

AXIS 10 ( 3.08 %) SUBADUL T FEMALE RANK2 SUBADUL T RANK2

.368 .311

AXIS 11 ( 2.91 %)

******************************************************************************************************************* TABLE 8.102

Highest correlations on axes for unrotated PCA (correlation matrix) of Model 4D

******************************************************************************************************************* Positive

Negative

AXIS 1 ( 12 %) LEADER RANK! MALERANKl

.732 .689 .655

RANK2 ADULTRANK2

229

-.689 -.537

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************************************************** TABLE 8.102 (Continued)

******************************************************************************************************************** Positive

Negative

AXIS 2 ( 7.9 %) LEADER RANK2

.636 .502

RANKl

-.502

.666 .591 .585

ADULT MALE RANK2 MALERANK2 ADULT MALE

-.537 -.534 -.505

.547 .514

ADULT MALE AVERAGE RANKl MALE AVERAGE RANKl MALERANKl

-.52 -.518 -.509

SUBADULT RANK2 SUBADUL T POOR RANK2 SUBADULT FEMALE RANK2

-.639 -.521 -.511

.362 .303

CLAN2

-.362

.391 .313

ADULT MALE AVERAGE RANK2

-.34

.274 .232

ADULT FEMALE AVERAGE RANKl FEMALE AVERAGE RANKl

-.222 -.22

FEMALE AVERAGE RANKl

-.46

AXIS 3 ( 6 %) FEMALE WEALTHY RANK2 ADULT FEMALE ADULT FEMALE RANK2 AXIS 4 ( 5.6 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl AXIS 5 ( 5.4 %) MALE WEALTHY RANKl SUBADULT MALE WEALTHY RANKl

.592 .56

AXIS 6 ( 4.9 %) ADULT

.506

AXIS 7 ( 4.5 %) ADULTFEMALEAVERAGERANK2 AGED 40+ FEMALE RANK2

.409 .403

AXIS 8 ( 4 %) CLANl ADULT MALE WEALTHY RANKl AXIS 9 ( 3.5 %) MALE WEALTHY RANK2 WEALTHY RANK2 AXIS 10 ( 3.3 %) SUBADULTAVERAGERANK2 FEMALE RANK2 AXIS 11 ( 3.1 %)

AXIS 12 ( 3 %) MALE WEALTHY RANK2

.221

AXIS 13 ( 2.9 %) SUBADULT MALE RANK2

.341

******************************************************************************************************************** TABLE 8.103 Highest correlations on axes for Varimax-rotated PCA of Model 4D

************************************************************************************************************** Positive

Negative

AXIS 1 ( 12 %) LEADER ADULT MALE WEALTHY RANKl

.978 .526

AXIS 2 ( 7.9 %) MALERANKl RANKl MALE AVERAGE RANKl

RANK2

.659 .629 .629

AXIS 3 ( 6 %)

230

-.629

Appendix 1 ******************************************************************************************************************* TABLE 8.103 (Continued)

******************************************************************************************************************* Positive FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl ADULT FEMALE RANKl

Negative

.708 .651 .607

AXIS 4 ( 5.6 %) FEMALE WEALTHY RANK2

.751

AXIS 5 ( 5.4 %) SUBADULT MALE WEALTHY RANKl MALE WEALTHY RANKl WEALTHY RANKl

.731 .646 .522

AXIS 6 ( 4.9 %) AGED 40+ MALE RANK2 ADULT MALE RANK2 MALERANK2

.641 .595 .556

AXIS 7 ( 4.5 %) ADULT

.508

SUBADUL T RANK2 SUBADULT AVERAGE RANK2 SUBADUL T FEMALE RANK2

-.54 -.524 -.509

.539

CLANl

-.539

FEMALE AVERAGE RANKl AVERA GE RANKl ADULT FEMALE AVERAGE RANKl

-.647 -.616 -.572

SUBADUL T MALE RANK2

-.468

AXIS 8 ( 4 %) CLAN2 AXIS 9 ( 3.5 %) ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2

.602 .587

AXIS 10 ( 3.3 %) MALE WEALTHY RANK2

.69

AXIS 11 ( 3.1 %)

AXIS 12 ( 3 %) ADULT MALE WEALTHY RANKl

.519

AXIS 13 ( 2.9 %)

*********************************************************************************************************** TABLE 8.104 Highest correlations on axes for Oblirnin-rotated PCA of Model 4D

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 12 %) RANKl MALERANKl ADULTRANKl

RANK2

.637 .593 .568

AXIS 2 ( 7.9 %) LEADER ADULT MALE WEALTHY RANKl

.988 .574

AXIS 3 (6%) FEMALE WEALTHY RANK2 ADULT FEMALE RANK2 WEALTHY RANK2

.768 .516 .514

AXIS 4 ( 5.6 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl ADULT FEMALE RANKl

.689 .617 .599

231

-.637

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

*********************************************************************************************************** TABLE 8.104 (Continued)

*********************************************************************************************************** Positive

Negative

AXIS 5 ( 5.4 %) SUBADULT MALE WEALTHY RANKl MALE WEALTHY RANKl WEALTHY RANKl

.736 .675 .559

AXIS 6 ( 4.9 %) ADULT MALE RANK2 AGED 40+ MALE RANK2 MALERANK2

FEMALE

-.506

.565

AVERAGE RANKl FEMALE AVERAGE RANKl RANKl

-.714 -.638 -.565

.549

CLANl

-.549

.727 .713 .641

AXIS 7 ( 4.5 %) ADULT FEMALE RANK2 AGED 40+ FEMALE RANK2 ADULT FEMALE

.699 .644 .535

AXIS 8 ( 4 %) ADULT MALE WEALTHY RANKl ADULT MALE RANKl

.57 .535

AXIS 9 ( 3.5 %) MALE WEALTHY RANK2

.712

AXIS 10 ( 3.3 %) SUBADULTAVERAGERANK2

.407

AXIS 11 ( 3.1 %) RANK2

AXIS 12 ( 3 %) CLAN2 AXIS 13 ( 2.9 %) SUBADULT MALE RANK2

.438

******************************************************************************************************** TABLE 8 .105 Summary of highest correlations in PCNCA analyses of Model 4D

********************************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANKl 12 RANK! ADULT 13 RANK! ADULT MALE 14 RANKlADULTFEMALE 15 RANK! SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEAL THY 19 RANK! MALE WEALTHY 20 RANKl ADULT MALE WEAL THY 21 RANK! SUBADMALEWEALTHY 22 RANK! FEMALE WEALTHY 23 RANKl ADULT FEMALE WEAL THY 24 RANKl SUBADFEMALEWEALTHY 25 RANK! AVERAGE 26 RANKl MALE AVERAGE 27 RANK! ADULT MALE AVERAGE 28 RANKl SUBADMALEAVERAGE 29 RANKl FEMALE AVERAGE 30 RANK! ADULT FEMALE AVERAGE 31 RANK! SUBADFEMALEAVERAGW

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.345 .345 -.302 -.526 .526 -.277 .54 -.54 .547 -.564 .915 .714 .464 .591 .465 .376 .262 .552 -.476 -.32 .428 .366 .309 .191 .641 .414 .347 .216 .451 .489 .292

-.227 .227 -.231 -.315 .315 -.114 -.542 .542 -.497 .563 .849 .641 -.429 .436 .462 .376 .257 .515 .415 -.366 .334 .291 .255 .146 .594 .391 .33 .2 .412 .34 .217

.62 -.62 .353 -.346 .346 -.181 .639 -.639 .662 -.491 .71 .574 .43 -.527 .334 .286 -.275 .392 .329 .338 .322 -.39 -.422 .07 .527 .412 .353 .223 -.503 -.398 -.292

.362 -.362 .732 .506 -.506 .282 -.487 .487 -.505 .591 .689 .605 .608 -.459 -.269 .271 -.212 .566 .592 .566 .56 .547 .514 .226 -.429 -.518 -.52 .156 -.46 -.398 -.214

-.539 .539 .978 .508 -.508 .291 .463 -.463 .53 .469 .629 .547 .58 .607 .374 .473 -.269 .522 .646 .526 .731 .708 .651 .272 -.616 .629 .574 .265 -.647 -.572 -.283

-.549 .549 .988 .445 -.445 .356 .506 -.506 .621 .535 .637 .568 .535 .599 .392 .498 -.252 .559 .675 .574 .736 .689 .617 .297 -.714 .564 .501 .255 -.638 -.562 -.284

232

Appendix 1 ******************************************************************************************************** TABLE 8.105 (Continued)

********************************************************************************************************

32 RANKl MALE 33 RANKl FEMALE 34 RANK2 35 RANK2SUBADULT 36 RANK2ADULT 37 RANK2MALE 38 RANK2 FEMALE 39 RANK2MALEADULT 40 RANK2ADULTFEMALE 41 RANK2SUBADULTFEMALE 42 RANK2SUBADULTMALE 43 RANK2 SUBADULT POOR 44RANK2SUBADULTAVERAGE 45 RANK2 AGED 40+ 46 RANK2 AGED 40+ MALE 47 RANK2AGED 40+FEMALE 48 RANK2 WEALTHY 49 RANK2 WEALTHY FEMALE 50 RANK2 WEALTHY MALE 51 RANK2ADULT AVERAGE 52 RANK2ADULT AVERAGE MALE 53 RANK2 ADULT A VER FEMALE 54 RANK2ADULTPOOR 55 RANK2ADULTPOORMALE 56 RANK2ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

.617 .582 -.915 .648 -.78 .585 -.547 .596 -.619 .555 .461 .383 .601 -.475 .423 - .396 .512 -.499 .447 -.384 -.319 -.296 -.293 -.28 .202

.586 .522 -.849 -.266 -.779 -.59 .544 -.583 .654 -.396 -.189 -.061 -.46 -.452 -.493 .413 -.411 .432 -.281 -.34 -.365 .378 -.284 -.263 .268

.532 -.467 -.71 -.402 -.757 .598 -.663 .653 -.582 -.393 .29 -.349 .179 -.478 .567 -.426 -.308 -.444 .291 -.408 .427 -.416 .287 .275 .291

.655 -.477 -.689 -.639 -.537 -.534 .435 -.537 .585 -.511 .341 -.521 -.326 -.344 -.444 .403 .337 .666 .391 .324 -.34 .409 .224 -.166 .316

.659 .585 -.629 -.54 .442 .556 -.409 .595 .602 -.509 -.468 -.307 -.524 .379 .641 .587 .488 .751 .69 .27 .483 .477 .199 .267 .371

.593 .586 -.637 -.42 .556 .641 -.404 .727 .699 -.41 .438 -.344 .407 .483 .713 .644 .514 .768 .71 .369 .547 .527 .24 .296 .398

************************************************************************* TABLE 8.106 Top Jaccard values for Ward's method clustering of Model SA

************************************************************************* GROUP 1 ADULT 2 SUBADULT 3 SUBADULT AVERAGE 4 ADULTFEMALE 5 SUBADULT MALE POOR 6 SUBADULT FEMALE 7 ADULTMALECLANl POOR 8 SUBADULT MALE CLANl POOR 9 MALE WEALTHY 10 FEMALE WEALTHY 11 SUBADULT MALE CLANl 12 ADULT FEMALE CLANl AVERAGE 13 SUBADULT MALE CLAN2 POOR 14 MALE CLAN2 POOR 15 SUBADULTFEMALECLANl 16 ADULTFEMALEPOOR 17 ADULTFEMALECLAN2POOR 18 SUBADULTFEMALECLAN2POOR 19 ADULT FEMALE CLANl POOR 20 SUBADULTFEMALECLANlPOOR

LEVEL

JACCARD .96 .932 .786 .75 .714 .644 .643 .619 .567 .514 .5 .478 .467 .455 .44 .412 .389 .308 .278 .167

2 2

11 4

3 3

13 5 4 6

10 13 12 5 7 9

14 11 14 15

************************************************************************* TABLE 8.107 Top Jaccard values for Average linkage clustering of Model SA

************************************************************************* GROUP 1 ADULT 2 SUBADULT 3 ADULTFEMALE 4 ADULTMALE 5 SUBADULT MALE POOR 6 SUBADULT MALE 7 SUBADULT FEMALE 8 SUBADULTFEMALECLAN2 9 SUBADULT FEMALE CLANl POOR 10 ADULTMALECLAN2AVERAGE 11 SUBADULTCLANlAVERAGE 12 SUBADULTFEMALECLAN2POOR 13 FEMALE CLANl POOR 14 ADULT MALE CLANl POOR 15 ADULT FEMALE CLAN2 POOR 16 AGED 40+ FEMALE CLAN2 AVERAGE 17 ADULT MALE CLANl AVERAGE

LEVEL

JACCARD .936 .859 .851 .825 .765 .744 .629 .278 .263 .267 .25 .143 .136 .111 .1 .1 .1

6

3

11 11 8 4 4

12 6

15 7 8 9

15 5

.10 14

233

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************ TABLE 8 .108 Top Jaccard values for monothetic divisive clustering of Model SA

************************************************************************ GROUP

LEVEL

1 MALE WEALTHY 2 ADULT MALE AVERAGE 3 FEMALE WEAL THY 4 ADULT FEMALE AVERAGE 5 POOR 6 AGED 40+ MALE AVERA GE 7 ADULT FEMALE 8 SUBADUL T MALE 9 SUBADULT 10 ADULT MALE 11 ADULT FEMALE CLANl AVERAGE 12 SUBADULT MALE CLANl 13 MALE 14 SUBADULT FEMALE 15 ADULT FEMALE CLAN2 AVERAGE 16 MALE CLANl WEALTHY 17 ADULT POOR 18 SUBADULTMALECLAN2POOR 19 AGED 40+ CLANl POOR 20 SUBADULT FEMALE CLAN2 21 SUBADULTFEMALECLANl

6 6 7 7

JACCARD

1 1

1 1 .86 .833 .761 .757 .734 .722 .667 .652 .647 .634 .632 .538 .515 .438 .333 .316 .192

3 15 2

5 4 3

10 12 2 8

10 13 9

12 8

11 9

***************************************************************************************** TABLE 8.109 Summary of top Jaccard values for clustering of Model SA

***************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

.449@2 .316@ 2 .1 @4 .96@ 2 ** .932@ 2 ** .634@ 3 .644@3 .786@ 11 * .74@ 2 * .556@4 .75 @4 * .346@ 6 .514@ 6 .567 @4 .452@6 .526@ 13 .412@ 9 .446@2 .333@ 4 .367 @4 .333 @4 .556@4 .653@ 2 .356@4 .544@4

1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT S)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 ) ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.568@ 2 .43@ 2 .073@ 13 .936@ 6 ** .859@ 3 * .763@ 8 * .629@4 .258@4 .662@3 .825@ 11 * .851@ 11 * .328@ 10 .318@ 11 .381@ 13 .223@ 10 .217@13 .169@ 11 .433@ 6 .358@ 11 .435@ 11 .505@ 11 .594@ 11 .669@6 .636@ 13 .691@ 11

DIVISIVE .434@2 .403 @2 .429@ 13 .432@2 .734@4 * .757@ 5 * .634@ 8 .195@ 8 .597 @4 .722@ 3 * .761 @2 * .553@ 7 1@ 7 ** 1@ 6** .515@ 9 .269@ 9 .4@9 .6@7 1@ 6 ** 1 @7 ** .647@2 .524@ 2 .411@ 2 .429@3 .609@2

***************************************************************************************** TABLE 8 .110 Highest correlations on axes for correspondence analysis of Model SA

***************************************************************************************** Positive

Negative

AXIS 1 ( 17.72 %) SUBADULT SUBADULTPOOR SUBADULT FEMALE

.855 .685 .545

ADULT AGED 40+

-.855 -.531

.597

SUBADULT MALE

-.631

.661 .604 .513

MALE WEALTHY ADULT MALE MALE

-.618 -.615 -.604

.652

SUBADULTPOOR

-.517

AXIS 2 ( 15.29 %) SUBADULT FEMALE AXIS 3 ( 11.24 %) ADULT FEMALE FEMALE AGED 40+ FEMALE AXIS 4 ( 9.13 %) SUBADULT AVERAGE

234

Appendix 1 ***************************************************************************************** TABLE 8.110 (Continued)

***************************************************************************************** Positive

Negative

AXIS 5 ( 8.55 %) CLAN2

.519

CLANl

-.519

AXIS 6 ( 5.86 %) WEALTHY

.443

AXIS 7 ( 4.96 %) SHAMAN

.317

************************************************************************************************* TABLE 8.111 Highest correlations on axes for detrended correspondence analysis of Model SA

************************************************************************************************* Positive

Negative

AXIS 1 ( 17.72 %) SUBADULT SUBADULT POOR SUBADULT FEMALE

.884 .746 .625

ADULT AGED 40+

-.884 -.533

.551 .52

FEMALE SUBADULT FEMALE

-.52 -.507

.509

CLANl

-.509

ADULT MALE ADULT MALE AVERAGE

-.5 -.419

AXIS 2 ( 10.51 %) SUBADUL T MALE MALE AXIS 3 ( 7.72 %) CLAN2 AXIS 4 ( 4.83 %)

************************************************************************************************* TABLE 8 .112 Highest correlations on axes for unrotated PCA (covariance matrix) of Model SA

************************************************************************************************* Positive

Negative

AXIS 1 ( 22.35 %) ADULT AGED 40+ ADULT FEMALE

.847 .702 .594

SUBADULT SUBADULT POOR SUBADUL T MALE

-.847 -.72 -.586

.624 .597 .506

ADULT MALE MALE WEALTHY MALE

-.77 -.684 -.624

.602

CLAN2

-.602

WEALTHY

-.453

SHAMAN

-.22

SUBADULT FEMALE

-.514

AXIS 2 ( 14.1 %) FEMALE ADULT FEMALE AGED 40+ FEMALE AXIS 3 (9%) CLANl AXIS 4 ( 8.16 %)

AXIS 5 ( 6.46 %) AGED 40+

.326

AXIS 6 ( 5.08 %)

AXIS 7 ( 5.01 %)

235

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************************************** TABLE 8.113 Highest correlations on axes for unrotated PCA (correlation matrix) of Model 5A

************************************************************************************************** Positive

Negative

AXIS I ( 19.3 %) ADULT AGED 40+ WEALTHY

.785 .617 .585

SUBADULT SUBADULTPOOR SUBADULT MALE

-.785 -.638 -.532

.75 .668 .599

MALE MALE WEALTHY

-.599 -.568

.395 .341

ADULT POOR SHAMAN

-.368 -.313

.632

CLAN!

-.632

.602 .51

SUBADULT MALE MALE

-.569 -.51

AXIS 2 ( 16.8 %) ADULT FEMALE AGED 40+ FEMALE FEMALE AXIS 3 ( 8.4 %) SHAMAN

.601

AXIS 4 ( 6.6 %) WEALTHY SUBADULT AVERAGE AXIS 5 ( 6.6 %) CLAN2 AXIS 6 ( 5.9 %) SUBADULT FEMALE FEMALE

*************************************************************************************************** TABLE 8.114 Highest correlations on axes for Varimax-rotated PCA of Model 5A

*************************************************************************************************** Positive

Negative

AXIS 1 ( 19.3 %) MALE WEALTHY ADULT MALE AGED 40+ MALE

.767 .719 .561

AXIS 2 ( 16.8 %) FEMALE WEAL THY ADULT FEMALE AGED 40+ FEMALE

.816 .638 .584

AXIS 3 ( 8.4 %) ADULT AGED 40+

.662 .544

SUBADULT SUBADULT MALE

-.662 -.581

CLAN2

-.702

SUBADULT FEMALE

-.702

AXIS 4 ( 6.6 %) SHAMAN

.944

AXIS 5 ( 6.6 %) CLAN!

.702

AXIS 6 ( 5.9 %)

236

Appendix 1

************************************************************************************************** TABLE 8.115 Highest correlations on axes for Oblimin-rotated PCA of Model 5A

************************************************************************************************** Positive

Negative

AXIS 1 ( 19.3 %) MALE WEALTHY ADULT MALE AGED 40+ MALE

.773 .735 .58

AXIS 2 ( 16.8 %) FEMALE WEALTHY ADULT FEMALE AGED 40+ FEMALE

.81 .691 .635

AXIS 3 ( 8.4 %) SHAMAN

.966

AXIS 4 ( 6.6 %) SUBADULT SUBADUL T MALE SUBADULT POOR

.738 .604 .556

ADULT AGED 40+ ADULT FEMALE

-.738 -.612 -.53

.703

CLANl

-.703

AXIS 5 ( 6.6 %) CLAN2 AXIS 6 ( 5.9 %) SUBADULT FEMALE

.734

*********************************************************************************************** TABLE 8 .116 Summary of highest correlations in PCNCA analyses of Model 5A

***********************************************************************************************

1 2 3 4 5 6 7 8 9

CLANl CLAN2 SHAMAN ADULT SUBADULT SUBADULT MALE SUBADULTFEMALE SUBADULTAVERAGE SUBADULT POOR 10 ADULTMALE 11 ADULTFEMALE 12 WEALTHY 13 WEALTHY FEMALE 14 WEALTHY MALE 15 ADULTPOOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULT AVERAGE MALE 20 ADULTAVERAGEFEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.519 .519 -.348 -.855 .855 -.631 .597 .652 .685 -.615 .661 .443 .448 -.618 -.37 -.23 -.267 -.416 -.334 .344 -.604 .604 -.531 -.378 .513

-.509 .509 -.163 -.884 .884 .551 .625 -.374 .746 -.5 -.42 -.476 .35 -.376 -.129 .089 .122 -.41 -.419 -.248 .52 -.52 -.533 -.326 -.328

.602 -.602 -.348 .847 -.847 -.586 -.514 -.314 -.72 -.77 .597 -.453 .448 -.684 .334 .24 -.285 .45 -.409 .381 -.624 .624 .702 -.592 .565

-.632 .632 .601 .785 -.785 -.569 .602 -.342 -.638 .512 .75 .585 .592 -.568 -.368 -.243 -.253 .331 -.444 .393 -.599 .599 .617 .454 .668

.702 -.702 .944 .662 -.662 -.581 -.702 -.45 -.488 .719 .638 .577 .816 .767 .367 -.184 .324 .447 .386 .363 .473 -.473 .544 .561 .584

-.703 .703 .966 -.738 .738 .604 .734 .499 .556 .735 .691 .565 .81 .773 -.319 -.193 -.285 -.461 .388 -.379 -.483 .483 -.612 .58 .635

********************************************************************************** TABLE 8 .117 Top Jaccard values for Ward's method clustering of Model 5B

********************************************************************************** GROUP

LEVEL

I WEALTHY RANK! 2 FEMALE WEAL THY RANKl 3 MALE WEAL THY RANKl 4 RANK2 5 RANKl 6 AVERAGE RANK! 7 ADULT RANK2 8 SUBADULT RANK2 9 ADULT FEMALE AVERAGE RANKl 10 ADULT FEMALE RANK2

6 8 8 2

2 6

3 3 11 4

237

JACCARD 1 1 1 .993 .981 .971 .95 .885 .846 .772

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ********************************************************************************** TABLE 8.117 (Continued)

********************************************************************************** GROUP 11 12 13 14 15 16 17 18 19 20 21 22 23 24

ADULT MALE AVERAGE RANK2 MALE CLAN2 WEALTHY RANK2 ADULTMALERANK2 FEMALE WEAL THY RANK2 SUBADULT FEMALE RANK2 SUBADULTCLAN1RANK2 ADULT POOR SUBADULTFEMALECLAN2POOR SUBADULT MALE CLAN2 RANK2 SUBADULTMALERANK2 SUBADULTFEMALECLAN2RANK2 AGED 40+ FEMALE CLANl AVERAGE RANK2 SUBADULT FEMALE CLANl POOR MALEPOOR

LEVEL 15 15

JACCARD

5 5 7 9

.742 .727 .709 .636 .595 .543 .533 .438

13

.4

10

.389 .316 .308 .267 .259

4

12

14 12 14 13

********************************************************************************* TABLE 8.118 Top Jaccard values for Average linkage clustering of Model 5B

********************************************************************************* GROUP 1 2 3 4 5 6 7 8 9

WEALTHY RANKl FEMALE WEALTHY RANKl MALE WEALTHY RANKl RANK2 RANKl AVERAGE RANKl ADULTRANK2 SUBADUL T FEMALE RANK2 ADULT FEMALE RANK2 10 ADULT MALE RANK2 11 SUBADULTFEMALECLAN2RANK2 12 SUBADULTFEMALECLAN1RANK2 13 SUBADULTMALERANK2 14 SUBADULTFEMALECLAN2POOR 15 SUBADULT MALE CLANl RANKl 16 SUBADULTMALECLANl POOR 17 MALE CLAN2 AVERAGE RANK2 18 AGED 40+ MALE CLAN2 A VERA GE RANK2 19 AGED 40+ FEMALE CLAN2 AVERAGE RANK2 20 ADULT FEMALE CLANl AVERAGE RANK2

LEVEL

JACCARD

II

I

14 14

1 1 .993 .981

2 2

11 5 4 10 10 8 8

5 13 12 15 15

.971

.93 .865 .754 .655 .625 .579 .5 .467 .333 .286 .2

.143 .125 .1

3 9 9

********************************************************************************** TABLE 8.119 Top Jaccard values for monothetic divisive clustering of Model 5B

**********************************************************************************

GROUP

LEVEL

1 2 3 4 5 6 7 8 9

10 9 7 9 10 3

MALE WEALTHY RANK2 FEMALE WEALTHY RANKl WEALTHY RANKl MALE WEALTHY RANK! ADULT MALE AVERAGE RANK2 RANK! FEMALE CLAN! WEALTHY RANK2 AVERAGE RANKl RANK2 10 ADULT FEMALE AVERAGE RANK2 11 ADULT MALE RANK2 12 ADULT FEMALE RANK2 13 POOR 14 FEMALE RANK2 15 SUBADULT RANK2 16 ADULT POOR 17 SUBADULT FEMALE RANK2 18 SUBADULT 19 ADULTFEMALECLAN2RANK1 20 FEMALE POOR 21 SUBADULT MALE RANK2 22 SUBADULT MALE CLAN2 AVERAGE 23 AGED 40+ FEMALE AVERAGE RANK2 24 FEMALE CLAN2 WEAL THY RANK2

JACCARD .933 .889 .842 .8

14 12

.792 .727 .727 .706

2

.644

14

.636 .611

4 5 5 4 8

.522

13 11 3 8

13 15 15 6

12

238

.508 .5 .475 .469 .412 .347 .333 .333 .286 .25 .238 .167

Appendix 1

*************************************************************************************************** TABLE 8.120 Summary of top Jaccard values for clustering of Model 5B

*************************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLAN! 2)CLAN2 3)LEADER 4)ADULT 5 )SUBADULT 6 )AGED 40+ 7)MALE 8)FEMALE 9 )ADULT MALE 10 )ADULT FEMALE 11 )RANKl 12 )RANKl ADULT 13 )RANKl ADULT MALE 14)RANK1ADULTFEMALE 15 )RANKl SUBADULT 16 )RANKl SUBADULTMALE 17)RANK1SUBADULTFEMALE 18 )RANKl WEALTHY 19 ) RANKl MALE WEAL THY 20 ) RANKl ADULT MALE WEAL THY 21 )RANKl SUBADULTMALEWEALTHY 22 ) RANKl FEMALE WEALTHY 23 ) RANKl ADULT FEMALE WEAL THY 24 ) RANKl SUBADULT FEMALE WEALTHY 25 )RANKl AVERAGE 26 ) RANKl MALE A VERA GE 27) RANKl ADULT MALE AVERAGE 28 )RANK! SUBADULTMALEAVERAGE 29 ) RANKl FEMALE A VERA GE 30 ) RANKl ADULT FEMALE A VERA GE 31 )RANK! SUBADULTFEMALEAVERAGE 32) RANKl MALE 33 ) RANKl FEMALE 34 )RANK2 35)RANK2SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 ) RANK2 MALE ADULT 40)RANK2ADULTFEMALE 4l)RANK2SUBADULTFEMALE 42 ) RANK2 SUBADUL T MALE 43)RANK2SUBADULTPOOR 44 )RANK2SUBADULT AVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 )RANK2AGED 40+FEMALE 48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEALTHY MALE 5l)RANK2ADULTAVERAGE 52 ) RANK2 ADULT A VERA GE MALE 53)RANK2ADULTAVERAGEFEMALE 54)RANK2ADULTPOOR 55 ) RANK2 ADULT POOR MALE 56)RANK2ADULTPOORFEMALE

.45@2 .401@ 2 .2@8 .709@ 3 * .671@ 3 .475@ 3 .429@ 4 .463@ 2 .557@ 4 .579@ 4 .981@ 2 ** .654@2 .333@ 11 .55@ 11 .379@ 11 .267@ 8 .292@ 11 1 @6 ** 1@ 8 ** .6@8 .4@8 1@ 8 ** .778@ 8 * .222@ 8 .971@ 6 ** .591@ 11 .429@ 11 .182@ 11 .559@ 6 .846@ 11 * .318@ 11 .462@ 2 .528@ 2 .993@ 2 ** .885@ 3 * .95@ 3 ** .582@ 4 .544@2 .709@4 * .772 @4 * .654@ 3 .393@ 5 .692@ 3 .263@ 10 .577@ 3 .532@ 4 .421@ 4 .378@ 12 .636@ 12 .667@ 15 .469@ 3 .742@ 15 * .4@7 .533@ 7 .333@ 7 .292@ 7

.452@ 3 .401@ 2 .2@ 14 .694@ 5 .538@ 4 .47@ 5 .457@ 4 .466@ 3 .514@ 10 .566@ 10 .981@ 2 ** .654@2 .316@ 14 .365@ 2 .34@2 .267@ 14 .2@ 12 1@ 11 ** 1@ 14 ** .6@ 14 .4@ 14 1@ 14 ** .778@ 14 * .222@ 14 .971@ 11 ** .424@ 11 .281@ 12 .2@ 12 .576@ 12 .375@ 12 .212@ 12 .462@ 2 .528@ 2 .993@ 2 ** .723 @4 * .93@ 5 ** .553@ 4 .548@ 3 .655@ 10 .754@ 10 * .865 @4 * .5@5 .578@ 4 .25@ 13 .573@ 5 .468@ 10 .429@ 10 .319@ 6 .259@ 10 .405@ 10 .448@ 5 .488@ 10 .357@ 10 .317@ 10 .174@ 7 .164@ 10

.371 @ 2 .401@ 2 .25@9 .5@2 .413@ 8 .331@ 2 .367@ 4 .408@2 .478@4 .369@ 5 .727@ 3 * .52@ 3 .295@ 3 .286@ 9 .298@ 3 .214@ 9 .214@ 12 .842@ 7 * .8 @9 * .556@ 9 .333 @9 .889@ 9 * .667@9 .25@9 .706 @12* .407@ 12 .32@ 12 .143 @ 15 .419@ 12 .312@ 8 .231 @ 12 .404@3 .42@3 .644@2 .475@ 8 .644@2 .5@4 .5@4 .611 @4 .522@ 5 .412@ 11 .286@ 15 .4@8 .143@ 11 .394@ 2 .477 @4 .316@ 5 .483@ 10 .714 @14* .933@10** .413@ 10 .792 @IO* .636@ 14 .469@ 13 .37@ 13 .237@ 5

********************************************************************************************************** TABLE 8.121 Highest correlations on axes for correspondence analysis of Model 5B

********************************************************************************************************** Positive

Negative

AXIS 1 ( 13.82 %) RANK! ADULTRANKl AVERAGE RANK!

.953 .717 .68

RANK2

-.953

.746 .639 .552

ADULT ADULTRANK2

-.639 -.556

.541

SUBADUL T MALE RANK2

-.56

.647

MALE

-.613

AXIS 2 ( 10.54 %) SUBADUL T RANK2 SUBADULT SUBADULT AVERAGE RANK2 AXIS 3 ( 9.17 %) SUBADUL T FEMALE RANK2 AXIS 4 ( 7.9 %) FEMALE RANK!

239

Theoretical and Quantitative Approaches to the Study of Mortuary Practice *********************************************************************************************************** TABLE 8.121 (Continued)

*********************************************************************************************************** Positive ADULT FEMALE RANKl FEMALE

Negative

.639 .613

MALE WEALTHY RANKl ADULT MALE

-.586 -.549

ADULT FEMALE RANK2 FEMALE RANK2

-.603 -.554

SUBADUL T POOR RANK2

-.529

FEMALE AVERAGE RANKl LEADER

-.532 -.519

WEALTHY RANK2

-.374

AXIS 5 ( 6.78 %)

AXIS 6 ( 6.21 %) AVERAGE RANKl

.522

AXIS 7 ( 5.67 %) SUBADULTAVERAGERANK2

.581

AXIS 8 ( 4.48 %) SUBADUL T POOR RANK2

.402

AXIS 9 ( 3.88 %)

AXIS 10 ( 3.28 %) AGED 40+ RANK2 AGED 40+ AGED 40+ FEMALE RANK2

.385 .365 .334

*************************************************************************************************** TABLE 8.122 Highest correlations on axes for detrended correspondence analysis of Model SB

*************************************************************************************************** Positive

Negative

AXIS 1 ( 13.82 %) RANK! ADULTRANKl AVERAGE RANKl

.917 .704 .63

RANK2

-.917

SUBADULT FEMALE RANK2

-.74

FEMALE RANK! ADULT FEMALE RANKl

-.568 -.537

ADULT FEMALE RANK2

-.533

AXIS 2 ( 8.85 %)

AXIS 3 ( 6.87 %)

AXIS 4 ( 4.57 %)

*********************************************************************************************************** TABLE 8.123 Highest correlations on axes for unrotated PCA (covariance matrix) of Model SB

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 20.35 %) RANK! ADULTRANKl AVERAGE RANKl

.888 .695 .618

RANK2 ADULTRANK2 AGED 40+ RANK2

-.888 -.791 -.534

.752 .674 .654

ADULT WEALTHY RANK2

-.654 -.504

.67 .641 .639

ADULT MALE ADULT MALE RANK2 MALE

-.706 -.671 -.67

AXIS 2 ( 10.81 %) SUBADULT RANK2 SUBADULT FEMALE RANK2 SUBADULT AXIS 3 ( 9.36 %) FEMALE ADULT FEMALE RANK2 FEMALE RANK2

240

Appendix 1 *********************************************************************************************************** TABLE 8.123 (Continued)

*********************************************************************************************************** Positive

Negative

AXIS 4 ( 6.7 %) MALE WEALTHY RANKl ADULT MALE WLTHY RANKl

-.56 -.501

CLANl

-.521

FEMALE RANKl ADULT FEMALE RANKl

-.45 -.423

SUBADUL T MALE RANK2 MALE MALERANK2

-.277 -.252 -.248

AXIS 5 ( 6.22 %) CLAN2

.521

AXIS 6 ( 4.97 %)

AXIS 7 ( 4.55 %) FEMALE WEALTHY RANKl ADLT FEMALE WLTHY RANKl

.547 .525

AXIS 8 ( 4.06 %) WEALTHY RANKl

.414

AXIS 9 ( 3.51 %) ADULT MALE POOR RANK2

.253

AXIS 10 ( 2.91 %) FEMALE SUBADUL T FEMALE RANK2 FEMALE RANK2

.252 .25 .201

*********************************************************************************************************** TABLE 8.124

Highest correlations on axes for unrotated PCA (correlation matrix) of Model 5B

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 17.7 %) RANKl WEALTHY RANKl ADULTRANKl

.829 .758 .726

RANK2 ADULTRANK2

-.829 -.628

.602 .535

FEMALE RANK! ADULT FEMALE RANKl

-.63 -.614

.723 .675 .662

SUBADULT SUBADUL T RANK2

-.53 -.523

.645 .616 .571

FEMALE RANK2

-.589

.502

MALE AVERAGE RANKl AVERAGE RANK! ADULT MALE AVERAGE RANK!

-.56 -.547 -.525

CLAN!

-.43

AXIS 2 ( 11.1 %) LEADER ADULT MALE WEALTHY RANKl AXIS 3 ( 9.1 %) ADULT FEMALE FEMALE WEALTHY RANK2 ADULT FEMALE RANK2 AXIS 4 ( 8.1 %) MALE WEALTHY RANK2 ADULT MALE RANK2 ADULT MALE AXIS 5 ( 7.2 %) ADLT FEMALE WEALTHY RANKl

AXIS 6 ( 4.9 %) FEMALE AVERAGE RANKl

.536

AXIS 7 ( 3.9 %) CLAN2

.43

AXIS 8 ( 3.6 %) WEALTHY RANK2

.411

AXIS 9 ( 3.2 %)

241

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

*********************************************************************************************************** TABLE 8.124 (Continued)

*********************************************************************************************************** Positive

Negative ADULT MALE POOR RANK2 SUBADULT MALE RANK2

-.406 -.404

************************************************************************************************* TABLE 8.125 Highest correlations on axes for Varimax-rotated PCA of Model SB

************************************************************************************************* Positive

Negative

AXIS 1 ( 17.7 %) MALERANKl MALE WEALTHY RANKl ADULT MALE RANKl

.797 .77

.676

AXIS 2 ( 11.1 %) LEADER ADULT MALE WEALTHY RANKl

.981 .552

AXIS 3 ( 9.1 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl WEALTHY RANKl

.923 .904 .734

AXIS 4 ( 8.1 %) FEMALE WEALTHY RANK2 ADULT FEMALE RANK2 ADULT FEMALE

.806 .782 .601

AXIS 5 ( 7.2 %) MALE WEALTHY RANK2 ADULT MALE RANK2 ADULT MALE

.772

.737 .62

AXIS 6 ( 4.9 %) AVERAGE RANKl FEMALE AVERAGE RANKl RANKl

.855 .69 .688

RANK2

-.688

CLANl

-.677

AXIS 7 ( 3.9 %) AGED 40+ RANK2 AGED 40+

.632 .549

AXIS 8 ( 3.6 %) CLAN2

.677

AXIS 9 ( 3.2 %) SUBADULT FEMALE RANK2 -.501

********************************************************************************************************** TABLE 8.126 Highest correlations on axes for Oblimin-rotated PCA of Model SB

********************************************************************************************************** Positive

Negative

AXIS 1 ( 17.7 %) MALERANKl MALE WEALTHY RANKl ADULT MALE RANKl

RANK2

.844

.781 .729

AXIS 2 ( 11.1 %) LEADER

.987

242

-.523

Appendix 1 *********************************************************************************************************** TABLE 8.126 (Continued)

*********************************************************************************************************** Positive ADULT MALE WEALTHY RANKl MALE WEALTHY RANKl

Negative

.665 .535

AXIS 3 ( 9.1 %) FEMALE WEALTHY RANK2 ADULT FEMALE RANK2 ADULT FEMALE

.798 .786 .606

AXIS 4 ( 8.1 %) MALE WEALTHY RANK2 ADULT MALE RANK2 ADULT MALE

.762 .761 .631

AXIS 5 ( 7.2 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl WEALTHY RANKl

.916 .893 .79

AXIS 6 ( 4.9 %) AVERAGE RANKl RANKl FEMALE RANKl

.845 .76 .684

RANK2

-.76

.669

CLANl

-.669

AGED 40+ RANK2 AGED 40+ ADULT AVERAGE RANK2

-.707 -.565 -.547

AXIS 7 ( 3.9 %) CLAN2 AXIS 8 ( 3.6 %)

AXIS 9 ( 3.2 %) - -- -- --- -- -- -- --- -- -- --- SUBADUL T FEMALE RANK2

.552

**************************************************************************************************************** TABLE 8.127 Summary of highest correlations in PCNCA analyses of Model SB

****************************************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANKl 12 RANKl ADULT 13 RANKl ADULT MALE 14 RANKl ADULT FEMALE 15 RANKl SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEALTHY 19 RANKl MALE WEALTHY 20 RANKl ADULT MALE WEALTHY 21 RANKl SUBADMALEWEALTHY 22 RANKl FEMALE WEALTHY 23 RANKl ADULT FEMALE WEALTHY 24 RANKl SUBADFEMALEWEALTHY 25 RANKl AVERAGE 26 RANKlMALEAVERAGE 27 RANKl ADULT MALE AVERAGE 28 RANKl SUB AD MALE A VERA GE 29 RANKl FEMALE AVERAGE 30 RANKlADULTFEMALEAVERAGE 31 RANKl SUBADULTFEMALEAVERAGE 32 RANKl MALE 33 RANKl FEMALE

243

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.326 .326 -.519 -.639 .639 -.386 -.613 .613 -.549 .557 .953 .717 -.489 .639 .515 .356 .354 .562 -.586 -.522 .328 -.498 -.473 .18 .68 .418 .327 .255 -.532 -.494 .348 .591 .648

.336 -.336 .228 .432 -.432 .259 .44 -.44 .312 -.45 .917 .704 .434 -.537 .477 .329 .329 .572 .423 .377 .242 .395 .349 .178 .63 .379 .301 .22 .466 -.432 .276 .562 .63

-.521 .521 -.276 -.654 .654 -.386 -.67 .67 -.706 .601 .888 .695 .475 .464 .445 .326 .289 .545 -.56 -.501 -.261 .547 .525 .17 .618 -.435 -.39 .208 .419 .427 .239 .593 .565

-.43 .43 .602 .53 -.53 .325 .459 -.459 .571 .723 .829 .726 .598 -.614 .317 -.296 -.191 .758 .645 .586 -.341 -.494 .502 .146 -.547 -.56 -.525 -.219 .536 .498 .216 .663 -.63

-.677 .677 .981 .407 -.407 .549 .448 -.448 .62 .601 .688 .565 .676 .612 .311 .39 .284 .734 .77 .578 .494 .923 .904 .254 .855 .447 .384 .242 .69 .624 .3 .797 .614

-.669 .669 .987 -.432 .432 -.565 .455 -.455 .631 .606 .76 .632 .729 .649 .333 .396 .292 .79 .781 .665 .47 .916 .893 .259 .845 .442 .424 .234 .682 .623 .289 .844 .684

Theoretical and Quantitative Approaches to the Study of Mortuary Practice **************************************************************************************************************** TABLE 8.127 (Continued)

****************************************************************************************************************

34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

RANK2 RANK2 SUBADULT RANK2ADULT RANK2 MALE RANK2 FEMALE RANK2MALEADULT RANK2 ADULT FEMALE RANK2SUBADULTFEMALE RANK2SUBADULTMALE RANK2SUBADULTPOOR RANK2SUBADULT AVERAGE RANK2 AGED 40+ RANK2 AGED 40+ MALE RANK2 AGED 40+ FEMALE RANK2 WEAL THY RANK2 WEALTHY FEMALE RANK2 WEALTHY MALE RANK2ADULTAVERAGE RANK2ADULT AVERAGE MALE RANK2ADULT AVRAGEFEMALE RANK2ADULTPOOR RANK2ADULTPOORMALE RANK2ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.953 .746 -.556 .497 -.554 .478 -.603 .541 -.56 -.529 .581 -.385 .384 -.369 -.374 -.383 .336 -.378 .335 -.455 -.238 -.169 .21

-.917 -.48 -.433 .448 -.485 .32 -.533 -.74 .321 -.366 -.319 -.302 .259 -.336 .121 -.345 .183 -.294 .272 -.38 -.233 .179 -.161

-.888 .752 -.791 -.611 .639 -.671 .641 .674 -.277 .628 .344 -.534 -.521 .422 -.504 -.498 -.568 -.493 -.415 .426 -.281 .253 -.196

-.829 -.523 -.628 .475 -.589 .616 .662 -.447 -.404 -.41 -.304 -.435 .496 .468 .411 .675 .645 -.399 .341 .333 -.322 -.406 -.159

-.688 -.39 .51 .596 .475 .737 .782 -.501 .458 -.319 -.304 .632 .526 .47 .558 .806 .772 .488 .407 .372 -.268 .369 -.16

-.76 -.395 .569 .619 .453 .761 .786 .552 -.419 -.317 -.338 -.707 .556 .487 .559 .798 .762 -.547 .427 .381 -.268 -.353 -.164

************************************************************************* TABLE 8.128 Top Jaccard values for Ward's method clustering of Model 6A

************************************************************************* GROUP

LEVEL

JACCARD

1 RANKl 2 ADULT FEMALE RANK3 3 ADULT FEMALE RANK2 4 ADULTMALERANK3 5 ADULT MALE RANK2 6 SUBADUL T RANK3 7 SUBADUL T RANK2 8 SUBADUL T CLAN2 RANK3 9 SUBADULT CLANl RANK3 10 SUBADULT MALE CLAN2 RANK3 11 SUBADULTFEMALECLAN2RANK3 12 SUBADULTFEMALECLAN1RANK3 13 SUBADULTMALECLANl RANK3 14 MALE WEALTHY RANKl 15 MALE WEALTHY RANK3 16 ADULT FEMALE CLANl RANK3 17 ADULT FEMALE CLAN2 RANK3 18 ADULTFEMALERANKl 19 SUBADULT 20 MALE RANKl 21 ADULTMALE 22 ADULT 23 ADULT FEMALE 24 MALE AVERAGE RANKl 25 ADULT MALE CLANl RANK3 26 ADULT MALE CLAN2 RANK3

4

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 .941 938 .923 .857 .85 .833 .823 .812 .786 .75 .667

5 5 6 6 7 7 8 8

9 9

12 12 14 15 11 11 10 4

10 3 2 3 14 13 13

************************************************************************* TABLE 8 .129 Top Jaccard values for Average linkage clustering of Model 6A

************************************************************************* GROUP

LEVEL

1 RANKl 2SUBADULTFEMALERANK3 3 SUBADULT MALE RANK3 4 SUBADUL T RANK2 5 ADULT FEMALE RANK3 6 ADULT FEMALE RANK2 7 SUBADULT FEMALE CLAN2 RANK3 8 SUBADULT FEMALE CLANl RANK3 9 SUBADUL T MALE CLAN2 RANK3 10 SUBADULT MALE CLANl RANK3 11 ADULT MALE RANK3 12 ADULT MALE RANK2 13 MALE WEALTHY RANKl 14 MALE WEALTHY RANK3 15 ADULT FEMALE CLANl RANK3 16 ADULT FEMALE CLAN2 RANK3 17 ADULTFEMALERANKl 18 SUBADULT

2 4 6 6

JACCARD

1 1 1 1 1 1 1 1 1 1 1 1 1 1 .941 .938 .923 .857

7 7 8 8 9 9

11 11 13 15 12 12 10 3

244

Appendix 1 ************************************************************************* TABLE 8.129 (Continued)

************************************************************************* GROUP

LEVEL

19 20 21 22 23 24 25 26 27

10 5

MALE RANKl ADULTMALE ADULT ADULT MALE CLANl RANK3 ADULT FEMALE MALE AVERAGE RANKl SUBADUL T MALE RANK3 ADULT MALE CLAN2 RANK3

JACCARD .85 .833 .823 .828 .812 .786 .739 .641 .583

3

14 5 13 4 2

14

************************************************************************ TABLE 8.130 Top Jaccard values for monothetic divisive clustering of Model 6A

************************************************************************ GROUP

LEVEL

JACCARD

-------------1 RANKl 2 ADULT FEMALE RANK2 3 ADULT FEMALE RANK3 4 ADULT MALE RANK2 5 ADULT MALE RANK3 6 SUBADULT RANK2 7 SUBADULT RANK3 8 MALERANKI 9 FEMALE RANK! 10 SUBADULT MALE RANK3 II SUBADULTFEMALERANK3 12 MALE WEAL THY RANK3 13 MALE WEALTHY RANKl 14 MALE AVERAGE RANK! 15 ADULT FEMALE CLAN! RANK3 16 ADULT FEMALE CLAN2 RANK3 17 FEMALE WEALTHY RANK! 18 FEMALE AVERAGE RANK! 19 SUBADULTCLAN2RANK2 20 SUBADULTCLANIRANK2 21 SUBADULT 22 ADULTMALE 23 ADULTFEMALE 24 ADULT MALE CLAN! RANK3 25 RANK3 26 FEMALE 27 ADULT MALE CLAN2 RANK3

2 5 5 6 6 7 7 8 8 9 9 10

1 I I 1 I I 1 I I 1 I I 1 I I 1 I I 1 I .857 .833 .812 .75 .641 .609 .583

11 11

12 12 13 13 15 15 4 3 4 14 2 3 14

***************************************************************************************** TABLE 8.131 Summary of top Jaccard values for clustering of Model 6A

***************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

I )CLAN! 2)CLAN2 3)LEADER 4)ADULT 5)SUBADULT 6)MALE ?)FEMALE 8 )ADULT MALE 9 )ADULT FEMALE 10 )RANKl 11 )RANKl ADULT 12 )RANK! ADULT MALE 13)RANK1ADULTFEMALE 14 )RANKl SUBADULT 15 )RANK! SUBADULTMALE 16)RANK1SUBADULTFEMALE 17 ) RANKl WEALTHY 18 ) RANK! MALE WEAL THY 19 ) RANKl ADULT MALE WEAL THY 20 ) RANKl SUB ADULT MALE WEALTHY 21 ) RANK! FEMALE WEALTHY 22 ) RANKl ADULT FEMALE WEAL THY 23 )RANKl SUBADULTFEMALEWEALTHY 24 ) RANK! A VERA GE 25) RANKl MALE AVERAGE 26) RANKl ADULT MALE AVERAGE 27 )RANK! SUBADULTMALEAVERAGE 28) RANKl FEMALE A VERA GE 29 )RANKl ADULT FEMALE AVERAGE 30 )RANK! SUBADULTFEMALEAVERAGE 31 )RANKl MALE 32) RANKl FEMALE

.45@2 .371@ 2 .5@ 14 .823@ 2 * .857 @4 * .519@ 3 .553@ 3 .833@ 3 * .812@ 3 * 1 @4 ** .697@ 4 .55@ 10 .923@ 10 ** .429@ 10 .3@ 10 .2@ 14 .429@ 14 I @ 14 ** .667@ 14 .333@ 14 .615@ 10 .538@ 10 .077@ 10 .737@ 14 * .786@ 14 * .5@ 14 .286@ 14 .312@ 10 .385@ 10 .214@ 14 .85@ 10 * .812@ 10 *

.484@2 .425@ 2 .5@ 13 .823@ 3 * .857@ 3 * .519@ 5 .553@ 5 .833@ 5 * .812@ 5 * 1 @2** .697@ 2 .55@ 10 .923@ 10 ** .429@ 10 .3@ 10 .2@ 13 .429@ 13 I@ 13 ** .667@ 13 .333@ 13 .615@ 10 .538@ 10 .077@ 10 .737@ 13 * .786@ 13 * .5@ 13 .286@ 13 .312@ 10 .385@ 10 .214@ 13 .85@ 10 * .812@ 10 *

.484@2 .425@ 2 .5@ 11 .563@ 2 .857 @4 * .519@ 3 .609@ 3 .833@ 3 * .812@4 * 1 @2 ** .697@ 2 .647@ 8 .75@ 8 * .303@ 2 .353@ 8 .333 @ 13 .571 @ 13 I@ 11 ** .667@ 11 .333 @ 11 I @ 13 ** .875 @13* .125@ 13 .579@ 11 1 @ 11 ** .636@ 11 .364@ 11 1@ 13 ** .625@ 13 .375@ 13 1@ 8 ** 1@ 8 **

245

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ***************************************************************************************** TABLE 8.131 (Continued)

***************************************************************************************** SOCIAL GROUP 33 )RANK2 34 ) RANK2 SUBADULT 35 )RANK2ADULT 36 ) RANK2 MALE 37 ) RANK2 FEMALE 38 )RANK2MALEADULT 39)RANK2ADULTFEMALE 40)RANK2SUBADULTFEMALE 41 )RANK2SUBADULTMALE 42 )RANK3 43 ) RANK3 SUBADULT 44 ) RANK3 ADULT 45 ) RANK3 MALE 46 ) RANK3 FEMALE 47 )RANK3MALEADULT 48)RANK3ADULTFEMALE 49)RANK3SUBADULTFEMALE 50 ) RANK3 SUB ADULT MALE 51)RANK3SUBADULTPOOR 52)RANK3SUBADULTAVERAGE 53 ) RANK3 AGED 40+ 54 ) RANK3 AGED 40+ MALE 55 ) RANK3 AGED 40+ FEMALE 56 ) RANK3 WEALTHY 57 ) RANK3 WEALTHY FEMALE 58 ) RANK3 WEAL THY MALE 59 ) RANK3 ADULT A VERA GE 60 ) RANK3 ADULT AVERAGE MALE 61)RANK3ADULTAVERAGEFEMALE 62)RANK3ADULTPOOR 63 )RANK3ADULTPOORMALE 64 ) RANK3 ADULT POOR FEMALE

WARD

AVERAGE

.35@ 7 1 @7 ** .513@ 5 .559@ 6 .769@ 5 * 1@ 6** 1@ 5 ** .286@ 7 .714@7 * .466@2 1 @7 ** .636@ 2 .655@ 6 .615@ 5 1@ 6** 1@ 5 ** .7 @9 * .579@ 9 .795 @7 * .222@ 8 .434@6 .639@ 6 .545@ 11 .688@ 15 .158@ 11 1@ 15 ** .349@ 5 .381 @ 13 .469@5 .294@ 13 .455@ 13 .375@ 5

.359@ 2 1@ 6** .513@ 7 .559@ 11 .769 @7 * 1@ 11 ** 1@ 7 * * .286@ 6 .714@ 6 * .641@ 2 .65@3 .636@ 3 .655@ 11 .615@ 7 1@ 11 ** 1@ 7 ** 1 @4** 1@ 6** .517@ 3 .222@ 8 .434@ 11 .639@ 11 .545@ 12 .688@ 15 .158@ 12 1@ 15 ** .349@ 7 .381 @ 15 .469@7 .294@ 15 .455@ 15 .375@ 7

DIVISIVE .359@ 2 1 @7 ** .513@ 5 .559@ 6 .769@ 5 * 1@ 6** 1@ 5 ** .286@ 7 .714@7 * .641@ 2 1 @7 ** .529@ 6 .655@ 6 .615@ 5 1 @6 ** 1@ 5 ** 1 @9 ** 1 @9 ** .795 @7 * .205@ 7 .434@ 6 .639@ 6 .531 @ 5 .688@ 10 .167@ 12 1@ 10 ** .349@ 5 .44@ 10 .469@ 5 .378@ 10 .56@ 10 .375@ 5

******************************************************************************************************************* TABLE 8.132 Highest correlations on axes for correspondence analysis of Model 6A

************************************************************************************************************** Positive

Negative

AXIS 1 ( 15.82 %) RANKl ADULTRANKl MALERANKl

.955 .785 .719

RANK3 ADULTRANK3

-.619 -.542

.811 .724 .535

SUBADULT SUBADULT RANK3 SUBADUL T POOR RANK3

-.811 -.755 -.622

.917 .714 .609

MALE SUBADULT

-.609 -.528

.506

ADULT FEMALE RANKl FEMALE RANKl FEMALE WEALTHY RANKl

-.617 -.604 -.574

.526

SUBADULT FEMALE RANK3 FEMALE RANK3

-.751 -.58

.537 .511

ADULTRANK2 RANK2 FEMALE RANK2

-.659 -.574 -.557

.504

AVERAGE RANKl MALE AVERAGE RANKl ADULT MALE AVERAGE RANKl

-.793 -.697 -.528

AXIS 2 ( 11.98 %) ADULT ADULT MALE ADULT MALE RANK3 AXIS 3 ( 10.93 %) ADULT FEMALE ADULT FEMALE RANK3 FEMALE AXIS 4 ( 8.99 %) ADULT MALE WEALTHY RANKl

AXIS 5 ( 7.78 %) SUBADULT MALE RANK3 AXIS 6 ( 7.22 %) MALERANK3 ADULTRANK3 AXIS 7 ( 6.46 %) WEALTHY RANKl

246

Appendix 1

***************************************************************************************************************** TABLE 8.132 (Continued)

***************************************************************************************************************** AXIS 8 ( 4.78 %) CLAN2

.731

CLANl

-.731

.454 .449 .417

ADULT MALE RANK2 CLAN2

-.484 -.417

FEMALE WEALTHY RANK3

-.587

AXIS 9 ( 4.19 %) AGED 40+ MALE RANK3 ADULT MALE POOR RANK3 CLANl AXIS 10 ( 3.26 %)

AXIS 11 ( 3.15 %) SUBADULT POOR RANK3 SUBADUL T MALE RANK3 SUBADUL T RANK3

.761 .596 .592

*********************************************************************************************************** TABLE 8.133 Highest correlations on axes for detrended correspondence analysis of Model 6A

*********************************************************************************************************** Positive

Negative

AXIS 1 ( 15.82 %) RANKl ADULTRANKl MALERANKl

.884 .724 .644

ADULTRANK3 RANK3 AGED 40+ RANK3

-.699 -.675 -.501

.631 .615 .606

ADULT MALE ADULT ADULT MALE RANK3

-.796 -.606 -.557

.646 .599 .581

ADULT FEMALE ADULT FEMALE

-.819 -.646 -.599

AXIS 2 ( 10.74 %) SUBADUL T RANK3 FEMALE RANK3 SUBADULT AXIS 3 ( 7.53 %) SUBADULT MALE SUBADUL T RANK3 AXIS 4 ( 4.37 %) SUBADUL T FEMALE RANK3 .505

******************************************************************************************************************** TABLE 8.134 Highest correlations on axes for unrotated PCA (covariance matrix) of Model 6A

******************************************************************************************************************** Positive

Negative

AXIS 1 ( 19.12 %) RANKl ADULTRANKl MALERANKl

.832 .707 .586

ADULTRANK3 ADULT MALE RANK3

-.612 -.538

.669 .566 .561

CLAN2 RANK3 FEMALE RANK3

-.669 -.576 -.541

.807 .744 .665

MALE ADULT MALE

-.636 -.54

.656 .502

CLAN2 RANK2 ADULTRANK2

-.656 -.578 -.564

AXIS 2 ( 16.1 %) CLAN! ADULT MALE RANK2 ADULT MALE AXIS 3 ( 12.84 %) ADULT FEMALE ADULT FEMALE RANK2 FEMALE RANK2 AXIS 4 ( 10.08 %) CLANl RANK3

247

Theoretical and Quantitative Approaches to the Study of Mortuary Practice **************************************************************************************************************** TABLE 8.134 (Continued)

**************************************************************************************************************** AXIS 5 ( 7.86 %) SUBADULT SUBADULT RANK2 RANK2

.65 .62 .543

ADULT ADULTRANK3

-.65 -.566

.483 .461 .444

ADULT MALE RANKl AGE 40+ FEMALE RANK3

-.427 -.419

.598 .524

SUBADULT FEMALE RANK3 SUBADUL T POOR RANK3 SUBADULT RANK3

-.726 -.577 -.576

.571

WEALTHY RANKl FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl

-.585 -.515 -.509

.575

SUBADULT MALE RANK3

-.674

AXIS 6 ( 6.45 %) ADULT FEMALE RANKl

.416

AXIS 7 ( 5.19 %) ADULT FEMALE RANKl ADULT FEMALE WEALTHY RANKl FEMALE WEALTHY RANKl AXIS 8 ( 4.31 %) SUBADULT MALE RANK2 SUBADULT RANK2 AXIS 9 ( 3.11 %) AVERAGE RANKl

AXIS 10 ( 2.95 %) SUBADULT FEMALE RANK2 AXIS 11 ( 2.78 %) ADULT MALE WEALTHY RANKl LEADER MALE WEALTHY RANKl

.599 .583 .565

************************************************************************************************************** TABLE 8.135 Highest correlations on axes for unrotated PCA (correlation matrix) of Model 6A

******************************************************************************************************************** Positive

Negative

AXIS 1 ( 20.4 %) RANKl ADULTRANKl WEALTHY RANKl

.849 .787 .741

AXIS 2 ( 12.8 %) ADULT MALE MALE

.767 .586

FEMALE ADULT FEMALE RANKl FEMALE RANKl

-.586 -.552 -.541

FEMALE RANK3

-.535

.728 .697 .637

SUBADULT

-.618

.634 .523

RANK2

-.566

.523

AVERAGE RANKl MALE AVERAGE RANKl ADULT MALE AVERAGE RANKl

-.633 -.601 -.52

.841

CLANl

-.841

AXIS 3 ( 10.7 %)

AXIS 4 ( 9.5 %) ADULT FEMALE ADULT FEMALE RANK2 ADULTRANK2 AXIS 5 ( 7.7 %) ADULTRANK3 RANK3 AXIS 6 ( 6.8 %) RANK2

AXIS 7 ( 5.1 %) CLAN2

248

Appendix 1 ******************************************************************************************************************* TABLE 8.135 (Continued)

******************************************************************************************************************* Positive

Negative

AXIS 8 ( 4 %) ADULT MALE RANK2 MALERANK2

.531 .503

AXIS 9 ( 3.6 %) SUBADUL T MALE RANK2

SUBADUL T FEMALE RANK3

.596

-.744

AXIS 10 ( 3.1 %) FEMALE AVERAGE RANKl ADULT FEMALE AVERAGE RANKl

.334 .325

******************************************************************************************************************* TABLE 8.136

Highest correlations on axes for Varimax-rotated PCA of Model 6A

******************************************************************************************************************* Positive

Negative

AXIS 1 ( 20.4 %) LEADER ADULT MALE WEALTHY RANKl MALE WEALTHY RANKl

.962 .931 .827

AXIS 2 ( 12.8 %) AVERAGE RANKl MALE AVERAGE RANKl RANKl

.933 .813 .809

AXIS 3 ( 10.7 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl ADULT FEMALE RANKl

.953 .949 .824

AXIS 4 ( 9.5 %) ADULT MALE RANK2 ADULT MALE MALERANK2

.868 .689 .64

AXIS 5 ( 7 .7 %) ADULT FEMALE RANK2 FEMALE RANK2 ADULTRANK2

.909 .766 .702

AXIS 6 ( 6.8 %) ADULT FEMALE RANK3 AGED 40+ FEMALE RANK3 ADULT FEMALE AVERAGE RANK3

.872 .758 .654

AXIS 7 ( 5.1 %) ADULT MALE RANK3 AGED 40+ MALE RANK3 ADULTRANK3

.83 .772 .68

AXIS 8 (4%) CLANl

.982

CLAN2

-.982

.506

SUBADUL T MALE RANK2 SUBADUL T RANK2

-.775 -.671

.285 .282 .218

SUBADULT MALE WEALTHY RANKl ADULT MALE POOR RANK3 ADULT POOR RANK3

-.294 -.224 -.218

AXIS 9 ( 3.6 %) SUBADUL T FEMALE RANK3 AXIS 10 ( 3.1 %) FEMALE AVERAGE RANKl ADULT FEMALE AVERAGE RANKl MALE WEALTHY RANK3

249

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

************************************************************************************************************** TABLE 8.137 Highest correlations on axes for Oblimin-rotated PCA of Model 6A

******************************************************************************************************************** Positive

Negative

AXIS 1 ( 20.4 %) LEADER ADULT MALE WEALTHY RANKl MALE WEALTHY RANKl

.96 .939 .848

AXIS 2 ( 12.8 %) ADULT MALE RANK2 ADULT MALE MALERANK2

.854 .733 .612

FEMALE

-.506

AVERAGE RANKl RANKl MALERANKl

-.903 -.887 -.806

CLANl

-.985

MALERANKl MALE WEALTHY RANKl SUBADULT MALE WEALTHY RANKl

-.329 -.312 -.307

AXIS 3 ( 10.7 %) FEMALE WEALTHY RANKl ADULT FEMALE WEALTHY RANKl ADULT FEMALE RANKl

.947 .943 .847

AXIS 4 ( 9.5 %) ADULT FEMALE RANK2 FEMALE RANK2 ADULTRANK2

.901 .758 .72

AXIS 5 ( 7.7 %) ADULT MALE RANK3 AGED 40+ MALE RANK3 ADULTRANK3

.85 .778

.733

AXIS 6 ( 6.8 %)

AXIS 7 ( 5.1 %) CLAN2

.985

AXIS 8 ( 4 %) ADULT FEMALE RANK3 AGED 40+ FEMALE RANK3 ADULTFEMALEAVERAGERANK3

.885 .758 .655

AXIS 9 ( 3.6 %) SUBADULT MALE RANK2 SUBADULT RANK2 MALERANK2

.784 .688 .516

AXIS 10 ( 3.1 %)

**********************************************************************************************

TABLE 8.138 Summary of highest correlations in PCNCA analyses of model 6A

**********************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 MALE 7 FEMALE 8 ADULTMALE 9 ADULTFEMALE 10 RANKl 11 RANKl ADULT 12 RANKl ADULT MALE 13 RANKl ADULT FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.731 .731 .489 .811 -.811 -.609 .609 .724 .917 .955 .785 .584 -.617

-.412 .412 .297 -.646 .646 .599 -.599 -.796 -.819 .884 .724 .518 .475

.669 -.669 .583 -.65 .65 -.636 .636 .561 .807 .832 .707 .483 .485

-.841 .841 .676 .618 -.618 .586 -.586 .767 .728 .849 .787 .701 -.552

.982 -.982 .962 .414 -.414 .483 -.483 .689 .578 .809 .624 .634 .824

-.985 .985 .96 .443 -.443 .506 -.506 .733 .645 -.887 -.714 -.685 .847

250

Appendix 1 *********************************************************************************************** TABLE 8.138 (Continued)

***********************************************************************************************

14 RANKl SUBADULT 15 RANKl SUBADULTMALE 16 RANKl SUBADULTFEMALE 17 RANKl WEALTHY 18 RANKl MALE WEALTHY 19 RANKl ADULT MALE WEALTHY 20 RANKl SUBAD MALE WEALTHY 21 RANKl FEMALE WEALTHY 22 RANKl ADULTFEMALEWLTHY 23 RANKl SUB AD FEMALE WLTHY 24 RANKl AVERAGE 25 RANKlMALEAVERAGE 26 RANKl ADULT MALE A VERA GE 27 RANKl SUB AD MALE A VERA GE 28 RANKl FEMALE AVERAGE 29RANK1ADULTFEMALEAVRAGE 30 RANKl SUB AD FEMALE A VRAGE 31 RANKl MALE 32 RANKl FEMALE 33 RANK2 34 RANK2SUBADULT 35 RANK2ADULT 36 RANK2 MALE 37 RANK2 FEMALE 38 RANK2MALEADULT 39 RANK2ADULTFEMALE 40RANK2SUBADULTFEMALE 41 RANK2SUBADULTMALE 42 RANK3 43 RANK3 SUBADULT 44 RANK3 ADULT 45 RANK3 MALE 46 RANK3 FEMALE 47 RANK3 MALE ADULT 48 RANK3ADULTFEMALE 49 RANK3 SUBADULTFEMALE 50 RANK3SUBADULTMALE 51RANK3SUBADULTPOOR 52 RANK3 SUBADULT AVERAGE 53 RANK3 AGED 40+ 54 RANK3 AGED 40+ MALE 55 RANK3 AGED 40+ FEMALE 56 RANK3 WEALTHY 57 RANK3 WEALTHY FEMALE 58 RANK3 WEALTHY MALE 59 RANK3 ADULT AVERAGE 60 RANK3 ADULT A VERA GE MALE 61 RANK3ADULTAVERAGEFEMALE 62 RANK3ADULTPOOR 63 RANK3ADULTPOORMALE 64 RANK3ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

.476 .395 .261 .645 .477 .506 .243 -.574 -.568 .138 -.793 -.697 -.528 -.442 -.375 .281 -.327 .719 -.604 -.574 -.398 -.659 -.36 -.557 -.484 -.541 -.245 .36 -.619 -.755 -.542 .537 -.58 .535 .714 -.751 .596 .761 -.378 .407 .454 .548 -.372 -.587 .339 .312 .266 .506 .296 .449 .33

.445 .36 .254 .578 .407 .34 .219 .398 .375 .129 .616 .483 .384 .283 .359 .282 .217 .644 .547 -.25 .248 -.481 -.267 -.409 -.462 -.497 .12 .244 -.675 .631 -.699 .579 .615 -.557 -.498 .521 .534 .532 .315 -.501 -.465 -.388 -.324 -.253 -.364 -.379 -.276 -.354 -.344 .32 -.216

.382 .312 .215 -.585 .565 .599 .18 -.515 -.509 .11 .585 .469 .367 .291 .35 .296 .211 .586 .536 -.578 .62 -.564 .497 .665 .566 .744 .575 .598 -.576 -.576 -.612 -.464 -.541 -.538 -.446 -.726 -.674 -.577 .266 -.461 -.438 -.419 -.359 -.225 -.361 -.318 -.287 -.325 -.255 .242 -.213

-.332 -.287 -.167 .741 .699 .701 -.3 -.501 -.502 .091 -.633 -.601 -.52 -.295 .334 .325 -.198 .734 -.541 -.566 .421 .637 .503 .556 .531 .697 .177 .596 .523 -.486 .634 -.398 -.535 .444 -.456 -.744 -.311 -.422 -.259 .498 -.466 .437 .395 .316 .377 .344 -.221 .346 .23 -.296 -.194

.464 .403 .232 .777 .827 .931 -.294 .953 .949 .174 .933 .813 .701 .403 .451 .366 .258 .765 .757 .564 -.671 .702 .64 .766 .868 .909 -.194 -.775 -.496 -.401 .68 .678 .519 .83 .872 .506 -.382 -.345 -.174 .546 .772 .758 .553 .558 .623 .45 .383 .654 .285 .351 .254

-.465 -.402 -.235 .804 .848 .939 -.307 .947 .943 .175 -.903 -.779 -.674 -.384 -.445 -.364 -.249 -.806 .79 .558 .688 .72 .612 .758 .854 .901 -.196 .784 -.485 -.411 .733 .678 .548 .85 .885 -.48 .384 -.349 -.186 .585 .778 .758 .592 .566 .654 .459 .393 .655 .288 .344 .267

************************************************************************* TABLE 8.139 Top Jaccard values for Ward's method clustering of Model 7A

************************************************************************* GROUP

LEVEL

JACCARD

----------1 ADULT 2 SUBADULT 3 SUBADULT MALE 4 SUBADULT FEMALE 5 FEMALE WEALTHY 6 MALE WEALTHY 7 SUBADULT MALE CLANl 8 SUBADULTMALECLAN2 9 ADULTMALE 10 ADULT FEMALE 11 SUBADULTFEMALECLANl 12 SUBADULTFEMALECLAN2 13 ADULTMALECLAN2 14 ADULTFEMALECLAN2 15 ADULT FEMALE CLANl AVERAGE 16 SUBADULTFEMALEAVERAGE 17 ADULTMALECLANl 18 SUBADULTFEMALECLANlPOOR 19 SUBADULTFEMALECLAN2POOR 20 ADULT FEMALE CLANl POOR

---------------2 2 4 4 5 6 7 7 3 3 9 9 10 8 8 15 10 11 12 13

1 1 1 1 1 1 1 1 .981 .986 .857 .833 .773 .733 .724 .625 .545 .5 .5 .182

251

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************ TABLE 8.140 Top Jaccard values for Average linkage clustering of Model 7A

************************************************************************ GROUP

LEVEL

JACCARD

----------1 ADULT 2 SUBADULT 3 ADULTMALE 4 ADULT FEMALE 5 SUBADULT CLAN! 6 SUBADUL T CLAN2 7 SUBADUL T FEMALE CLAN2 8 SUBADUL T MALE CLAN2 9 SUBADULT MALE CLAN! 10 SUBADULT FEMALE CLAN! 11 MALE WEAL THY 12 FEMALE WEAL THY 13 ADULT MALE CLAN2 14 AGED 40+ MALE CLAN2 AVERAGE 15 ADULT FEMALE CLAN! AVERAGE 16 ADULT FEMALE CLAN2 17 SUBADULTMALECLANl POOR 18 ADULTMALECLANl 19 SUBADULTFEMALECLAN2POOR 20 ADULT MALE CLAN2 POOR 21 SUBADULT MALE CLAN2 POOR

----------2 2 3 3 4 4 5 5 6 6 8 9 11 15 13 13 14 11 10 15 7

1 1 1 1 1 1 1 1 1 1 1 1 .818 .818 .75 .733 .611 .594 .5 .444 .067

************************************************************************ TABLE 8.141 Top Jaccard values for monothetic divisive clustering of Model 7A

************************************************************************ GROUP

LEVEL

JACCARD

----------1 ADULT 2 SUBADULT 3 ADULTMALE 4 ADULT FEMALE 5 FEMALE WEAL THY 6 MALE WEALTHY 7 SUBADULT CLAN! 9 SUBADULT MALE CLAN! 10 SUBADULT FEMALE CLAN! 11 SUBADULTFEMALECLAN2 12 SUBADULT MALE CLAN2 13 FEMALE CLAN! WEALTHY 14 FEMALE CLAN2 WEALTHY 15 ADULT MALE CLAN2 16 ADULTFEMALECLANlAVERAGE 17 ADULT FEMALE CLAN2 18 AGED 40+ MALE CLAN2 AVERAGE 19 MALE CLAN! WEALTHY 20 ADULT MALE CLAN! 21 ADULTMALECLANlAVERAGE 22 ADULTMALECLANl POOR 23 ADULT MALE CLAN2 POOR 24 ADULT FEMALE CLAN! POOR

----------2 2 3 3 4 5 6 8 8 10 10

1 1 1 1 1 1 1 1 1 1 1 1 1 .818 .75 .733 .727 .615 .594 .538 .5 .4 .333

11

11 9 7 7 14 12 9 15 15 14 13

***************************************************************************************** TABLE 8 .142 Summary of top J accard values for clustering of Model 7A

***************************************************************************************** SOCIAL GROUP 1 )CLAN! 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .44@2 .327@ 2 .176@ 6 1@ 2 ** 1 @2** 1@ 4 ** 1@ 4 ** .333@ 15 .813@ 2 * .981@ 3 ** .986@ 3 ** .553@ 5 1@ 5 ** 1@ 6** .283@ 6 .417@ 6 .345@ 8 .472@5 .568@ 6 .647@5 .57@ 3 .657@ 3 .64@2 .585@ 3 .681@ 3

252

AVERAGE

DIVISIVE

.44@2 .395 @4 .176@ 8 1@ 2 ** 1 @2** .52@ 2 .583@ 6 .187@ 2 .813@ 2 * 1@ 3 ** 1@ 3 ** .553@ 9 1@ 9 ** 1@ 8 ** .278@ 8 .405@ 8 .345@ 13 .458@ 9 .595@ 8 .66@9 .581 @ 3 .664@3 .64@2 .574@ 3 .69@3

.44@2 .395@ 6 .375@ 12 1@ 2 ** 1 @2** .52@ 2 .583@ 8 .187@ 2 .813@ 2 * 1@ 3 ** 1@ 3 ** .553@ 4 1@ 4 ** 1@ 5 ** .278@ 5 .405@5 .345@ 7 .458@4 .595@ 5 .66@4 .581 @ 3 .664@3 .64@2 .574@ 3 .69@3

Appendix 1

************************************************************************** TABLE 8.143 Top Jaccard values for Ward's method clustering of Model 7B

************************************************************************** GROUP 1 ADULT FEMALE 2 FEMALE WEALTHY 3 MALE WEALTHY 4 ADULTMALE 5 SUBADULT 6 SUBADULT MALE 7 SUBADULT FEMALE 8 SUBADULT MALE CLAN2 9 SUBADULT FEMALE CLAN2 10 SUBADULTFEMALECLANl II MALE 12 AGED 40+ MALE CLAN2 AVERAGE 13 SUBADULT MALE CLAN! POOR 14 ADULT MALE AVERAGE 15 ADULTFEMALEAVERAGE 16 SUBADULTMALECLAN2POOR 17 ADULT FEMALE CLAN! AVERAGE 18 ADULTFEMALECLAN2 19 AGED 40+ MALE CLAN! POOR 20 SUBADULTFEMALECLAN2POOR 21 ADULTMALECLANIAVERAGE 22 SUBADULT MALE CLAN!

LEVEL

JACCARD 1 I I .981 .987 .923 .9 .826 .812 .808 .721 .692 .667 .645 .619

2

5 6

3 3

4 4 8

7 7 2

14 8

10 9

13 11

.6 .542 .5 .5 .462 .444

9

10 15 14 12

.35

************************************************************************ TABLE 8.144 Top Jaccard values for Average linkage clustering of Model 7B

************************************************************************ GROUP

LEVEL

1 ADULT FEMALE 2 MALE WEALTHY 3 FEMALE WEALTHY 4 ADULT 5 SUBADULT 6 ADULTMALE 7 SUBADULT CLAN! 8 SUBADULT CLAN2 9 SUBADULT FEMALE CLAN! 10 SUBADULT MALE CLAN2 11 SUBADULT MALE CLAN! 12 SUBADULTFEMALECLAN2 13 ADULTMALEAVERAGE 14 AGED 40+ MALE CLAN! POOR 15 SUBADULT MALE CLAN! POOR 16 SUBADULTFEMALECLAN2POOR 17 SUBADULTCLAN2AVERAGE 18 SUBADULTFEMALECLANIAVERAGE 19 ADULTMALECLAN2AVERAGE

3 8 14

JACCARD 1 I I .992 .987 .981 .884 .842 .84 .773 .75 .722

2 2 3 4

4 5 6 5 6

.586 .5 .471 .462

11 11 12 7 15 9 10

.3 .167 .133

************************************************************************* TABLE 8.145 Top Jaccard values for monothetic divisive clustering of Model 7B

************************************************************************* GROUP 1 ADULT FEMALE 2 ADULTMALE 3 SUBADULT 4 FEMALE WEALTHY 5 MALE WEALTHY 6 SUBADULT FEMALE 7 SUBADULTMALE 8 SUBADULT MALE CLAN! 9 SUBADULT MALE CLAN2 10 SUBADULTFEMALECLAN2 11 SUBADULTFEMALECLANl 12 FEMALE CLAN2 WEALTHY 13 FEMALE CLAN! WEALTHY 14 MALE CLAN2 WEAL THY 15 MALE CLAN! WEALTHY 16 ADULTFEMALECLAN2AVERAGE 17 ADULTFEMALECLAN2POOR 18 ADULTMALECLAN2 19 ADULTFEMALECLANlAVERAGE 20 ADULT FEMALE CLAN2 21 MALE 22 SUBADULT MALE CLAN! POOR 23 ADULT MALE CLAN!

LEVEL

JACCARD

15

1 1 I 1 1 I 1 1 I 1 1 I 1 1 I 1 1 .818 .75 .733 .721 .667

9

.594

2

3 3 4

5 6 6 8 8

10 10 11 11 12 12 14 14 9 7 7 2

253

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

****************************************************************************************** TABLE 8 .146 Summary of top Jaccard values for clustering of Model 7B

****************************************************************************************** SOCIAL GROUP I )CLAN! 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17 )ADULT POOR FEMALE 18 ) ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

WARD .429@2 .352@ 2 .176@ 6 .568@ 2 .987@ 3 ** .923 @4 ** .9 @4 ** .273@ 13 .803@ 3 * .981@ 3 ** 1 @2** .553@ 5 I@ 5 ** 1@ 6** .262@5 .378@ 6 .34@5 .458@ 5 .645@ 10 .66@5 .721 @2 * .664@2 .48@2 .585@ 3 .69@2

AVERAGE

DIVISIVE

.442@2 .371 @4 .176@ 8 .992@ 2 ** .987@ 2 ** .513@ 2 .605@ 5 .211@ 7 .803 @2 * .981@ 3 ** 1@ 3 ** .553@ 14 I@ 14 ** 1@ 8 ** .262@ 14 .378@ 8 .34@ 14 .458@ 14 .611 @ 8 .66@ 14 .57@3 .664@3 .645@ 2 .585@ 3 .69@3

.429@2 .352@ 2 .231@ 12 .568@ 2 1@ 3 ** 1@ 6** I@ 6 ** .187@ 3 .813@ 3 * I@ 3 ** 1 @2** .553 @4 I@ 4 ** 1@ 5 ** .312@ 14 .405@5 .588@ 14 .458@4 .595@ 5 .66@4 .721 @2 * .664@2 .48@2 .574@ 3 .69@2

********************************************************************************* TABLE 8.147 Top Jaccard values for Ward's method clustering of Model 7C

********************************************************************************* GROUP 1 RANK2 2 RANK! 3 ADULT MALE RANK2 4 SUBADUL T RANK2 5 ADULT FEMALE RANK2 6 MALE WEALTHY RANK! 7 ADULT FEMALE AVERAGE RANK! 8 ADULTMALEAVERAGERANKl 9 SUBADULT CLAN! RANK2 10 ADULT FEMALE RANK! 11 SUBADULTCLAN2RANK2 12 MALE WEALTHY RANK2 13 SUBADULT AVERAGE RANK! 14 SUBADULTFEMALECLAN1RANK2 15 SUBADULT MALE CLAN! RANK2 16 FEMALE WEAL THY RANK! 17 FEMALE RANK2 18 ADULT MALE CLAN2 RANK2 19 FEMALE WEAL THY RANK2 20 ADULT MALE CLAN! RANK2 21 ADULTFEMALECLAN2RANK2 22 MALE RANK! 23 AVERAGE RANK! 24 SUBADULTFEMALECLAN2RANK2 25 ADULT FEMALE CLAN! RANK2

LEVEL

JACCARD

2 2

1 1

3

I

4 4

1 1

8

I

12 14

1 1

6

.964 .95 .95 .938 .929

5 6 9

14 11 II

12 3

7 10 7 13 5 8 15 13

.9 .889 .889 .871 .829 .786 .76 .76 .727 .629 .562 .542

********************************************************************************* TABLE 8 .148 Top Jaccard values for Average linkage clustering of Model 7C

********************************************************************************* GROUP 1 RANK2 2 RANK! 3 SUBADUL T RANK2 4 ADULTRANK2 5 SUBADULT CLAN! RANK2 6 SUBADUL T CLAN2 RANK2 7 SUBADULT MALE CLAN2 RANK2 8 SUBADULT FEMALE CLAN2 RANK2 9 MALE WEALTHY RANKl 10 AVERAGE RANK! 11 ADULTMALERANK2 12 SUBADULTFEMALECLAN1RANK2 13 ADULT FEMALE RANK2 14 FEMALE WEAL THY RANK! 15 FEMALE WEAL THY RANK2 16 SUBADULT MALE CLAN! RANK2 17 ADULT MALE CLAN! RANK2

LEVEL

JACCARD

2 2 3

I I

3

I I

1

4 4

1

7 7

I I

9 II

1 .971 .944 .95 .939 .889 .786

5 8

5 II

14 8

13

254

.778 .76

Appendix 1 ********************************************************************************* TABLE 8.148 (Continued)

********************************************************************************* GROUP

LEVEL

18 ADULT MALE CLAN2 RANK2 19 SUBADULTMALEAVERAGERANK2 20 ADULT CLAN2 POOR

JACCARD .743 .333 .312

13 6

10

********************************************************************************** TABLE 8.149 Top Jaccard values for monothetic divisive clustering of Model 7C

********************************************************************************** GROUP

LEVEL

JACCARD

1 RANKl 2 RANK2 3 ADULT MALE RANK2 4 ADULT FEMALE RANK2 5 SUBADULT RANK2 6 FEMALE WEAL THY RANKl 7 ADULT MALE CLANl RANK2 8 ADULT MALE CLAN2 RANK2 9 ADULT FEMALE CLANl RANK2 10 ADULT FEMALE CLAN2 RANK2 11 SUBADULT CLAN2 RANK2 12 SUBADULT CLANl RANK2 13 ADULT MALE AVERAGE RANKl 14 MALE WEALTHY RANKl 15 ADULT FEMALE AVERAGE RANKl 16 SUBADULTAVERAGERANKl 17 MALE CLAN2 WEALTHY RANK2 18 SUBADULT MALE CLANl RANK2 19 SUBADULT FEMALE CLANl RANK2 20 FEMALE CLANl WEALTHY RANK2 21 MALE CLAN2 WEALTHY RANKl 22 MALE CLANl WEAL THY RANKl 23 FEMALE RANK2 24 FEMALE RANKl 25 MALE RANKl 26 FEMALE AVERAGE RANKl 27 ADULT FEMALE CLANl AVERAGE RANK2

2 2 3 4 4 6 7 7 8 8 9 9

10 10 11 11 12 13 13 14 15 15 3 5 5

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 .871 .853 .792

6

.8

14

.667

****************************************************************************************** TABLE 8.150 Summary of top Jaccard values for clustering of Model 7C

****************************************************************************************** SOCIAL GROUP 1 )CLANl 2)CLAN2 3)LEADER 4)ADULT 5 )SUBADULT 6 )AGED 40+ 7)MALE 8)FEMALE 9 )ADULT MALE 10 )ADULT FEMALE 11 )RANKl 12 )RANKl ADULT 13 )RANKl ADULT MALE 14)RANK1ADULTFEMALE 15 )RANKl SUBADULT 16 )RANKl SUBADULTMALE 17)RANK1SUBADULTFEMALE 18 ) RANKl WEALTHY 19 ) RANKl MALE WEAL THY 20 ) RANKl ADULT MALE WEAL THY 21 )RANKl SUBADULTMALEWEALTHY 22 ) RANKl FEMALE WEALTHY 23 ) RANKl ADULT FEMALE WEAL THY 24 )RANKl SUBADULTFEMALEWEALTHY 25)RANK1AVERAGE 26) RANKl MALE AVERAGE 27) RANKl ADULT MALE AVERAGE 28 )RANKl SUBADULTMALEAVERAGE 29) RANKl FEMALE A VERA GE 30 )RANKl ADULT FEMALE AVERAGE 31 )RANKl SUBADULTFEMALEAVERAGE 32) RANKl MALE 33 ) RANKl FEMALE 34 )RANK2 35 )RANK2SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 ) RANK2 MALE ADULT

WARD .456@ 2 .393@ 2 .2@8 .552@ 2 .712@4 * .345@ 2 .6@3 .664@ 3 .783@ 3 * .708 @4 * 1 @2 ** .642@ 2 .6@ 14 .95@ 5 ** .737@ 14 * .278@ 14 .6@ 14 .526@ 8 1@ 8 ** .6@8 .4@8 .889@ 12 * .875@ 12 * .111 @ 12 .642@2 .643@ 14 1@ 14 ** .357@ 14 .6@ 12 1@ 12 ** .571@ 14 .727@ 5 * .69@5 1 @2 ** 1 @4 ** .68@2 .818@ 3 * .871@ 3 * 1@ 3 **

255

AVERAGE

DIVISIVE

.456@ 2 .393@ 2 .2@9 .746@ 3 * .712@ 3 * .479@ 3 .567@ 5 .46@2 .739@ 5 * .676@ 5 1 @2 ** .642@ 2 .316@ 9 .442@ 9 .358@ 2 .267@ 9 .25@ 11 .526@ 9 1 @9 ** .6@9 .4@9 .889@ 11 * .875@ 11 * .111@11 .971@ 11 ** .4@ 11 .257@ 11 .143@ 11 .571 @ 11 .343@ 11 .229@ 11 .453@ 2 .674@9 1 @2 ** 1@ 3 ** 1@ 3 ** .773@ 5 * .551 @ 2 .944@ 5 **

.456@ 2 .393@ 2 .333@ 15 .552@ 2 .712@4 * .345@ 2 .6@3 .664@3 .783@ 3 * .708 @4 * 1 @2** .642@2 .789@ 5 * .632@ 11 .684@ 11 .444@ 15 .533 @ 11 .526@ 10 1@ 10 ** 1 @ 15 ** 1 @ 15 ** 1 @6 ** .778@ 6 * .222@ 6 .735@ 6 * .643@ 10 1@ 10 ** .385@ 11 .8 @6 * 1 @ 11 ** .615@ 11 .792@ 5 * .853@ 5 * 1 @2 ** 1 @4 ** .68@ 2 .818@ 3 * .871@ 3 * 1@ 3 **

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ****************************************************************************************** TABLE 8.150 (Contimued)

****************************************************************************************** SOCIAL GROUP 40)RANK2ADULTFEMALE 4l)RANK2SUBADULTFEMALE 42 ) RANK2 SUB ADULT MALE 43)RANK2SUBADULTPOOR 44)RANK2SUBADULTAVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 )RANK2AGED 40+FEMALE 48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEAL THY MALE 51 )RANK2ADULT AVERAGE 52 ) RANK2 ADULT AVERAGE MALE 53 )RANK2ADULT AVERAGE FEMALE 54)RANK2ADULTPOOR 55 )RANK2ADULTPOORMALE 56)RANK2ADULTPOORFEMALE

WARD

AVERAGE .939@ 5 ** .745@ 3 * .538@ 8 .787@ 3 * .267@4 .58@3 .627@ 5 .657@ 14 .379@ 14 .786@ 14 * .469@ 13 .46@3 .483@ 13 .636@ 14 .25@ 3 .222@ 5 .235@ 14

1@ 4 ** .745 @4 * .615@ 11 .787 @4 * .276@6 .4@ 3 .593@ 3 .605@ 10 .5 @9 .786@ 10 * .938@ 9 ** .373@ 10 .483@ 7 .629@ 10 .294@9 .417@ 9 .286@ 10

DIVISIVE 1@ 4 ** .745 @4 * .667@ 13 .787 @4 * .267@ 9 .4@ 3 .593@ 3 .565@ 4 .31 @ 12 .643@ 14 .6@ 12 .316@ 3 .444@ 3 .478@ 4 .286@ 12 .4@ 12 .25@ 14

************************************************************************* TABLE 8.151 Top Jaccard values for Ward's method clustering of Model 7D

************************************************************************* GROUP

LEVEL

JACCARD

1 RANK2 2 RANK! 3 SUBADUL T RANK2 4 ADULTRANK2 5 FEMALE WEALTHY RANK! 6 ADULTMALEAVERAGERANKl 7 MALE WEALTHY RANK! 8 ADULTMALERANK2 9 ADULT FEMALE RANK2 10 ADULT MALE CLAN2 RANK2 11 ADULT MALE CLAN! RANK2 12 SUBADULTCLAN1RANK2 13 FEMALE RANK! 14 ADULT FEMALE CLAN2 RANK2 15 MALE RANKl 16 FEMALEAVERAGERANKl 17 ADULT FEMALE CLAN! RANK2 18 SUBADULTCLAN2RANK2 19 SUBADULTCLAN2AVERAGERANK1 20 CLANlAVERAGERANKl 21 SUBADULT AVERAGERANK2 22 SUBADULT CLAN! POOR 23 MALE CLAN2 AVERAGE RANK2 24 SUBADULT FEMALE RANK2

2 2

5

1 1 1 1 1 1 1 .982 .978 .966 .926 .875 .853 .846 .792

8

.8

9 7

.792 .789 .692

3 3

8 11 11 4 4 6 6 7

5 9

12 12 15 14 13 14

.6

.5

.409 .385 .143

************************************************************************** TABLE 8 .152 Top Jaccard values for Average linkage clustering of Model 7D

************************************************************************** GROUP 1 2 3 4

RANK2 RANKl SUBADULTCLANlWEALTHY ADULTRANK2 5 ADULT MALE RANK2 6 ADULT FEMALE RANK2 7 SUBADUL T RANK2 8 FEMALE RANKl 9 MALERANKl 10 SUBADULT FEMALE RANK2 11 SUBADULTMALEPOOR 12 SUBADULTFEMALECLAN2RANK2 13 SUBADULTFEMALECLAN1RANK2 14 SUBADULTFEMALECLAN2POOR 15 SUBADULTFEMALECLANlPOOR 16 SUBADULTCLAN1RANK2 17 SUBADUL TA VERAGE RANK2 18 SUBADULT MALE CLAN! POOR 19 ADULTFEMALECLANl POOR 20 SUBADULTCLAN2AVERAGERANK2 21 ADULT FEMALE CLAN2 POOR 22 ADULT MALE CLAN2 POOR

LEVEL

JACCARD 1 1 1 .98 .981 .978 .959 .871 .88 .833 .778 .733

2 2 6

3 4 4

3 11 11

5 5 7 7 13 15 9 15 10 14 13 12

.645 .615 .6

.588 .421 .4

.375 .25 .167 .1

8

256

Appendix 1 ********************************************************************************* TABLE 8.153 Top Jaccard values for monothetic divisive clustering of Model 7D

********************************************************************************* GROUP

LEVEL

1 RANK! 2 RANK2 3 ADULT MALE RANK2 4 ADULT FEMALE RANK2 5 SUBADULT RANK2 6 MALERANKI 7 FEMALE RANK! 8 ADULT MALE CLAN! RANK2 9 ADULT MALE CLAN2 RANK2 10 FEMALEAVERAGERANKl 11 FEMALE WEALTHY RANK! 12 ADULT FEMALE CLAN2 RANK2 13 ADULT FEMALE CLAN! RANK2 14 SUBADULT CLAN2 RANK2 15 SUBADULT CLAN! RANK2 16 MALEAVERAGERANKl 17 MALE WEAL THY RANK! 18 MALE CLAN2 WEALTHY RANK2 19 FEMALE CLAN! WEALTHY RANK2 20 MALE CLAN! WEALTHY RANK! 21 MALE CLAN2 WEALTHY RANK! 22 MALE CLAN! WEAL THY RANK2 23 SUBADULT MALE CLAN! RANK2 24 SUBADULTFEMALECLANIRANK2 25 FEMALE RANK2 26 ADULT FEMALE CLAN! AVERAGE RANK2

JACCARD 1 I I 1 I I 1 I I 1 I I 1 I I 1 I I 1 I I 1 I I .871 .667

2

2 3 4 4

5 5 6 6 7 7 8 8 9 9

10 10 11 12 13 13 14 15 15 3 12

***************************************************************************************** TABLE 8.154 Summary of top Jaccard values for clustering of Model 7D

***************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

I )CLAN! 2)CLAN2 3)LEADER 4 )ADULT 5 )SUBADULT 6 )AGED 40+ ?)MALE 8)FEMALE 9 )ADULT MALE 10 )ADULT FEMALE 11 )RANK! 12 )RANK! ADULT 13 )RANK! ADULT MALE 14)RANKIADULTFEMALE 15 )RANK! SUBADULT 16 )RANK! SUBADULTMALE 17)RANKISUBADULTFEMALE 18 ) RANK! WEALTHY 19 ) RANK! MALE WEAL THY 20 ) RANK! ADULT MALE WEAL THY 21 )RANK! SUBADULTMALEWEALTHY 22 ) RANK! FEMALE WEALTHY 23 ) RANK! ADULT FEMALE WEAL THY 24 ) RANK! SUBADULT FEMALE WEALTHY 25 )RANK! AVERAGE 26) RANK! MALE AVERAGE 27) RANK! ADULT MALE AVERAGE 28 )RANK! SUBADULTMALEAVERAGE 29) RANK! FEMALE A VERA GE 30 ) RANK! ADULT FEMALE A VERA GE 31 )RANK! SUBADULTFEMALEAVERAGE 32) RANK! MALE 33 ) RANK! FEMALE 34 )RANK2 35 )RANK2SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 ) RANK2 MALE ADULT 40)RANK2ADULTFEMALE 41)RANK2SUBADULTFEMALE 42 ) RANK2 SUBADUL T MALE 43)RANK2SUBADULTPOOR 44 )RANK2SUBADULT AVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 )RANK2AGED 40+FEMALE 48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEALTHY MALE

.456@ 2 .393@ 2 .2@ 11 .746@ 3 * .712@ 3 * .479@ 3 .593@ 4 .46@2 .771 @4 * .692@4 I @2 ** .642@ 2 .789@ 5 * .559@ 5 .419@ 8 .267@ 11 .438@ 12 .526@ 11 I@ 11 ** .6@ 11 .4@ 11 1@ 8 ** .778@ 8 * .222@ 8 .735@ 8 * .643@ 11 I@ 11 ** .214@ 12 .8@ 8 * .5@ 12 .5@ 12 .792@ 5 * .853@ 5 * 1 @2 ** 1@ 3 ** I@ 3 ** .806 @4 * .556@ 4 .982@ 4 ** .978@ 4 ** .745@ 3 * .273@ 10 .787@ 3 * .5@ 15 .58@3 .582@ 4 .543@ 4 .29@3 .32@9 .273@ 4

.456@ 2 .393@ 2 .087@ 11 .731@ 3 * .691@ 3 .463@ 3 .589@ 4 .46@2 .768 @4 * .692@4 I @2 ** .654@ 6 .652@ 11 .655@ 11 .358@ 2 .28@ 11 .258@ 11 .358@ 2 .435@ 11 .261@ 11 .174@ 11 .267@ 11 .241@ 11 .5@6 .654@ 6 .48@ 11 .391@ 11 .12@ 11 .633@ 11 .414@ 11 .233@ 11 .88@ 11 * .871@ 11 * 1 @2 ** .959@ 3 ** .98@ 3 ** .803 @4 * .556@ 4 .981@ 4 ** .978@ 4 ** .833@ 5 * .583@ 5 .755@ 3 * .421 @ 15 .56@ 3 .585@ 8 .568@ 12 .296@ 3 .378@ 14 .288@ 8

.456@2 .393@ 2 .333 @ 13 .552@ 2 .712@4 * .345@ 2 .6@3 .664@ 3 .783@ 3 * .708 @4 * I @2 ** .642@2 .625@ 5 .655@ 5 .358@ 2 .444@ 13 .364@ 7 .526@ 10 I@ 10 ** I @ 13 ** I @ 13 ** 1@ 7 ** .778@ 7 * .222@7 .642@2 1@ 10 ** .643@ 10 .357@ 10 1@ 7 ** .6@7 .4@7 1@ 5 ** I@ 5 ** 1 @2 ** 1 @4 ** .68@2 .818@ 3 * .871@ 3 * I@ 3 ** 1 @4 ** .745 @4 * .667@ 15 .787 @4 * .267@ 9 .4@3 .593@ 3 .565@ 4 .31@ 11 .643@ 12 .6@ 11

257

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

****************************************************************************************** TABLE 8.154 (Continued)

****************************************************************************************** SOCIAL GROUP 51 )RANK2ADULT AVERAGE 52 ) RANK2 ADULT AVERAGE MALE 53 )RANK2ADULT AVERAGE FEMALE 54)RANK2ADULTPOOR 55 )RANK2ADULTPOORMALE 56)RANK2ADULTPOORFEMALE

WARD .46@3 .436@4 .489@4 .25@3 .346@ 13 .2@9

AVERAGE

DIVISIVE

.469@3 .462@ 8 .489@4 .23@3 .259 @4 .231@ 14

.316@ 3 .483@ 14 .478@ 4 .286@ 11 .4@ 11 .25@ 12

*********************************************************************************************** TABLE 8.155 Sununary of highest correlations in PCNCA analyses of Model 7A

***********************************************************************************************

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBADULTFEMALE 8 SUBADULT AVERAGE 9 SUBADULTPOOR 10 ADULTMALE 11 ADULTFEMALE 12 WEALTHY 13 WEALTHYFEMALE 14 WEALTHY MALE 15 ADULTPOOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULTAVERAGEMALE 20 ADULT AVERAGEFEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.793 .793 .423 .924 -.924 -.728 .74 -.662 -.715 .707 -.879 .576 -.573 .495 -.444 .35 .292 -.53 - .423 -.477 .748 -.748 .588 .516 -.747

.77 -.77 -.174 -.934 .934 .593 .607 .437 .74 -.815 .722 -.562 .547 -.413 -.187 -.311 -.139 -.365 -.533 .378 -.742 .742 -.605 -.607 .619

.972 -.972 .297 .774 -.774 -.641 .7 -.303 -.646 .926 .893 .479 .622 .681 -.228 -.267 .184 .349 .505 .499 .559 -.559 .611 .709 .775

-.694 .694 .559 .797 -.797 -.619 .645 -.366 -.64 .833 .74 .702 .706 .806 -.318 .279 .251 -.425 -.444 .506 .608 -.608 .61 .641 .653

.987 -.987 .937 .821 -.821 -.711 .782 -.383 -.652 .892 .847 .771 .941 .856 .238 .384 .299 .566 .644 .669 -.59 .59 .626 .804 .735

-.981 .981 .935 .909 -.909 -.776 .927 .408 -.736 .675 .64 .737 .972 .932 .269 .267 .221 .555 .404 .479 .449 -.449 .679 .563 .553

*********************************************************************************************** TABLE 8 .156 Summary of highest correlations in PCNCA analyses of Model 7B

***********************************************************************************************

1 CLANl 2 CLAN2 3 SHAMAN 4 ADULT 5 SUBADULT 6 SUBADULTMALE 7 SUBADULTFEMALE 8 SUBADULT AVERAGE 9 SUBADULTPOOR 10 ADULTMALE 11 ADULTFEMALE 12 WEALTHY 13 WEALTHYFEMALE 14 WEALTHY MALE 15 ADULTPOOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULTAVERAGEMALE 20 ADULT AVERAGEFEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.815 .815 -.418 .693 -.693 -.749 .733 .74 -.646 .84 -.91 .513 -.668 .702 -.419 -.331 -.267 -.536 -.459 -.494 -.592 .592 -.53 .651 -.766

.656 -.656 .289 .749 -.749 -.586 -.652 -.411 -.56 .795 .661 .506 .602 .6 .173 .239 -.209 .241 .391 .437 -.686 .686 .428 .619 .602

.93 -.93 .303 .784 -.784 -.522 -.45 -.292 -.663 .912 .877 .49 .598 .687 -.361 -.276 -.224 -.391 .464 .501 .562 -.562 .614 .716 .751

-.696 .696 .533 .744 -.744 -.483 .59 .658 -.605 .828 .816 .639 .735 .834 -.311 -.229 .27 -.431 -.44 .52 .541 -.541 .588 .641 .712

.981 -.981 .952 .805 -.805 -.728 .677 .487 -.619 .878 .848 .748 .942 .901 .241 .42 .318 .538 .659 .651 -.561 .561 .616 .808 .719

-.98 .98 .974 .849 -.849 .85 -.784 .444 -.675 -.828 .718 -.743 -.976 .947 .246 -.412 .263 .513 -.616 .534 -.598 .598 .635 -.788 .611

258

Appendix 1

*********************************************************************************************** TABLE 8.157 Summary of highest correlations in PCNCA analyses of Model 7C

***********************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANKl 12 RANKl ADULT 13 RANKl ADULT MALE 14 RANKl ADULT FEMALE 15 RANKl SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEALTHY 19 RANKl MALE WEALTHY 20 RANKl ADULT MALE WEALTHY 21 RANKl SUBAD MALE WEALTHY 22 RANKl FEMALE WEALTHY 23 RANKl AD FEMALE WEALTHY 24 RANKl SUB AD FEMALE WLTHY 25 RANKlAVERAGE 26 RANKl MALE A VERA GE 27 RANKl ADULT MALE A VERA GE 28 RANKl SUB AD MALE A VRAGE 29 RANKlFEMALEAVERAGE 30 RANKlADULTFEMALEAVRAGE 31 RANKl SUBADFEMALEAVRAGE 32 RANKl MALE 33 RANKl FEMALE 34 RANK2 35 RANK2SUBADULT 36 RANK2ADULT 37 RANK2MALE 38 RANK2 FEMALE 39 RANK2MALEADULT 40 RANK2ADULTFEMALE 41 RANK2SUBADULTFEMALE 42 RANK2SUBADULTMALE 43 RANK2 SUBADULT POOR 44RANK2SUBADULTAVERAGE 45 RANK2 AGED 40+ 46 RANK2 AGED 40+ MALE 47 RANK2AGED 40+FEMALE 48 RANK2 WEALTHY 49 RANK2 WEALTHY FEMALE 50 RANK2 WEALTHY MALE 51 RANK2ADULT AVERAGE 52 RANK2ADULT AVERAGE MALE 53 RANK2 ADULT A VER FEMALE 54 RANK2ADULTPOOR 55 RANK2ADULTPOORMALE 56 RANK2ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.555 .555 -.448 -.753 .753 -.444 .672 -.672 .732 -.624 .949 .775 .545 .688 .435 .313 .288 .6 -.486 -.516 -.319 -.559 -.527 -.2 .646 .492 .416 .252 .513 .519 .317 .642 .694 -.949 .889 -.754 .69 -.705 .768 -.714 .738 -.781 .621 .623 -.498 .618 -.523 .585 -.548 .554 -.393 .434 -.373 -.342 .215 -.307

.399 -.399 .211 -.257 .257 -.137 .706 -.706 -.679 .582 .923 .738 .489 .523 .445 .307 .306 .547 .401 .34 .221 .498 .461 .185 .658 .411 .348 .209 .474 .385 .26 .592 .611 -.923 .126 -.796 -.698 .609 -.778 .65 -.351 .461 -.222 .287 -.53 -.634 .496 -.415 .481 -.523 -.41 -.445 .337 -.24 -.238 .209

.813 -.813 -.209 .698 -.698 .397 .62 -.62 .686 -.509 .933 .741 .493 .506 .455 .329 .299 -.711 -.439 -.353 -.278 -.578 -.561 -.169 .692 .472 .377 .27 .488 .395 .267 .609 .606 -.933 -.822 -.824 .622 -.716 .723 -.624 -.769 -.236 -.707 -.339 -.549 .595 -.478 -.385 -.426 .474 -.459 .421 -.367 -.252 .224 -.191

.746 -.746 .657 .755 -.755 .417 .649 -.649 .696 -.633 .907 .784 .619 -.631 .361 .3 .199 .667 .571 .632 -.411 .484 .489 -.143 .545 -.541 -.507 -.211 .55 .551 -.246 .694 -.634 -.907 -.804 -.723 .668 -.683 .732 -.728 -.676 .563 -.616 -.468 -.482 .593 -.545 .672 -.59 .596 -.538 -.422 -.408 -.326 -.277 -.351

.989 -.989 .963 .58 -.58 .444 .6 -.6 .769 .72 .783 .669 .791 .666 .321 .346 .274 .758 .792 .666 .421 .911 .902 .232 .905 .431 .457 .266 .767 .714 .31 .862 .724 -.783 -.556 .618 .701 -.522 .86 .847 -.482 .794 -.399 -.37 .53 .812 .789 .691 .949 .957 .456 .732 .713 -.211 .31 .369

.992 -.992 .988 .588 -.588 .426 .607 -.607 .781 .702 .853 .729 -.824 .701 .349 -.355 .281 .792 -.8 -.691 -.403 .916 .907 .232 .889 -.455 -.478 .26 .738 .684 .3 -.894 .757 -.853 -.545 .668 .733 -.512 .893 .852 -.503 .786 -.416 -.357 .535 .821 .777 .68 .975 .986 .471 .711 .691 .225 .306 .357

******************************************************************************************************* TABLE 8.158 Summary of highest correlations in PCNCA analyses of Model 7D

*******************************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANKl 12 RANKl ADULT 13 RANKl ADULT MALE 14 RANKl ADULT FEMALE 15 RANKl SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEALTHY 19 RANKl MALE WEALTHY

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.695 .695 -.495 .749 -.749 .417 -.622 .622 -.652 .675 .946 .755 .542 .581 .457 .339 .292 .588 -.59

.484 -.484 .199 -.267 .267 .186 .682 -.682 .699 -.476 .915 .718 -.54 .477 .457 .327 .304 .535 .413

.75 -.75 -.345 .66 -.66 .396 .606 -.606 .68 -.517 .938 .744 .502 .502 .458 .337 .296 -.592 -.4

.794 -.794 .645 .766 -.766 .441 .686 -.686 .732 -.662 .907 .784 .623 -.633 .361 .304 .196 .668 .574

.984 -.984 .963 .572 -.572 .408 .593 -.593 .808 .706 .747 .652 .782 .625 .289 .356 .26 .751 .794

.985 -.985 .988 .582 -.582 .413 .567 -.567 .795 .692 .782 .687 .824 .682 .297 .369 .263 .797 .789

259

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ******************************************************************************************************* TABLE 8.158 (Continued)

*******************************************************************************************************

20 RANKl ADULT MALE WEAL THY 21 RANKl SUBADMALEWEALTHY 22 RANKl FEMALE WEALTHY 23 RANKl ADULT FEMALE WEAL THY 24 RANKl SUBADFEMALEWEALTHY 25 RANKl AVERAGE 26 RANKl MALE AVERAGE 27 RANKl ADULT MALE A VERA GE 28 RANKl SUBADMALEAVERAGE 29 RANKl FEMALE AVERAGE 30 RANKlADULTFEMALEAVERAGE 31 RANKl SUBADFEMALEAVERAGE 32 RANKl MALE 33 RANKl FEMALE 34 RANK2 35 RANK2 SUBADULT 36 RANK2ADULT 37 RANK2 MALE 38 RANK2 FEMALE 39 RANK2MALEADULT 40 RANK2ADULTFEMALE 41 RANK2SUBADULTFEMALE 42 RANK2SUBADULTMALE 43 RANK2SUBADULTPOOR 44 RANK2SUBADULT AVERAGE 45 RANK2 AGED 40+ 46 RANK2 AGED 40+ MALE 47 RANK2 AGED 40+ FEMALE 48 RANK2 WEAL THY 49 RANK2 WEALTHY FEMALE 50 RANK2 WEALTHY MALE 51RANK2ADULTAVERAGE 52 RANK2ADULT AVERAGE MALE 53 RANK2ADULT AVRAGEFEMALE 54 RANK2ADULTPOOR 55 RANK2ADULTPOORMALE 56 RANK2ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.593 .362 -.594 -.569 .213 .653 .543 .456 .281 -.545 -.511 .36 .656 .581 -.946 -.906 -.75 -.681 .646 -.719 .764 -.767 .806 -.714 -.655 -.484 -.564 .519 .601 .622 -.534 -.422 .605 -.456 -.401 .296 -.356

.331 .24 .397 .387 .128 .657 -.534 -.494 .219 .464 .361 .273 -.636 .586 -.915 .375 -.79 .646 -.736 .756 -.619 -.37 .491 .276 .238 -.511 .591 -.435 -.444 -.464 .527 -.404 .412 -.338 -.207 .239 -.167

.309 -.297 -.442 -.444 .161 .695 .455 .385 .234 .483 .389 .268 .622 .602 -.938 -.765 -.813 .622 -.691 .735 -.563 -.705 -.237 -.64 -.348 -.548 .591 -.417 -.386 -.394 .486 -.451 .4 -.382 -.244 .259 .267

.627 .38 .519 .525 .164 .544 -.521 -.491 -.199 -.544 -.528 -.251 .699 -.637 -.907 -.838 -.717 .691 -.656 .753 -.701 -.693 .523 -.641 -.488 -.482 .607 -.511 .638 -.577 .61 -.497 -.444 -.367 -.375 -.297 -.254

.668 .422 .908 .901 .226 .918 .481 .444 .267 .74 .682 .307 .861 .672 -.747 -.537 .615 .691 -.5 .883 .848 -.486 .577 -.44 .567 .497 .798 .788 .681 .944 .938 .437 .692 .719 -.257 .38 .344

.679 .401 .906 .899 .228 .9 .476 .488 .257 .733 .681 .297 .903 .719 -.782 -.537 .689 .698 -.464 .897 .869 -.467 .57 -.434 -.598 .535 .803 .784 .672 .974 .976 .463 .685 .7 .25 .373 .331

****************************************************************************************** TABLE 8 .159 Summary of top Jaccard values for clustering of Model 8A

******************************************************************************************

SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2 )CLAN2 3)SHAMAN 4)ADULT 5)SUBADULT 6) SUBADULT MALE 7 )SUBADULTFEMALE 8 ) SUBADULT A VERA GE 9 ) SUBADULT POOR 10 )ADULT MALE 11 ) ADULT FEMALE 12)WEALTHY 13 ) WEAL THY FEMALE 14 ) WEALTHY MALE 15 )ADULT POOR 16 )ADULT POOR MALE 17)ADULTPOORFEMALE 18 )ADULT AVERAGE 19 )ADULT AVERAGE MALE 20 )ADULT AVERAGE FEMALE 21 )MALE 22)FEMALE 23 )AGED 40+ 24 ) AGED 40+ MALE 25 ) AGED 40+ FEMALE

.435@ 2 .418@ 5 .143@ 6 1@ 2 ** 1 @2** .871 @2 * 1@ 8 ** .857@ 8 * .8 @2* 1@ 3 ** 1@ 3 ** .724@ 6 * 1@ 12 ** 1@ 6 ** .432@ 13 .667@ 13 .351 @ 3 .492@2 .562@ 13 .478@ 9 .604@3 .804@3* .608@2 .613@ 3 .595@ 3

.435@ 2 .36@4 143@ 8 1@ 2 ** 1 @2** .871 @2 * .667@6 .2@2 .8 @2* 1@ 3 ** 1@ 3 ** .724@ 8 * 1 @7 ** 1@ 8 ** .285@ 2 .333@ 8 .448@7 .545@ 8 .667@ 8 .552@ 7 .604@3 .804@3* .608@2 .613@ 3 .595@ 3

.387@ 5 .36@6 .25@ 10 .715@ 2 * 1@ 3 ** .871@ 3 * .667@9 .2@ 3 .8 @3 * 1 @2** 1@ 3 ** .724@4 * 1@ 7 ** 1 @4 ** .282@4 .333 @4 .448@7 .545 @4 .667@4 .552@ 7 .604@2 .804@3* .496@2 .613@ 2 .595@ 3

********************************************************************************************** TABLE 8 .160 Summary of highest correlations in PCNCA analyses of Model 8A

**********************************************************************************************

1 CLANl 2 CLAN2 3SHAMAN 4 ADULT 5 SUBADULT

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.784 .784 -.584 -.969 .969

-.515 .515 -.157 -.971 .971

-.982 .982 .208 .838 -.838

.844 -.844 .662 .675 -.675

-.995 .995 .955 .902 -.902

.998 -.998 .968 .92 -.92

260

Appendix 1 *********************************************************************************************** TABLE 8.160 (Continued)

***********************************************************************************************

6 SUBADULT MALE 7 SUBADULTFEMALE 8 SUBADULTAVERAGE 9 SUBADULT POOR 10 ADULTMALE 11 ADULTFEMALE 12 WEALTHY 13 WEALTHY FEMALE 14 WEALTHY MALE 15 ADULTPOOR 16 ADULTPOORMALE 17 ADULTPOORFEMALE 18 ADULT AVERAGE 19 ADULT AVERAGE MALE 20 ADULTAVERAGEFEMALE 21 MALE 22 FEMALE 23 AGED 40+ 24 AGED 40+ MALE 25 AGED 40+ FEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

.861 .913 .725 .735 -.706 .947 .69 -.536 .56 -.382 -.325 .527 -.461 -.398 .566 -.851 .851 -.595 -.498 .766

.87 .416 .449 .776 -.845 .627 -.436 .408 -.407 -.261 -.349 .335 -.446 -.663 .373 -.718 .718 -.605 -.647 .509

-.787 -.18 -.332 -.702 .912 -.938 .644 -.527 .619 -.354 -.252 -.407 .464 .563 -.592 .832 -.832 .588 .721 -.724

-.616 .93 -.306 -.543 .83 .84 .599 .787 .744 -.336 -.303 -.422 -.596 -.525 .42 -.713 .713 .474 .649 .638

-.861 .953 -.439 -.708 .812 .922 .853 .933 .93 .26 .267 .537 .626 .631 .652 -.817 .817 .627 .658 .84

-.875 .959 -.445 -.724 .817 .936 .853 .957 .949 .243 .247 .518 .596 .595 .64 -.839 .839 .638 .657 .838

*************************************************************************************************** TABLE 8.161 Summary of top Jaccard values for clustering of Model 8B

*************************************************************************************************** SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )CLANl 2)CLAN2 3)LEADER 4 )ADULT 5 )SUBADULT 6 )AGED 40+ ?)MALE 8)FEMALE 9 )ADULT MALE 10 )ADULT FEMALE 11 )RANKl 12 )RANKl ADULT 13 )RANKl ADULT MALE 14)RANK1ADULTFEMALE 15 )RANKl SUBADULT 16 )RANKl SUBADULTMALE 17)RANK1SUBADULTFEMALE 18 ) RANKl WEALTHY 19 ) RANKl MALE WEAL THY 20 ) RANKl ADULT MALE WEAL THY 21 )RANKl SUBADULTMALEWEALTHY 22 ) RANKl FEMALE WEALTHY 23 ) RANKl ADULT FEMALE WEAL THY 24 )RANKl SUBADULTFEMALEWEALTHY 25)RANK1AVERAGE 26 ) RANKl MALE A VERA GE 27) RANKl ADULT MALE AVERAGE 28 )RANKl SUBADULTMALEAVERAGE 29) RANKl FEMALE A VERA GE 30 ) RANKl ADULT FEMALE A VERA GE 31 )RANKl SUBADULTFEMALEAVERAGE 32) RANKl MALE 33 ) RANKl FEMALE 34 )RANK2 35 )RANK2SUBADULT 36 )RANK2ADULT 37 )RANK2MALE 38 ) RANK2 FEMALE 39 ) RANK2 MALE ADULT 40)RANK2ADULTFEMALE 41)RANK2SUBADULTFEMALE 42 ) RANK2 SUBADUL T MALE 43)RANK2SUBADULTPOOR 44 )RANK2SUBADULT AVERAGE 45 ) RANK2 AGED 40+ 46 ) RANK2 AGED 40+ MALE 47 )RANK2AGED 40+FEMALE 48 ) RANK2 WEALTHY 49 ) RANK2 WEALTHY FEMALE 50 ) RANK2 WEALTHY MALE

.392@ 2 .366@ 2 .211@ 7 .491@ 2 .426@ 4 .356@ 2 .405@ 5 .471@ 3 .519@ 3 .631@ 4 1 @2 ** .67@2 .867@ 5 * .49@ 5 .645@ 12 .346@ 12 .44@ 12 .5@ 6 1@ 7 ** .684@7 .316@ 7 1 @6 ** .737@ 6 * .263@ 6 .596@2 .743@ 7 * 1@ 7 ** .45@ 12 .7 @6 * 1@ 12 ** .55@ 12 .833@ 5 * .816@ 5 * 1 @2 ** 1 @4 ** .783@ 2 * .737@ 3 * .837 @4 * 1@ 3 ** 1 @4 ** .75@ 11 * .652@ 4 .826 @4 * .25@ 11 .453@ 2 .548@ 3 .61 @4 .682@ 13 1 @ 15 ** 1@ 13 **

.392@ 2 .366@ 2 .211@ 9 .568@ 3 .426@ 3 .429@ 3 .409@ 8 .461@ 5 .519@ 5 .631@ 5 1 @2 ** .67@2 .667@ 15 .483@ 8 .33@ 2 .214@ 9 .219@ 13 .5@ 8 1 @9 ** .684@9 .316@ 9 1@ 8 ** .737@ 8 * .263@ 8 1 @9 ** .744@ 13 * 1@ 15 ** .231 @ 13 .63@ 13 .435@ 13 .286@ 15 .72@ 8 * .475@ 8 1 @2 ** 1@ 3 ** 1@ 3 ** .737@ 5 * .837@ 5 * 1@ 5 ** 1@ 5 ** 1 @4 ** 1 @4 ** .826@ 3 * .25@7 .578@ 3 .548@ 5 .61 @5 .682@ 14 1@ 12 ** 1@ 14 **

.392@ 2 .366@ 2 1@ 14 ** .491@ 2 .426@5 .356@ 2 .405 @4 .471@ 3 .519@ 3 .631@ 5 1 @2 ** .67@2 .867 @4 * .583@ 13 .645@ 10 .346@ 10 .44@ 10 .5@6 1@ 7 ** .684@ 7 .4@ 14 1 @6 ** 1 @ 13 ** 1@ 13 ** .596@2 .743@ 7 * 1@ 7 ** .45@ 10 .7 @6 * 1@ 10 ** .55@ 10 .833 @4 * .816@4 * 1 @2 ** 1@ 5 ** .783@ 2 * .737@ 3 * .837@ 5 * 1@ 3 ** 1@ 5 ** .429@ 12 .652@ 5 .826@ 5 * .174@ 5 .453@ 2 .548@ 3 .61@ 5 .5@ 15 .174@ 9 .733 @15*

261

Theoretical and Quantitative Approaches to the Study of Mortuary Practice ************************************************************************************************** TABLE 8.161 (Continued)

************************************************************************************************** SOCIAL GROUP

WARD

51)RANK2ADULTAVERAGE 52 ) RANK2 ADULT A VERA GE MALE 53 )RANK2ADULT AVERAGE FEMALE 54)RANK2ADULTPOOR 55 )RANK2ADULTPOORMALE 56)RANK2ADULTPOORFEMALE

AVERAGE

.377@ 2 .429@3 .537 @4 .258@ 10 .316@ 9 .364@ 10

.482@ 3 .429@5 .537@ 5 .258@ 10 .316@ 11 .364@ 10

DIVISIVE .377@ .429@ .537@ .24@5 .261@ .32@9

2 3 5 8

******************************************************************************************************* TABLE 8 .162 Summary of highest correlations in PCNCA analyses of Model 8B

*******************************************************************************************************

1 CLANl 2 CLAN2 3 LEADER 4 ADULT 5 SUBADULT 6 AGED 40+ 7 MALE 8 FEMALE 9 ADULTMALE 10 ADULTFEMALE 11 RANKl 12 RANKl ADULT 13 RANKlADULTMALE 14 RANKl ADULT FEMALE 15 RANKl SUBADULT 16 RANKl SUBADULTMALE 17 RANKl SUBADULTFEMALE 18 RANKl WEALTHY 19 RANKl MALE WEALTHY 20 RANKl ADULT MALE WEAL THY 21 RANKl SUBADMALEWEALTHY 22 RANKl FEMALE WEALTHY 23 RANKl ADULT FEMALE WEAL THY 24 RANKl SUBADFEMALEWEALTHY 25 RANKl AVERAGE 26 RANKl MALE AVERAGE 27 RANKl ADULT MALE A VERA GE 28 RANKl SUBAD MALE AVERAGE 29 RANKl FEMALE AVERAGE 30 RANKlADULTFEMALEAVERAGE 31RANK1SUBADFEMALEAVERAGE 32 RANKl MALE 33 RANKl FEMALE 34 RANK2 35 RANK2 SUBADULT 36 RANK2ADULT 37 RANK2 MALE 38 RANK2 FEMALE 39 RANK2MALEADULT 40 RANK2 ADULT FEMALE 41 RANK2SUBADULTFEMALE 42 RANK2SUBADULTMALE 43 RANK2SUBADULTPOOR 44 RANK2SUBADULT AVERAGE 45 RANK2 AGED 40+ 46 RANK2 AGED 40+ MALE 47 RANK2 AGED 40+ FEMALE 48 RANK2 WEAL THY 49 RANK2 WEALTHY FEMALE 50 RANK2 WEALTHY MALE 51 RANK2ADULTAVERAGE 52 RANK2ADULT AVERAGE MALE 53 RANK2ADULT AVERAGE FEMALE 54 RANK2ADULTPOOR 55 RANK2ADULTPOORMALE 56 RANK2ADULTPOORFEMALE

ORD CA

DET CA

PCA

cov

PCA COR

VAR PCA

OBL PCA

-.65 .65 .584 -.567 .567 -.278 -.628 .628 -.521 .707 -.948 -.704 .536 -.783 -.404 -.276 -.271 .658 .519 .539 -.281 -.718 -.71 -.188 -.751 -.582 -.474 -.298 -.379 -.336 -.354 -.629 -.718 .948 .926 .932 -.706 .72 -.64 .818 .906 .893 .73 .659 .64 -.481 .669 .435 .514 -.469 .543 -.401 .547 .344 .275 .303

.344 -.344 -.167 -.371 .371 .219 -.562 .562 -.48 .616 -.966 -.716 -.61 -.382 -.414 -.282 -.279 -.523 -.363 -.3 -.191 .447 .445 -.158 -.735 -.72 -.643 -.277 -.33 -.23 -.253 -.711 -.5 .966 .366 .89 -.642 .786 -.705 .821 .11 .372 .307 .317 .607 -.474 .652 .399 .406 -.485 .52 -.375 .533 .345 -.25 .38

.792 -.792 .393 .489 -.489 -.317 .775 -.775 .815 -.759 .966 .755 .621 .564 .363 .283 .209 .776 .709 .641 .292 .497 .512 .161 -.717 .529 .475 -.305 -.481 -.33 -.331 .722 .563 -.966 -.663 -.883 -.584 -.61 .704 -.624 .354 -.588 -.584 -.288 -.612 .572 -.52 -.404 -.281 .554 -.526 .393 -.462 -.322 -.214 -.235

-.848 .848 .583 .475 -.475 .288 .538 -.538 .56 -.529 .93 .769 .629 -.737 .296 .241 .16 .599 .527 .563 .324 -.654 -.664 -.142 -.637 -.609 -.547 -.229 -.409 -.363 -.208 .704 -.702 -.93 -.749 -.862 -.575 -.688 .684 -.714 .637 -.665 -.586 -.479 -.612 .558 -.6 .495 .636 .629 -.489 -.461 -.511 -.276 -.275 -.237

.988 -.988 .976 .499 -.499 .309 .575 -.575 .513 -.695 .862 .666 .659 .874 .333 .271 .181 .73 .831 .724 .383 .879 .899 .181 .784 .692 .631 .245 -.467 -.394 -.251 .749 .759 -.862 -.743 -.784 -.628 -.758 -.633 -.857 -.933 -.875 -.515 -.613 -.581 -.531 -.793 .791 .973 .957 -.542 -.499 -.758 -.324 -.285 -.305

-.988 .988 .994 .42 -.42 -.29 .527 -.527 .413 -.683 .69 .531 .486 -.872 .269 .206 .159 .766 .89 .797 .379 -.874 -.894 -.181 .567 .481 .43 .183 -.399 -.34 -.211 .536 -.758 -.69 -.737 -.774 -.748 -.835 -.871 -.932 .879 -.865 -.525 -.581 -.583 -.757 -.825 .802 .995 .988 -.531 -.662 -.772 -.275 -.339 -.347

********************************************************************************* TABLE 8 .163 Summary of top Jaccard values for clustering of Ramsauer (random seed 1)

********************************************************************************* SOCIAL GROUP

WARD

AVERAGE

DIVISIVE

1 )RANKl 2) RANKl POOR 3)RANK1AVERAGE 4 ) RANK 1 WEALTHY 5 )RANK2 6)RANK2AVERAGE 7 ) RANK2 WEALTHY

.339@ 2 .2@ 10 .381 @ 10 .32@ 2 .357@ 3 .333@ 8 .218@ 4

.474@2 .294@ 12 .4@ 12 576@ 2 .375 @4 .452@ 10 .202@4

.532@ 2 .296@ 8 .25@ 8 .526@ 3 .424@2 .31 @ 10 .349@ 8

262

Appendix 1 ******************************************************************************** TABLE 8.163 (Continued)

******************************************************************************** SOCIAL GROUP

WARD

AVERAGE

.5@5 .366@ 5

8 )RANK3 9)RANK4

.786@ 13 * .533@ 13

DIVISIVE .667@ 6 .533@ 6

********************************************************************************* TABLE 8.164 Summary of top Jaccard values for clustering of Rarnsauer (random seed 2)

********************************************************************************* SOCIAL GROUP 1 )RANKl 2 ) RANKl 3 ) RANKl 4 ) RANKl 5 )RANK2 6 ) RANK2 7 ) RANK2 8 )RANK3 9)RANK4

POOR A VERA GE WEAL THY A VERA GE WEAL THY

WARD .327@ 2 .222@ 15 .333@ 4 .412@ 7 .364@ 2 .297@4 .259@ 7 .583@ 5 .267@ 5

AVERAGE

DIVISIVE

.399@ 2 .176@ 11 .25@7 .42@ 10 .423@ 7 .354@ 7 .3@ 10 .621@ 13 .75@ 13 *

.363@ 2 .31 @ 12 .525@ 7 .451 @ 11 .325@ 7 .447@ 13 .208@ 2 .281@ 3 .3@ 10

********************************************************************************* TABLE 8.165 Summary of top Jaccard values for clustering of Rarnsauer (random seed 3)

********************************************************************************* SOCIAL GROUP 1 )RANKl 2 ) RANKl POOR 3 )RANKl AVERAGE 4 ) RANKl WEAL THY 5 )RANK2 6)RANK2AVERAGE 7 ) RANK2 WEAL THY 8 )RANK3 9)RANK4

WARD .331@ .429@ .226@ .354@ .348@ .263@ .237@ .444@ .353@

AVERAGE 2 7 2 3 4 10 15 4 4

.342@ 6 .238@ 10 .25@ 10 .5@9 .358@ 4 .255@ 13 .22@4 .561@ 11 .333@ 6

DIVISIVE .484@ .387@ .308@ .422@ .422@ .225@ .27@8 .442@ .385@

2 12 12 3 2 11 2 9

********************************************************************************* TABLE 8.166 Summary of top Jaccard values for clustering of Rarnsauer (random seed 4)

********************************************************************************* SOCIAL GROUP 1 )RANKl 2) RANKl 3 ) RANKl 4 ) RANKl 5 )RANK2 6 ) RANK2 7 ) RANK2 8 )RANK3 9)RANK4

POOR A VERA GE WEAL THY A VERA GE WEAL THY

WARD .479@ .375@ .214@ .453@ .348@ .321@ .32@8 .538@ .382@

3 8 13 3 8 11 3 3

AVERAGE

DIVISIVE

.434@ 2 .111@11 .333@ 13 .522@2 .391@ 4 .268@ 12 .333@ 14 .485@ 15 .414@ 4

.476@ 3 .19@ 13 .333@ 13 .436@ 3 .346@ 2 .286@ 12 .333@ 6 .484@ 3 .188@ 9

******************************************************************************** TABLE 8.167 Summary of top Jaccard values for clustering of Rarnsauer (random seed 5)

******************************************************************************** SOCIAL GROUP

WARD

AVERAGE

1 )RANKl 2) RANKl POOR 3 )RANKl AVERAGE 4 ) RANKl WEAL THY 5 )RANK2 6)RANK2AVERAGE 7 ) RANK2 WEAL THY 8 )RANK3 9)RANK4

.357@ 2 .375@ 6 .386@ 6 .565@ 4 .357@ 3 .314@ 10 .321@ 12 .568@ 3 .556@ 14

.373@ 2 .224@ 10 .31 @6 .538@ 2 .373@ 5 .286@ 15 .286@ 6 .556@ 13 .643@ 13

DIVISIVE .383@ .452@ .324@ .491@ .328@ .206@ .226@ .385@ .45@3

3 10 7 9 3 10 3 4

********************************************************************************* TABLE 8.168 Summary of average Jaccard values for clustering of Rarnsauer

********************************************************************************* SOCIAL GROUP 1 )RANKl 2 ) RANKl POOR 3 )RANKl AVERAGE 4 ) RANKl WEAL THY 5 )RANK2 6)RANK2AVERAGE 7 ) RANK2 WEAL THY 8 )RANK3 9)RANK4

WARD .367 .32 .308 .421 .355 .306 .271 .527 .385

AVERAGE .404 .209 .309 .511 .384 .323 .268 .602 .535

263

DIVISIVE .448 .327 .348 .465 .369 .295 .277 .452 .371

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

***************************************************************************************** TABLE 8 .169 Highest correlations on axes for correspondence analysis of Ramsauer (repetition 1)

***************************************************************************************** Positive

Negative

AXIS 1 ( 10.44 %) WEALTHY RANKl

-.592

RANK4

-.643

AVERAGE RANK2 RANK2

-.193 -.151

.163 .141 .101

WEALTHY RANKl RANKl

-.181 -.11

.27 .215

RANK3 AVERAGE RANKl

-.234 -.231

RANK2 WEALTHY RANK2

-.281 -.229

AVERAGE RANK2

-.303

AVERAGE RANK2

-.344

AVERAGE RANK2 RANK2

-.117 -.114

AVERAGE RANK2 RANK2

-.265 -.21

AXIS 2 ( 9.15 %) RANKl

.526

AXIS 3 ( 7.91 %)

AXIS 4 ( 7.06 %) AVERAGE RANKl RANK2 AVERAGE RANK2 AXIS 5 ( 6.44 %) AVERAGE RANK2 RANK2 AXIS 6 ( 6.28 %)

AXIS 7 ( 5.99 %) RANK3

.409

AXIS 8 ( 5.35 %)

AXIS 9 ( 5.15 %)

AXIS 10 ( 4.83 %)

AXIS 11 ( 4.61 %)

AXIS 12 ( 4.11 %) POORRANKl

.12

AXIS 13 ( 4.02 %) POORRANKl

.335

***************************************************************************************** TABLE 8.170 Highest correlations on axes for Oblimin-rotated PCA of Ramsauer (repetetion 1)

***************************************************************************************** Positive

Negative

AXIS 1 ( 16.6 %) RANK4

.829

AXIS 2 ( 9.4 %) RANK3

.795

AXIS 3 ( 8.7 %) AVERAGE RANKl

.314

WEALTHY RANKl

264

-.38

Appendix 1

****************************************************************************************** TABLE 8.170 (Continued)

****************************************************************************************** Positive

Negative

AXIS 4 ( 5.8 %) WEALTHY RANK2

.406

AXIS 5 ( 5.3 %) RANK4

.458

AXIS 6 ( 4.8 %) AVERA GE RANKl

-.114

.179

AVERA GE RANKl AVERAGE RANK2 POORRANKl

-.154 -.152 -.143

.101

POORRANKl WEALTHY RANKl

-.118 -.105

.369

RANK2 AVERAGE RANK2

-.333 -.313

AXIS 7 ( 4.6 %) WEALTHY RANKl

AXIS 8 ( 4.5 %) WEALTHY RANK2 AXIS 9 ( 4.3 %) RANKl

*************************************************************************************** TABLE 8.171a Summary of highest correlations in PCNCA analyses ofRamsauer

*************************************************************************************** Repetition

RANKl 2 RANKIPOOR 3 RANKlAVERAGE 4 RANKl WEALTHY 5 RANK2 6 RANK2AVERAGE 7 RANK2 WEALTHY 8 RANK3 9 RANK4

.526 -.426 .335 -.143 .477 .314 -.592 -.38 -.281 -.333 -.344 -.313 -.229 .406 -.47 .795 -.643 .829

Analysis

2

3

4

.588 .505 -.216 .184 -.434 .401 .523 -.354 .364 -.451 .305 -.382 .171 -.18 -.624 .821 -.466 .695

-.47 -.556 .389 .222 .511 .347 .526 -.426 .348 -.325 -.166 -.18 .281 .362 .336 .635 -.436 .719

-.345 .389 .197 -.096 .353 .256 -.621 -.425 .326 -.336 -.228 -.26 .254 .213 .478 .756 .539 -.789

5 -.411 -.494 -.364 .199 .511 .406 -.615 -.424 .427 .447 .206 .312 .366 .292 .493 .738 -.389 .858

CA PCA CA PCA CA PCA CA PCA CA PCA CA PCA CA PCA CA PCA CA PCA

************************************************************************* TABLE 8.171b Average point-biserial correlations in PCNCA analyses ofRamsauer

************************************************************************* CA RANKl

PCA

.468

.474

2 RANKlPOOR

.3

.169

3 RANKlAVERAGE

.457

.345

4 RANKl WEALTHY

.575

.402

5 RANK2

.349

.378

6 RANK2AVERAGE

.25

.289

7 RANK2 WEALTHY

.26

.291

8 RANK3

.48

.749

9 RANK4

.495

.778

265

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

**************************************************************************************** Table 8.172 Number of high Jaccard values with different clustering methods across all models (values of 0.9 or more, values of 0.7 or more)

**************************************************************************************** Ward's method 0.9+ 0.7+

Average Linkage 0.9+ 0.7+

Monothetic div. 0.9+ 0.7+

Model lA Model 2A Model 3A Model4A Model4B Model4C Model4D Model SA Model SB Model 6A Model 7A Model 7B Model 7C Model 7D Model 8A Model 8B

7 13 14 2 0 5 0 3 7 10 8 7 10 9 7 11

7 25 24 7 I 15 1 5 14 22 9 9 22 21 12 22

7 12 13 1 I 5 0 I 7 10 6 6 8 6 6 14

7 22 22 5 2 15 2 5 11 21 7 7 17 13 10 20

8 11 14 0 I 0 0 4 1 16 6 7 11 13 5 12

9 24 24 1 I 4 0 8 8 24 7 9 24 21 10 22

Totals

113

216

103

186

109

196

Preliminary model

Ward's method 0.9+ 0.7+

Average Linkage 0.9+ 0.7+

Monothetic div. 0.9+ 0.7+

50 burials 100 burials 200 burials 400 burials

4 0 1 0

17 11 11 10

7 3 0 0

16 10 8 8

4 0 0 1

9 4 8 8

Totals

5

49

10

42

5

29

********************************************************************************************************************** Table 8.173 Highest point-biserial correlations with different methods across all models (values of 0.8 or above, values of 0.6 or above)

************************************************************************************************************************ Ordinary Correspond 0.8+ 0.6+

Detrended Correspond 0.8+ 0.6+

Unrotated PCA (cov.) 0.8+ 0.6+

Unrotated PCA (cor.) 0.8+ 0.6+

Orthogonal PCA 0.8+ 0.6+

Oblique PCA 0.8+

0.6+

MODEL IA MODEL2A MODEL3A MODEL4A MODEL4B MODEL4C MODEL4D MODEL SA MODEL SB MODEL6A MODEL7A MODEL7B MODEL7C MODEL7D MODEL 8A MODEL8B

3 5 9 2 0 3 2 2 2 4 3 4 3 4 5 7

14 27 20 9 2 17 9 10 12 22 13 14 25 26 14 27

5 2 6 2 0 3 2 2 2 2 3 0 2 2 4 4

9 15 18 3 3 10 5 4 6 12 13 12 13 13 11 16

4 7 4 2 0 3 0 2 2 2 4 4 6 3 8 4

17 26 18 10 3 25 10 10 18 15 14 11 22 20 14 21

3 5 5 I 1 3 0 0 2 3 2 3 3 3 5 5

17 28 19 10 4 26 8 10 15 20 17 13 27 29 15 24

9 13 14 6 2 7 I 2 5 15 10 10 12 12 15 14

18 35 29 15 4 32 16 10 23 32 18 18 34 31 21 33

9 16 14 6 2 9 I 2 5 16 8 9 16 12 15 16

17 37 32 15 5 34 17 13 26 34 14 17 34 36 19 28

Totals

58

261

41

163

55

254

44

282

147

369

156

378

Preliminary model

Ordinary Correspond

Oblique PCA

0.8+

0.6+

0.8+

0.6+

50 burials 100 burials 200 burials 400 burials

8 0 4 10

27 24 26 31

10 8 6 8

49 34 48 53

Totals

22

108

32

184

266

Appendix 1

*********************************************************************************************** TABLE 8.174

Highest Jaccard values across all egalitarian models (-:0.7 or higher;*: 0.9 or higher)

***********************************************************************************************

ll. ::::)

0

a:

C,

...J c(

u

0

en

w Cl) => => Cl) 13 AND ELIMINATE(X%) = 0 THEN ELIMINATE(X%) = 1 NEWCASES = NEWCASES - 1 END IF END SELECT

NEXT X% PRINT #3 "********************************"

PRINT #3'. ELIMINATE$; "have been eliminated" PRINT ELIMINATE$;" have been eliminated" PRINT #3, "********************************" PRINT#3, "" PRINT #3, "There are now"; NEWCASES; "burials" PRINT "There are now"; NEWCASES; "burials" PRINT#3, "" END IF DESCRIPTION$ = DESCRIPTION$ + "/ELIMINATION=" + ELIMINATE$+ "/CASES=" + STR$(NEWCASES) END SUB

****************** SUB GENDERCAT

PRINT #3, "Saving social identity info for clustering ... " PRINT "Saving social identity info for clustering ... ";"" PRINT"" PRINT #2, "IDENTITY-LABELS" REM Next bit prints the identity labels in the output file, for later use FOR Y% = 1 TO NUMID SELECT CASE IDENTITY(Y%) CASE"AGE " FOR A%= 1 TO NUMAGEGROUPS WRITE #2, RTRIM$(AGEGROUP(A%).LABEL) NEXT A% CASE "WEALTH " WRITE #2, "WEALTHY", "AVERAGE", "POOR" CASE"SEX " WRITE #2, "MALE", "FEMALE" CASE"CLAN " FOR C% = 1 TO NUMCLAN CONVERT$= "CLAN"+ LTRIM$(STR$(C%)) WRITE #2, CONVERT$ NEXTC% CASE "LEADER " WRITE #2, "LEADER" CASE"RANK " FOR R % = I TO NUMRANKS CONVERT$= "RANK"+ LTRIM$(STR$(R%)) WRITE #2, CONVERT$ NEXTR% CASE "CHOICE " FOR C% = 1 TO NUMCHOICE CONVERT$= "CHOICE"+ LTRIM$(STR$(C%)) WRITE #2, CONVERT$ NEXTC% CASE "MARITAL " WRITE #2, "SINGLE", "MARRIED" CASE "PERIOD " FOR P% = 1 TO NUMPERIODS CONVERT$= "PERIOD"+ LTRIM$(STR$(P%)) WRITE #2, CONVERT$ NEXTP% CASE ELSE PRINT #3, "ERROR RELATING TO IDENTITY"; IDENTITY(Y%) STOP END SELECT NEXT Y% REM Next identity WRITE #2, "END" REM Indicates end of identities in the output file

****************** REM Allocate sex to each burial

REM Next bit prints in output file the value "1" for each REM identity that a burial has

MALES =0 FEMALES=0

FOR X% = 1 TO CASES PRINT#2,X%;" ";

INPUT #1, PROBMALE REM Read from data file the probability of being male

FOR Y% = 1 TO NUMID SELECT CASE IDENTITY(Y%) CASE"AGE " FOR A% = 1 TO NUMAGEGROUPS IF AGE(X%).VALUE >= AGEGROUP(A%).LOWER AND AGE(X%).VALUE LOWERAGE AND WEALTH(X%).VALUE = 1 THEN

RULENUMBER = RULENUMBER + 1 REM Records the number of rules

IF SOCTYPE$ = "RANKED" AND RANK(X%).VALUE 1 THEN REM Ignore, since not top rank ELSE PROBABILITY = INT(RND * 100) + 1

PRINT#3, 1111 PRINT #3, "***************" PRINT #3, "Rule number"; RULENUMBER PRINT #3 "***************" PRINT #8: "Rule"; RULENUMBER; CHR$(9);

IF PROBABILITY 3 REM Look through the rule text, until all has been looked at

DIM PERIODSIZE(l TO NUMPERIODS) REM Records the number of burials in each period

LENGTH = LEN(RULESTRING$) DIVIDER = INSTR(RULESTRING$, "/") REM Locate slash sign, which divides the rule into its parameters

FOR P% = 1 TO NUMPERIODS INPUT #1, DURATION(P%).VALUE REM Reads the probability of a burial being in each period TOTAL= TOTAL+ DURATION(P%).VALUE PRINT #3, "Duration of period"; P%; "is"; DURATION(P%).VALUE;

RULEBIT$ = LEFT$(RULESTRING$, DIVIDER - 1) REM The bit of the rule before the slash

"%" NEXTP%

RULESTRING$ = RIGHT$(RULESTRING$, REM The remaining bit of the rule

LENGTH - DIVIDER) IF TOTAL 100 THEN PRINT "WARNING! PERIOD LENGTHS ARE INCORRECT" STOP END IF

BITOP = INSTR(RULEBIT$, "=") REM Look for the operator in the part of the rule being examined IF BITOP 0 THEN RULEPARAM(RULENUMBER, PARAMNUMBER).OPERATOR = "=" REM Indicates the operator is an equal sign IF BITOP = 0 THEN REM Not an equal sign, so could be> sign BITOP = INSTR(RULEBIT$, ">") RULEPARAM(RULENUMBER, PARAMNUMBER).OPERATOR END IF IF BITOP = 0 THEN REM If value is still O, then must be < sign BITOP = INSTR(RULEBIT$, ""

FOR X% = 1 TO CASES PROBABILITY = INT(RND * 100) + 1 REM Used to allocate burial to period LOWERLIMIT = 0 FOR P% = 1 TO NUMPERIODS IF PROBABILITY > LOWERLIMIT AND PROBABILITY 0 THEN PRINT #3, "PERIOD"+ LTRIM$(STR$(PERIOD(X%).V ALUE)); " "; END IF

PRINT "Total burials with artefacts:"; NEWCASES PRINT #8, "Total burials with artefacts:"; NEWCASES PRINT "Press any key ..."

IF CHOICE(X%).VALUE > 0 THEN PRINT #3, "CHOICE"+ LTRIM$(STR$(CHOICE(X%).V ALUE)); " "; END IF

INPUT KEY$

PRINT#3, ""

END SUB

NEXT X%

***********************

REM Output of rules applying to each burial

SUB SOCIALSUMMARY

*********************** REM Note data written to rules file; point at which this procedure REM is called is thus important REM Data written is: general identites, rule data and artefact data PRINT#3, "" PRINT#3, "****************************" PRINT #3, "SUMMARY OF SOCIAL IDENTITIES" PRINT#3, "****************************" PRINT#3, "" PRINT #2, "GENERAL-IDENTITY" FOR X% = 1 TO CASES PRINT#2,X%;" PRINT#3,X%;"

"; ";

FOR Y% = 1 TO NUMID SELECT CASE IDENTITY(Y%) CASE"AGE " PRINT#2,AGE(X%).VALUE;" "; CASE "WEALTH " PRINT #2, WEALTH(X%).V ALUE; " "; CASE "SEX " PRINT#2,SEX(X%).VALUE;" "; CASE"CLAN " PRINT#2,CLAN(X%).VALUE;" "; CASE "LEADER " PRINT#2,LEADER(X%).VALUE;" "; CASE"RANK " PRINT #2, RANK(X%).V ALUE; " "; CASE "MARITAL " PRINT#2,MARITAL(X%).VALUE;" "; CASE "PERIOD " PRINT#2,PERIOD(X%).VALUE;" "; CASE "CHOICE " PRINT#2,CHOICE(X%).VALUE;" "; CASE ELSE PRINT #3, "ERROR RELATING TO IDENTITY"; IDENTITY(Y%) STOP END SELECT NEXT Y% PRINT#2, "" IF AGE(X%).VALUE > 13 THEN PRINT #3, "ADULT"; ELSE PRINT #3, "SUBADULT "; END IF IF SEX(X%).VALUE = 0 THEN PRINT#3, "MALE"; ELSE PRINT #3, "FEMALE"; END IF IF RANK(X%).VALUE > 0 THEN PRINT #3, "RANK"+ LTRIM$(STR$(RANK(X%).V ALUE)); " "; END IF IF WEALTH(X%).VALUE = 1 THEN

PRINT #2, "RULEDATA" FOR X% = 1 TO CASES PRINT#2,X%;" "; FOR RULENUM = 1 TO RULENUMBER PRINT #2, RULES(X%, RULENUM);" "; NEXT RULENUM PRINT#2, "" NEXT X% REM Output of artefact data PRINT #2, "ARTEFACTDATA" FOR X% = 1 TO CASES PRINT#2,X%;" "; FOR ARTEFACT= 1 TO NUMDIM PRINT#2,ASSEMB(X%,ARTEFACT);" NEXT ARTEFACT PRINT#2, "" NEXT X%

";

END SUB

**************** SUBSPSSOUT

**************** REM Outputs data for SPSS analysis;data is output in inline form in REM text of SPSS program that performs PCA PRINT #3, "SPSS output will go to file"; SPSS$ PRINT "SPSS outout will go to file"; SPSS$ OPEN SPSS$ FOR OUTPUT AS #6 PRINT"" PRINT "Saving data for SPSS ...please wait" PRINT #3, "Saving"; NUMDIM; "variables to"; SPSS$;" for SPSS" PRINT #3, NEWCASES; "burials are being saved" PRINT #6, "COMMENT"; DESCRIPTION$ PRINT #6, "SET LENGTH=NONE" REM Sets page length, so doesn't break up output PRINT #6, "DATA LIST FREE" PRINT #6, "I"; PRINT #6, "CASE"; REM Case number FOR K% = 1 TO NUMDIM PRINT #6," "; DIMENSION(K%) NEXT K% PRINT#6, "" PRINT #6, "BEGIN DATA" COUNTBURIALS% = 0 FOR X% = 1 TO CASES IF ELIMINATE(X%) 1 THEN COUNTBURIALS% = COUNTBURIALS% + 1 PRINT#6,X%;" "; FOR Y% = 1 TO NUMDIM PRINT #6, ASSEMB(X%, Y%);" ";

279

Theoretical and Quantitative Approaches to the Study of Mortuary Practice IF Y% = 35 THEN PRINT #6, "" REM Start a new line NEXT Y% PRINT#6, "" END IF NEXT X% PRINT #6, "END DATA" PRINT#6, "" PRINT #6, "LIST VARIABLES=CASE" REM Used to indicate burial numbers of those selected PRINT#6, "FACTOR VARIABLES="; FOR K% = 1 TO NUMDIM PRINT #6," "; DIMENSION(K%) NEXT K% PRINT #6, " /EXTRACTION=PC" PRINT #6," /ROTATION=NOROTATE" PRINT#6," /SAVE REG (ALL PC)" PRINT #6, "COMPUTE ENDV AR=l" PRINT #6, "LIST VARIABLES=PCl TO ENDV AR" PRINT#6, "" PRINT#6, "FACTOR VARIABLES="; FOR K% = 1 TO NUMDIM PRINT #6," "; DIMENSION(K%) NEXT K% PRINT #6, " /EXTRACTION=PC" PRINT #6," /ROTATION=V ARIMAX" PRINT #6," /SAVE REG (ALL ROTPC)" PRINT #6, "COMPUTE ENDV AR2=1" PRINT #6, "LIST VARIABLES=ROTPCl TO ENDV AR2" PRINT#6, "" PRINT#6, "FACTOR VARIABLES="; FOR K% = 1 TO NUMDIM PRINT #6," "; DIMENSION(K%) NEXT K% PRINT #6, "/CRITERIA=ITERATE (50)" PRINT #6, " /EXTRACTION=PC" PRINT #6," /ROTATION=OBLIMIN" PRINT #6," /SAVE REG (ALL OBPC)" PRINT #6, "COMPUTE ENDV AR3=1" PRINT#6, "LISTVARIABLES=OBPCl TOENDVAR3" PRINT COUNTBURIALS%; " burials were saved in SPSS file" PRINT #3, COUNTBURIALS%; "burials were saved in SPSS file" END SUB

************* SUB TABLE

************* REM Produces a summary of goods with each burial DIM FULLLABEL(l TO NUMDIM) AS STRING FOR X = 1 TO NUMDIM SELECT CASE DIMENSION(X) CASE "TUSK " FULLLABEL(X) = "Boar tusk" CASE "CPRING " FULLLABEL(X) = "Copper ring" CASE "FIGURINE " FULLLABEL(X) = "Clay figurine" CASE "COPPAWL " FULLLABEL(X) = "Copper awl" CASE"CPBRCLT" FULLLABEL(X) = "Copper bracelet" CASE "COPPAXE " FULLLABEL(X) = "Copper axe" CASE "DECPOT " FULLLABEL(X) = "Decorated pot" CASE "CPPLATE " FULLLABEL(X) = "Copper plate" CASE "COPPERPN " FULLLABEL(X) = "Copper pin" CASE"MACE " FULLLABEL(X) = "Polished stone mace" CASE "CPBLADE " FULLLABEL(X) = "Copper blade" CASE "OCHRE " FULLLABEL(X) = "Ochre" CASE "CPBEAD " FULLLABEL(X) = "Copper bead" CASE "CPSPIRAL " FULLLABEL(X) = "Copper spiral" CASE "JUG " FULLLABEL(X) = "Pottery jug" CASE"CUP " FULLLABEL(X) = "Pottery cup"

CASE "PLAINPOT " FULLLABEL(X) = "Plain pot" CASE "BONEPIN " FULLLABEL(X) = "Bone pin" CASE "BLADE " FULLLABEL(X) = "Flint blade" CASE "STONEAXE " FULLLABEL(X) = "Stone axe" CASE "ARROW " FULLLABEL(X) = "Flint arrowhead" CASE "MANDIBLE " FULLLABEL(X) = "Pig mandible" CASE "TEETH " FULLLABEL(X) = "Perforated teeth" CASE "ANIMAL " FULLLABEL(X) = "Animal bones" CASE "SCRAPER " FULLLABEL(X) = "Flint scraper" CASE "BONEAWL " FULLLABEL(X) = "Bone awl" CASE "SHBRCLT " FULLLABEL(X) = "Shell bracelet" CASE "BNRING " FULLLABEL(X) = "Bone ring" CASE "SHELLS " FULLLABEL(X) = "Shells" CASE"BOWL " FULLLABEL(X) = "Pottery bowl" CASE "PENDANTA " FULLLABEL(X) = "Bone pendant (type A)" CASE "PENDANTB " FULLLABEL(X) = "Bone pendant (type B)" CASE "STBEAD " FULLLABEL(X) = "Stone bead" CASE "SHBEAD " FULLLABEL(X) = "Shell bead" CASE "CHARCOAL " FULLLABEL(X) = "Charcoal" CASE ELSE FULLLABEL(X) = DIMENSION(X) END SELECT NEXTX

PRINT#8 "*************************************************" PRINT #8: "SUMMARY OF ASSEMBLAGES IN "; N$ PRINT #8,

"*************************************************"

PRINT#8, "" FOR BURIAL= 1 TO CASES PRINT #8, "Burial"; BURIAL;"/"; PRINT #8, "Aged"; AGE(BURIAL).V ALUE; "/"; SELECT CASE SEX(BURIAL).V ALUE CASE0 PRINT #8, "Male/"; CASEl PRINT #8, "Female/"; END SELECT PRINT#8, "Rank"; RANK(BURIAL).VALUE; "/"; PRINT #8, "Wealth level"; WEALTH(BURIAL).VALUE; "/"; PRINT#8, "Clan"; CLAN(BURIAL).VALUE; "/"; IF LEADER(BURIAL).V ALUE = 1 THEN PRINT #8, "Leader identity"; IF CHOICE(BURIAL).V ALUE > 0 THEN PRINT #8, "Choice"; CHOICE(BURIAL).VALUE; PRINT#8,"" PRINT #8, "ASSEMBLAGE: "; FOR GOOD = 1 TO NUMDIM IF ASSEMB(BURIAL, GOOD)= "1" THEN PRINT #8, FULLLABEL(GOOD); "; "; END IF NEXT GOOD PRINT#8, "" PRINT#8, "" NEXT BURIAL END SUB

****************** SUB WEALTHCAT

****************** REM Allocates burials to wealth categories and/or ranks WEALTHY =0 AVERAGE= 0 POOR=0 IF AGEDEFINED = 0 THEN PRINT "WARNING! AGE NOT DEFINED YET" STOP END IF

280

Appendix 2 INPUT #1, SOCTYPE$ SELECT CASE SOCTYPE$

AVERAGE=0 POOR=0 RANKTOTAL = 0

CASE "EGALITARIAN" PRINT #3," EGALITARIAN SOCIETY DEFINED BY RULES" INPUT #1, NUMAGES PRINT#3, "" PRINT #3," Wealth is defined across"; NUMAGES;" age divsions" PRINT#3, ""

INPUT #1, RANKPROB, AGEGROUPS PRINT #3, "Rank"; K%; "has a"; RANKPROB; "% probability" PRINT #3, "Wealth is defined across"; AGEGROUPS; "age categories:"

FOR X% = 1 TO NUMAGES INPUT #1, LOWER, UPPER, WEALTHPROB, AVPROB, POORPROB PRINT #3, "For age category "; LOWER; " to "; UPPER; " probabilities are:" PRINT #3, "WEALTHY:"; WEALTHPROB PRINT #3, "AVERAGE:"; A VPROB PRINT #3, "POOR:"; POORPROB FOR CASENUM% = 1 TO CASES IF AGE(CASENUM%).VALUE >= LOWER AND AGE(CASENUM%).VALUE HIJACCARD THEN HIJACCARD = JACC PRINT #3, JACC PRINT#3, "" NEXT X% PRINT #3, "Highest initialjaccard value is"; HIJACCARD PRINT#3, "" PRINT "Highest initial jaccard value is "; HIJACCARD PRINT"" REM Next section eliminates social identities which could not REM exceed this initial highest value (i.e. their percentage REM occurrence in the cluster falls below this value) PRINT "Checking clusters for prominent identities ..." PRINT PRINT

****************************

11

11

PRINT "Check of identity occurrence" PRINT "****************************" PRINT"" FOR X% = 1 TO NUMID IGNORE(X%) = 0 NEXT X% REM Reset ignore indicators to zero

ELIMINATED = 0 REM Records number of social identities eliminated NEWNUMID = 0 REM New number of social identities FOR Y = 1 TO NUMID REM Look at each social identity INCLUS =0 REM records occurrence of social identity in current cluster FOR X = 1 TO CASES IF CLUSMEM(X, LEVEL) = COUNT AND ID(X, Y) = 1 THEN REM This burial is in the current cluster and has this identity INCLUS = INCLUS + I REM Counts occurrence of identity Y in the cluster END IF NEXTX IF INCLUS > CLUSSIZE(LEVEL, COUNT) THEN PRINT "WARNING! PROBLEM WITH IDENTITY"; IDLABEL(Y) STOP END IF REM Error trapping PRINTY;""; RTRIM$(IDLABEL(Y)); CHR$(9); LEVEL; CHR$(9); COUNT; CHR$(9); PRINT"("; (INCLUS / (CLUSSIZE(LEVEL, COUNT)))* 100; "%)" IDPERCENT(Y) = INCLUS / (CLUSSIZE(LEVEL, COUNT)) REM Percentage occurrence of the identity in the cluster REM 0.01 subtracted from hijaccard because of rounding problem IF INCLUS / (CLUSSIZE(LEVEL, COUNT)) < (HIJACCARD - .01) THEN IGNORE(Y) = 1 PRINT "IGNORED" ELIMINATED = ELIMINATED + 1 END IF

TEXTLINE$ = "" DO WHILE LEFT$(TEXTLINE$, 17) "RAW LINE INPUT #1, TEXTLINE$ LOOP

NUMERIC DATA"

REM Find burial labels in the clustan file REM Note that a previous line has similar text, hence process repeated twice LINE INPUT #1, TEXTLINE$ REM blank line CASELABEL = 0 CRAP$= INPUT$(?, 1) REM First few characters must be ignored INPUT #1, CLUSLABEL REM read the label for the first burial from CLUSTAN file DO WHILE CLUSLABEL 0 REM While there are still burials remaining INPUT #2, ORIGINALLABEL REM search for corresponding number in rules file REM Must ensure that the rules file has been read to correct place DO WHILE ORIGINALLABEL CLUSLABEL FOR J% = 1 TO NUMID INPUT #2, CRAP NEXTJ% REM Read identity info, so can move to next line INPUT #2, ORIGINALLABEL LOOP REM Find the correct burial number in the rules file CASELABEL = CASELABEL + I REM CASELABEL records the number for this analysis FOR J% = 1 TO NUMID REM Read and record social identity info for this burial INPUT #2, ID(CASELABEL, J%) IF ID(CASELABEL, J%) = 1 THEN IDCOUNT(J%) = IDCOUNT(J%) + 1 REM Records the occurrence of each identity in the cemetery REM Provides a check that data is being correctly read END IF NEXTJ% PRINT #3, "Case label="; CASELABEL; "Original label="; ORIGINALLABEL PRINT "Case label="; CASELABEL; "Original label="; ORIGINALLABEL CRAP$= INPUT$(?, 1) INPUT #1, CLUSLABEL REM Read the next burial label LOOP CASES = CASELABEL REM The number of cases will equal the maximum CASELABEL

NEXTY PRINT"" PRINT ELIMINATED; "identities fell below initial threshold value" NEWNUMID = NUMID - ELIMINATED PRINT NEWNUMID; " identities remain" PRINT #3, NEWNUMID; "identities remain" END SUB

PRINT#3, "" PRINT #3, "Labels were read for"; CASES;" burials" PRINT"" PRINT "Labels were read for"; CASES;" burials"

************************

END SUB

SUB READBURIALLABEL

************************

****************************************************** SUB READCLUSRESULTS (TEXTLENGTH, SEARCHTEXT$)

REM Reads the burial labels in the CLUST AN file so can be REM related back to the burials in the original RULES file REM Needed in case some burials have been eliminated

****************************************************** REM Read in cluster membership from PRINT RESULTS REM Needed because standard ouput does not have clusters numbered REM Consecutively, and PRINT MEMBERS doesn't seem to work

PRINT "Reading burial numbers from CLUSTAN ..." TEXTLINE$ = "" DO WHILE LEFT$(TEXTLINE$, 17) " RAW NUMERIC DATA" LINE INPUT #1, TEXTLINE$

PRINT#3 "" DO WHILE LEFT$(TEXTLINE$, TEXTLENGTH) SEARCHTEXT$ LINE INPUT #1, TEXTLINE$ LOOP PRINT #3, TEXTLINE$

287

Theoretical and Quantitative Approaches to the Study of Mortuary Practice REM Move on to next level in the hierarchy IF SEARCHTEXT$ =" DIVIDE HIERARCHIC" THEN START% =2 FINISH% = NUMCLUS INCREM% = 1 ELSE START%= NUMCLUS FINISH%= 2 INCREM% = -1 END IF REM DIVIDE results are printed from 2 -10 cluster level FOR CLUSTERLEVEL% = START% TO FINISH% STEP INCREM% REM Each level in dendrogram PRINT #3, "Level"; CLUSTERLEVEL% PRINT #3, "**********"

REM Next section checks size of clusters that have been read FOR CLUSTERLEVEL% = 2 TO NUMCLUS TOTAL%= 0 REM Total number of burials read FOR CLUSTER%= 1 TO CLUSTERLEVEL% PRINT PRINT PRINT PRINT

#3, "Cluster"; CLUSTER%;" at level"; CLUSTERLEVEL%; "has"; #3, CLUSSIZE(CLUSTERLEVEL%, CLUSTER%);" members" "Cluster"; CLUSTER%;" at level"; CLUSTERLEVEL%; "has"; CLUSSIZE(CLUSTERLEVEL%, CLUSTER%);" members"

TOTAL%= TOTAL%+ CLUSSIZE(CLUSTERLEVEL%, CLUSTER%) FOR CLUSTER%= 1 TO CLUSTERLEVEL% REM Each cluster at current level PRINT#3, "" PRINT #3, "Cluster"; CLUSTER%;""; PRINT "LEVEL"; CLUSTERLEVEL%; "CLUSTER"; CLUSTER% PRINT "----------------------------" TEXTLINE$ = "" DO WHILE LEFT$(TEXTLINE$, 13) " CASE NUMBERS" LINE INPUT #1, TEXTLINE$ LOOP PRINT #3, TEXTLINE$ REM Finds the list of burials for the current cluster

NEXT CLUSTER% PRINT #3, "Total burials:"; TOTAL% PRINT "Total burials:"; TOTAL% IF TOTAL% CASES THEN PRINT "WARNING! INCORRECT NUMBER OF BURIALS READ" STOP END IF REM Error trapping PRINT#3, "" PRINT

INPUT #1, BURIAL% REM Read first burial number for current cluster NEXT CLUSTERLEVEL% DO WHILE BURIAL% 0 REM When 0, end of cluster membership will have been found REM *** check that this is functioning correctly **** IF (BURIAL% < 1) OR (BURIAL% > CASES) THEN PRINT "Error in reading cluster membership" STOP END IF REM Error trapping CLUSMEM(BURIAL%, CLUSTERLEVEL%) = CLUSTER% REM Burial BURIAL% is in cluster CLUSTER% at level CLUSTERLEVEL% PRINT #3, BURIAL%; PRINT BURIAL%; CLUSSIZE(CLUSTERLEVEL%, CLUSMEM(BURIAL%, CLUSTERLEVEL%)) = CLUSSIZE(CLUSTERLEVEL%, CLUSMEM(BURIAL%, CLUSTERLEVEL%)) + 1 INPUT #1, BURIAL% REM Read next burial in current cluster LOOP PRINT#3, "" PRINT REM Next section locates and summarises the occurrence of the artefacts REM in the cluster

END SUB

********************** SUB SOCIALSUMMARY

********************** PRINT #3, "SUMMARY OF SOCIAL IDENTITIES IN CEMETERY"; CLUSTERDAT$ PRINT "SUMMARY OF SOCIAL IDENTITIES IN CEMETERY"; CLUSTERDAT$ PRINT "-----------------------------------------------------------" PRINT PRINT #3, "--------------------------------------------------------------" PRINT#3, "" FORK% = 1 TO NUMID PRINT #3, K%; ") "; IDLABEL(K%); CHR$(9); IDCOUNT(K%); PRINT #3, CHR$(9); (IDCOUNT(K%) /CASES)* 100; "%" PRINT K%; ") "; IDLABEL(K%); PRINT CHR$(9); CHR$(9); IDCOUNT(K%); PRINT CHR$(9); (IDCOUNT(K%) /CASES)*

100; "%"

NEXT K% StartTime = TIMER WHILE TimePast < 10 TimePast = TIMER - StartTime WEND REM 10 second delay

TEXTLINE$ = "" DO WHILE LEFT$(TEXTLINE$, 33) " PERCENTAGE OCCURRENCE FOR BINARY" LINE INPUT #1, TEXTLINE$ LOOP

END SUB

**************** SUB SPSSINPUT

**************** PRINT #3, TEXTLINE$ PRINT #3 "*********************************************" LINE INPUT #1, TEXTLINE$ REM Read blank line REM Find % for artefacts in cluster FOR X% = 1 TO NUMDIM INPUT #1, DIMNUMBER, PERCENT PRINT #3, DIMENSION(DIMNUMBER); " "; PERCENT NEXT X%

FOR I% = 1 TO CASES FORA%= 1 TO9 INPUT #1, CRAP NEXT A% FOR J% = NUMCLUS TO 1 STEP -1 REM Check that the cluster variables are largest number of groups down INPUT #1, CLUSMEM(I%, J%) CLUSSIZE(J%, CLUSMEM(I%, J%)) = CLUSSIZE(J%, CLUSMEM(I%, J%)) + 1 NEXTJ%

PRINT NEXT CLUSTER% REM Read burials in next cluster at current level in hierarchy

FOR J% = 1 TO NUMID INPUT #2, ID(I%, J%) IF ID(I%, J%) = 1 THEN IDCOUNT(J%) = IDCOUNT(J%) + 1 END IF

NEXTCLUSTERLEVEL%

288

Appendix 2 NEXTJ%

PRINT CLUSV ALUE(CLUSLEVEL, CLUSLEVEL; CHR$(9);

NEXT!%

PRINT CLUSV ALUE(CLUSLEVEL,

FOR LEVEL = 10 TO 1 STEP -1 PRINT "CLusters at this level"; LEVEL+ 1 PRINT "**********************" FOR COUNT = 1 TO LEVEL + 1 PRINT "CLUSTER NUMBER:"; COUNT CALL CLUSTERSIZE CALL PROMINENT CALL CLUSTERCHAR

NEXT CLUSLEVEL HIVALUE = HIVALUE - .01 REM Decrease Jaccard value PRINT "Jaccard level:"; HIVALUE LOOP PRINT #4, CHR$(12)

NEXT LEVEL

END SUB END SUB

***************** SUB TOPGROUPS

***************** REM Prints out the groups in the dendrograrn that have the top Jaccard REM values REM Level for jaccard value at which group will be printed REDIM GROUP(l TO 50) AS STRING REDIM GROUPVAL(l TO 50)

* 40

REM Records groups printed; avoids same group being printed twice PRINT#4 "" PRINT #4'. CHR$(12)

PRINT#4,

"**********************************************"

PRINT#4,

"**********************************************"

PRINT #4, "TABLE Top Jaccard values for"; PRINT #4, ANAL YSISLABEL$; CLUSTERDAT$

PRINT#4 "" PRINT #4'. "GROUP"; CHR$(9); CHR$(9); "LEVEL"; CHR$(9); "JACCARD" PRINT #4, "---------"; CHR$(9); CHR$(9); "---------"; CHR$(9); "-----------

" HIVALUE = 1 REM Initial Jaccard value to look for GROUPNUM= 1 REM Number of group printed DO WHILE GROUPNUM < 50 AND HIV ALUE > 0 REM Print up to 50 top groups FOR CLUSLEVEL = 2 TO NUMCLUS REM Go through each level in the hierarchy FOR CLSTR = 1 TO CLUSLEVEL REM Look at each cluster at current level in hierarchy IF CLUSVALUE(CLUSLEVEL, CLSTR).VALUE >= HIVALUE THEN REM This cluster has a sufficiently large jaccard value ALREADYPRINTED = 0 REM used to indicate if current group has been printed FOR X = 1 TO GROUPNUM IF CLUSV ALUE(CLUSLEVEL, CLSTR).GROUP = GROUP(X) AND CLUSV ALUE(CLUSLEVEL, CLSTR).V ALUE 7 THEN REM Need to read the factors above number 7

PRINT#3, "" PRINT#4, "" END IF

******************* SUB MVSPSCORES

******************* REM Reads principal component scores for covariance matrix from MVSP DO WHILE LEFT$(TEXTLINE$, SCORES" LINE INPUT #1, TEXTLINE$ LOOP REM Locates loadings in file

26) "PRINCIPAL COMPONENT

PRINT"" PRINT "SCORES HAVE BEEN LOCATED FROM A MVSP FILE" PRINT#3,

''************************************************************* ********" IF NUMFACTOR > 7 AND NUMFACTOR < 15 THEN ONELINE = NUMFACTOR ELSE ONELINE = 14 END IF FORx%= 1 TO3 LINE INPUT #1, TEXTLINE$ NEXTx% REM Four blank lines FOR x% = 1 TO NUMV ARIABLES VARIABLE(x%) = INPUT$(10, I) CRAPTEXT = INPUT$(5, 1)

PRINT #3, "TABLE N$ PRINT#3,

Scores for unrotated PCA (covariance matrix) of";

''************************************************************* ********" PRINT"" PRINT#3 "" LINE INPUT #1, TEXTLINE$ LINE INPUT #1, TEXTLINE$ LINE INPUT #1, TEXTLINE$ IF NUMFACTOR > 7 THEN ONELINE = 7 ELSE ONELINE = NUMFACTOR

295

Theoretical and Quantitative Approaches to the Study of Mortuary Practice END IF REM If more than 7 factors are extracted, the additional factors are REM printed below the first 7, so must be read in two stages

END SUB

***************** SUB MVSPTYPES

FOR x% = 1 TO CASES

*****************

CRAPTEXT$ = INPUT$(16, 1) PRINT x%;" "; PRINT#3,x%;" ";

REM Read coordinates for types in correspondence analysis

FOR Y% = 1 TO ONELINE INPUT #1, SCORE(x%, Y%) PRINT SCORE(x%, Y%);" "; PRINT #3, SCORE(x%, Y%);" "; NEXT Y% PRINT II II PRINT#3,"" NEXT x% PRINT 1111 PRINT#3,

DO WHILE LEFT$(TEXTLINE$, 14) "SPECIES SCORES" LINE INPUT #1, TEXTLINE$ LOOP REM Locates loadings in file PRINT#3, 1111 PRINT #3, "COORDINATES HA VE BEEN LOCATED FROM A MVSP FILE" PRINT #4, CHR$(12) PRINT#4,

"************************************************************* ************" 1111

IF NUMFACTOR > 7 THEN REM Need to read the factors above number 8

PRINT #4, "TABLE of ";N$ PRINT#4,

Coordinates of types for"; ANALYSISLABEL$;"

"************************************************************* ************II

IF NUMFACTOR > 7 AND NUMFACTOR < 15 THEN ONELINE = NUMFACTOR ELSE ONELINE = 14 END IF

PRINT#3, 1111 PRINT#4 1111 LINE INPUT #1, TEXTLINE$ LINE INPUT #1, TEXTLINE$ LINE INPUT #1, TEXTLINE$

FORx% = 1 TO3 LINE INPUT #1, TEXTLINE$ NEXT x% REM Four blank lines

IF NUMF ACTOR> 7 THEN ONELINE = 7 ELSE ONELINE = NUMFACTOR END IF REM If more than 7 factors are extracted, the additional factors are REM printed below the first 7, so must be read in two stages

FOR x% = 1 TO CASES CRAPTEXT = INPUT$(16, 1) PRINT x%;" "; PRINT#3,x%;" "; FOR Y% = 8 TO ONELINE INPUT #1, SCORE(x%, Y%) PRINT SCORE(x%, Y%);" "; PRINT #3, SCORE(x%, Y%);" "; NEXT Y% PRINT II II PRINT#3,"" NEXT x% PRINT 1111 PRINT#3,

1111

FOR x% = 1 TO NUMV ARIABLES VARIABLE(x%) = INPUT$(10, 1) CRAPTEXT = INPUT$(5, 1) PRINT #3 VARIABLE(x%)· "*"· CRAPTEXT· "*"· PRINT #4'. V ARIABLE(x%); CHR$(9); ' ' FOR Y% = 1 TO ONELINE INPUT #1, LOADING(x%, Y%) PRINT #3, LOADING(x%, Y%);" "; REM Rounds the number PRINT #4, LOADING(x%, Y%); CHR$(9); NEXT Y% PRINT#3,"" PRINT#4,""

END IF IFNUMFACTOR> 14THEN REM Need to read the factors above number 14

NEXTx% PRINT#3, PRINT#4,

1111 1111

IF NUMFACTOR > 7 THEN REM Need to read the factors above number 8 FORx% = 1 TO3 LINE INPUT #1, TEXTLINE$ NEXT x% REM Four blank lines FOR x% = 1 TO CASES

IF NUMFACTOR > 7 AND NUMFACTOR < 15 THEN ONELINE = NUMFACTOR ELSE ONELINE = 14 END IF

CRAPTEXT = INPUT$(16, 1) PRINT x%;" "; PRINT#3,x%;" ";

FORx%= 1 TO3 LINE INPUT #1, TEXTLINE$ NEXTx% REM Four blank lines

FOR Y% = 15 TO NUMFACTOR

FOR x% = 1 TO NUMV ARIABLES

INPUT #1, SCORE(x%, Y%) PRINT SCORE(x%, Y%);" "; PRINT #3, SCORE(x%, Y%);" "; NEXT Y% PRINT II II PRINT#3,""

VARIABLE(x%) = INPUT$(10, 1) CRAPTEXT = INPUT$(5, 1) PRINT #3, VARIABLE(x%); "*"; CRAPTEXT; "*"; PRINT #4, V ARIABLE(x% ); CHR$(9); FOR Y% = 8 TO ONELINE

NEXT x% PRINT 1111 PRINT#3, END IF

INPUT #1, LOADING(x%, Y%) 1111

PRINT #3, LOADING(x%, Y%);" "; PRINT #4, LOADING(x%, Y%); CHR$(9); NEXT Y%

296

Appendix 2 PRINT#3,"" PRINT#4,""

REM Calculate correlation for each component NEWNUMID = NUMID

NEXTx% PRINT#3, "" PRINT#4, "" END IF

POSTOPGROUP$ NEGTOPGROUP$ POSTOPCORREL NEGTOPCORREL

IF NUMFACTOR > 14 THEN REM Need to read the factors above number 14

SCORETOT AL = 0 SCOREMEAN = 0

FORx%= 1 TO3 LINE INPUT #1, TEXTLINE$ NEXTx% REM Four blank lines

FOR CASENUM = 1 TO CASES SCORETOTAL = SCORETOTAL + SCORE(CASENUM, COMPONENT) NEXT CASENUM SCOREMEAN = SCORETOT AL/ CASES PRINT "Score mean for axis"; COMPONENT;":"; SCOREMEAN

FOR x% = 1 TO NUMV ARIABLES

SCOREVAR=0

VARIABLE(x%) = INPUT$(10, 1) CRAPTEXT = INPUT$(5, 1) PRINT #3 VARIABLE(x%)· "*"· CRAPTEXT· "*"· PRINT #4'. V ARIABLE(x% ); CHR$(9); ' '

FOR CASENUM = 1 TO CASES SCOREV AR= SCOREV AR+ ((SCORE(CASENUM, COMPONENT) SCOREMEAN) A 2) NEXT CASENUM SCORESD = SQR(SCOREV AR I CASES) PRINT "Score standard deviation for axis"; COMPONENT;":"; SCORESD

= "" = "" =0 =0

FOR Y% = 15 TO NUMFACTOR INPUT #1, LOADING(x%, Y%)

REM consider current group, since no identities are to be ignored

PRINT #3, LOADING(x%, Y%);" "; PRINT #4, LOADING(x%, Y%); CHR$(9); NEXT Y% PRINT#3,"" PRINT#4,""

PRINT "considering ... "

NEXTx% PRINT#3, "" PRINT#4, "" END IF PRINT #4, CHR$(12) END SUB

********************* SUB NEWCORRELATE

********************* REM Calculate point-biserial correlation coefficients for the social REM groups being examined, relative to the component scores or coordinates REM for each principal axis PRINT "Calculating correlation ... " PRINT #4, CHR$(12) REM Page break

FOR SEARCH= 1 TO NUMSEARCHGROUPS PRINT "Group"; SEARCH REDIM CHOOSE(l TO NUMID) CORRELATION= 0 FINDGROUP$ = "" FOR x = 1 TO NUMID FINDGROUP$ = "/" + IDLABEL(x) + "/" FINDPOS = INSTR(SEARCHGROUP(SEARCH), IF FINDPOS 0 THEN CHOOSE(x) = 1 PRINT IDLABEL(x); " "; END IF NEXTx PRINT"";

FINDGROUP$)

GROUPASIZE = 0 GROUPBSIZE = 0 GROUPATOTAL = 0 GROUPBTOTAL = 0

FOR CASENUM = 1 TO CASES NOMATCH=0 FOR CHECK = 1 TO NUMID "************************************************************* ***********" IF CHOOSE(CHECK) = 1 AND ID(CASENUM, CHECK) 1 THEN PRINT #4, "TABLE Highest correlations on axes for"; NOMATCH= 1 ANAL YSISLABEL$; REM This individual lacks some identity in current group PRINT #4," of"; N$ ELSE PRINT#4, END IF "************************************************************* NEXT CHECK PRINT#4,

***********II

PRINT#4 "" PRINT #4: CHR$(9); CHR$(9); CHR$(9); "Positive"; PRINT #4, CHR$(9); CHR$(9); CHR$(9); CHR$(9); "Negative" PRINT#4, "" FOR COMPONENT= 1 TO NUMFACTOR REDIM TOPCORREL(l TO NUMSEARCHGROUPS) AS STORECORREL REM Stores the top correlations for this component

IF NOMATCH = 0 THEN REM Burial corresponds with current group GROUPASIZE = GROUPASIZE + 1 GROUPATOTAL = GROUPATOTAL + SCORE(CASENUM, COMPONENT) ELSE GROUPBTOTAL = GROUPBTOTAL + SCORE(CASENUM, COMPONENT) GROUPBSIZE = GROUPBSIZE + 1 END IF

CORRELTHRESH = .5 REM Threshold above which to print correlations REM This burial is in this group PRINT "***************" PRINT "AXIS"; COMPONENT;"("; VARIANCE(COMPONENT); "%)" PRINT "***************" PRINT PRINT"("; ANAL YSISLABEL$; "of"; N$; ")" PRINT PRINT #4, "AXIS "; COMPONENT; " ("; V ARIANCE(COMPONENT); "%)" PRINT #4, "-------------------------" PRINT#4, ""

NEXT CASENUM IF GROUPASIZE + GROUPBSIZE CASES THEN PRINT "PROBLEM WITH GROUP SIZES" STOP END IF PRINT "Members:"; GROUPASIZE GROUPAMEAN = GROUPATOTAL I GROUPASIZE

297

Theoretical and Quantitative Approaches to the Study of Mortuary Practice GROUPBMEAN = GROUPBTOTAL I GROUPBSIZE PRODUCT= (GROUPASIZE I CASES) * (GROUPBSIZE I CASES) IF GROUPASIZE > 0 THEN REM Check that this group has members SUM=0 CORRELATION= ((GROUPAMEAN - GROUPBMEAN) I SCORESD) * SQR(PRODUCT) REM See Hammond and McCullagh (1974:210-11) for this formula REM for the point-biserial correlation coefficient CORRELATION= CINT(CORRELATION REM Round the value

* 1000) / 1000

IF TOPCORREL(x).V ALUE < TOPNEGCORREL THEN TOPNEGGROUP = x TOPNEGCORREL = TOPCORREL(x) .VALUE END IF NEXTx IF TOPPOSGROUP 0 THEN PRINT #4, RTRIM$(TOPCORREL(TOPPOSGROUP).GROUP); CHR$(9); TOPCORREL(TOPPOSGROUP).V ALUE; PRINT RTRIM$(TOPCORREL(TOPPOSGROUP).GROUP); CHR$(9); TOPCORREL(TOPPOSGROUP).VALUE; TOPCORREL(TOPPOSGROUP).V ALUE = 0 CORRELPRINTED = 1 ELSE PRINT #4, CHR$(9); CHR$(9); PRINT CHR$(9); CHR$(9); END IF

IF ABS(CORRELATION) > ABS(FINALTAB(SEARCH, ANAL YSISNUM)) THEN FINALTAB(SEARCH, ANALYSISNUM) = CORRELATION END IF

IF TOPNEGGROUP 0 THEN PRINT #4, CHR$(9); RTRIM$(TOPCORREL(TOPNEGGROUP).GROUP); CHR$(9); TOPCORREL(TOPNEGGROUP) .VALUE PRINT CHR$(9); RTRIM$(TOPCORREL(TOPNEGGROUP).GROUP); CHR$(9); TOPCORREL(TOPNEGGROUP) .VALUE

REM Stores the highest correlations for this analysis for this REM particular search group TOPCORREL(SEARCH) .VALUE = CORRELATION REM Store this correlation

TOPCORREL(TOPNEGGROUP).V ALUE = 0 CORRELPRINTED = 1 ELSE PRINT #4, CHR$(9); CHR$(9) PRINT CHR$(9); CHR$(9)

GROUPSTRING$ = "" FOR x% = 1 TO NUMID IF CHOOSE(x%) = 1 THEN GROUPSTRING$ = GROUPSTRING$ + IDLABEL(x%) +"" END IF NEXT x% TOPCORREL(SEARCH).GROUP

END IF IF TOPPOSGROUP = 0 AND TOPNEGGROUP = 0 THEN COMPLETED =1

= GROUPSTRING$

IF CORRELATION> CORRELTHRESH OR CORRELATION< CORRELTHRESH)THEN REM Only print highest correlations

LOOP (0 -

PRINT "Correlation for"; PRINT #3, "("; PRINT"("; FOR x% = 1 TO NUMID IF CHOOSE(x%) = 1 THEN PRINT #3, IDLABEL(x%);" "; PRINT IDLABEL(x%); " "; END IF NEXT x% PRINT #3, ")is"; CORRELATION PRINT") is"; CORRELATION

IF CORRELPRINTED = 0 THEN CORRELTHRESH = CORRELTHRESH- .1 END IF REM No correlations have exceeded threshold, so reduce it by 0.1 LOOP NEXT COMPONENT PRINT #4, CHR$(12) REM Page break END SUB

*********************** END IF

SUB NUMBERFACTORS

*********************** END IF REM New group sum PRINT"" NEXTSEARCH PRINT#3, "" PRINT"" PRINT "Top correlations are:" CORRELPRINTED = 0 REM Used to check if at least one correlation has exceeded threshold DO WHILE CORRELPRINTED = 0

REM Determines the number of factors extracted in SPSS file PRINT "Determining number of factors in SPSS file ... " TEXTLINE$ = "" DO WHILE LEFT$(TEXTLINE$, 10) "Extraction" REM Find the next factor extraction in SPSS output LINE INPUT #1, TEXTLINE$ IF LEFT$(TEXTLINE$, 8) = ">Warning" THEN PRINT #3, TEXTLINE$ LINE INPUT #1, TEXTLINE$ PRINT #3, TEXTLINE$ END IF REM Print warnings PRINT TEXTLINE$ LOOP

COMPLETED = 0 PRINT #3, TEXTLINE$ DO WHILE COMPLETED = 0 REM Indicates when all correlations above threshold have been printed TOPNEGGROUP = 0 TOPNEGCORREL = (0 - CORRELTHRESH) TOPPOSGROUP = 0 TOPPOSCORREL = CORREL THRESH FOR x = 1 TO NUMSEARCHGROUPS IF TOPCORREL(x).V ALUE > TOPPOSCORREL THEN REM Has highest correlation currently examined TOPPOSGROUP = x REM Store the group number TOPPOSCORREL = TOPCORREL(x).V ALUE REM Store the correlation END IF

NUMFACTOR = 0 TEXTLINE$ = "" DO WHILE LEFT$(TEXTLINE$, 18) "Initial Statistics" LINE INPUT #1, TEXTLINE$ IF LEFT$(TEXTLINE$, 8) = ">Warning" THEN PRINT #3, TEXTLINE$ LINE INPUT #1, TEXTLINE$ PRINT #3, TEXTLINE$ END IF REM Print warnings PRINT TEXTLINE$ LOOP FORx% = 1 TO4

298

Appendix 2 LINE INPUT #1, TEXTLINE$ NEXTx% EIGEN = 999 DO WHILE EIGEN > 1 CRAPTEXT = INPUT$(27, 1) INPUT #1, NUMFACTOR, EIGEN, VARIANCE(NUMFACTOR), CPCT PRINT #4, "Component"; NUMFACTOR; "Eigenvalue"; EIGEN; PRINT #4," Percentage"; VARIANCE(NUMFACTOR); "Cumulative:"; CPCT

FOR J% = 1 TO NUMID INPUT #2, ID(x, J%) IF ID(x, J%) = 1 THEN IDCOUNT(J%) = IDCOUNT(J%) + 1 END IF NEXTJ% REM CASELABEL records the number for this analysis NEXTx END SUB

PRINT "Component"; NUMFACTOR; "Eigenvalue"; EIGEN; PRINT" Percentage"; VARIANCE(NUMFACTOR)

***************** SUB READSOCIAL

LOOP

*****************

NUMFACTOR = NUMFACTOR - 1 REM Since the last factor read will have been below 1

REM Read in rules information RULENUMBER = 0

PRINT NUMFACTOR; " factors have been extracted" PRINT #4, NUMFACTOR; "factors have been extracted" PRINT 1111 END SUB

TEXTLINE$ = 1111 DO WHILE LEFT$(TEXTLINE$, 5) "RULES" LINE INPUT #2, TEXTLINE$ LOOP REM Locate section of rules file with rules

************************ SUB READCASELABELS

************************ PRINT "Reading burial numbers form MVSP file ..." REM Read labels for burials, and read appropriate identity info TEXTLINE$ = II II DO WHILE LEFT$(TEXTLINE$, 19) "TRANSPOSED RAW DATA" LINE INPUT #1, TEXTLINE$ LOOP LINE INPUT #1, TEXTLINE$ REM blank line

INPUT#2,x% DO WHILE x% 999 INPUT #2, RULE(x%).ARTEFACT, RULE(x%).PROBABILITY PRINT #3, "Rulenumber "; x%; PRINT #3, RULE(x%).ARTEFACT, RULE(x%).PROBABILITY PARAMNUM= 1 INPUT #2, RULEPARAM(x%, PARAMNUM).PARAMETER DO WHILE RULEPARAM(x%, PARAMNUM).PARAMETER "RULEEND II PRINT #3, RULEPARAM(x%, PARAMNUM).PARAMETER; INPUT #2, RULEPARAM(x%, PARAMNUM).OPERATOR INPUT #2, RULEPARAM(x%, PARAMNUM).V ALUE PRINT #3, RULEPARAM(x%, PARAMNUM).OPERATOR; RULEPARAM(x%, PARAMNUM).VALUE

CASES=0 INPUT #1, LABEL DO WHILE LABEL 0 REM read the label for the first burial CASES = CASES + 1 BURIALLABEL(CASES) = LABEL PRINT "Case"; CASES;" Original number"; BURIALLABEL(CASES) IF CINT(CASES / 7) = CASES / 7 THEN PRINT"*" FOR x = 1 TO NUMV ARIABLES + 2 LINE INPUT #1, TEXTLINE$ NEXTx

PARAMNUM = PARAMNUM + 1 INPUT #2, RULEPARAM(x%, PARAMNUM).PARAMETER LOOP PRINT #3, "*****************************************" RULENUMBER = RULENUMBER + 1 INPUT#2,x% LOOP PRINT #3, RULENUMBER; "rules were recovered" PRINT#3,"" END SUB

END IF

********************** INPUT #1, LABEL LOOP

SUB SOCIALSUMMARY

PRINT#3, PRINT #3, "Labels were read for"; CASES; "burials" PRINT "Labels were read for"; CASES; "burials"

REM Summarises the social identities in the cemetery

1111

**********************

CLS

PRINT "SUMMARY OF SOCIAL IDENTITIES IN CEMETERY" END SUB

PRINT"****************************************" PRINT#4,

************* SUB READID

************* REM Reads social identity information from rules file, linking it REM with correct burial number

''*************************************************************

****II

PRINT #4, "TABLE PRINT#4,

Summary of occurrence of social identities in"; N$

''************************************************************* ****" PRINT#4,

1111

FOR x = 1 TO CASES INPUT #2, ORIGINALLABEL REM search for corresponding number in rules file REM Must ensure that the rules file has been read to correct place DO WHILE ORIGINALLABEL BURIALLABEL(x) PRINT"/"; ORIGINALLABEL; FOR J% = 1 TO NUMID INPUT #2, CRAP NEXTJ% REM Read identity info, so can move to next line INPUT #2, ORIGINALLABEL LOOP REM Find the correct burial number in the rules file

FOR K% = 1 TO NUMID IDPERCENT = (IDCOUNT(K%) I CASES)* 100 PRINT K%; ") "; PRINT#4,K%; ") "; PRINT IDLABEL(K%); CHR$(9); CHR$(9); PRINT CINT(IDPERCENT * 100) I 100; "%" PRINT #4, IDLABEL(K%); CHR$(9); CHR$(9); PRINT #4, CINT(IDPERCENT * 100) I 100; 11%11 REM Rounds the number NEXT K% StartTime = TIMER WHILE TimePast < 10 TimePast = TIMER - StartTime

299

Theoretical and Quantitative Approaches to the Study of Mortuary Practice WEND

PRINT LOADING(x%, Y%); " "; NEXT Y%

END SUB

PRINT#3,"" PRINT"" PRINT#4,"" NEXTx%

****************************************** SUB SPSSIN (TEXTLENGTH, TEXTSEARCH$)

****************************************** REM Reads component loadings from SPSS file PRINT #4, CHR$(12) PRINT#4,

PRINT#3, "" PRINT#4 "" PRINT #4'. CHR$(12) PRINT"" END IF

''************************************************************* *************" END SUB PRINT#4, "TABLE PRINT#4,

Loadings for"; ANALYSISLABEL$; "of"; N$

****************** ''************************************************************* SUB SPSSLABELS *************" ******************

PRINT#4, "" REM Reads burial labels from SPSS DO WHILE LEFT$(TEXTLINE$, TEXTLENGTH) TEXTSEARCH$ LINE INPUT #1, TEXTLINE$ IF LEFT$(TEXTLINE$, 8) = ">Warning" THEN PRINT #3, TEXTLINE$ LINE INPUT #1, TEXTLINE$ PRINT #3, TEXTLINE$ END IF REM Print any warnings

PRINT "Reading burial numbers from SPSS file ..." TEXTLINE$ = "" DO WHILE LEFT$(TEXTLINE$, 8) " CASE" LINE INPUT #1, TEXTLINE$ LOOP LINE INPUT #1, TEXTLINE$

LOOP

INPUT #1, LABEL

FORx%=1TO4 LINE INPUT #1, TEXTLINE$ PRINT #4, TEXTLINE$ NEXT x%

DO WHILE LABEL 0

IF NUMFACTOR > 8 THEN ONELINE = 8 ELSE ONELINE = NUMFACTOR END IF REM If more than 8 factors are extracted, the additional factors are REM printed below the first 8, so must be read in two stages

CASES=0

REM read the label for the first burial CASES = CASES + 1 BURIALLABEL(CASES) = LABEL PRINT "Case"; CASES;" Original number"; BURIALLABEL(CASES) INPUT #1, LABEL LOOP PRINT#3, "" PRINT #3, "Labels were read for"; CASES; "burials"

FOR x% = 1 TO NUMV ARIABLES

END SUB

VARIABLE(x%) = INPUT$(10, 1) PRINT #3, VARIABLE(x%); "*"; CHR$(9); PRINT #4, VARIABLE(x% ); CHR$(9); PRINTVARIABLE(x%); "*";

SUB SPSSSCORES

****************** ******************

REM Reads principal components scores; used to work out correlations FOR Y% = 1 TO ONELINE INPUT #1, LOADING(x%, Y%) PRINT #3, LOADING(x%, Y%);" "; PRINT #4, LOADING(x%, Y%); CHR$(9); PRINT LOADING(x%, Y%); " "; NEXT Y%

PRINT#3, "" PRINT #3, "SCORES HAVE BEEN LOCATED FROM A SPSS FILE" PRINT#3,

PRINT#3,"" PRINT#4,"" PRINT"" NEXT x%

"************************************************************* ********II

PRINT#3, "" PRINT#4, "" PRINT"" IF NUMFACTOR > 8 THEN REM Need to read the factors above number 8 FORx%=1TO4 LINE INPUT #1, TEXTLINE$ NEXT x% REM Four blank lines FOR x% = 1 TO NUMV ARIABLES VARIABLE(x%) = INPUT$(10, 1) PRINT #3, VARIABLE(x%); PRINT #4, VARIABLE(x%); CHR$(9); PRINTVARIABLE(x%); "*"; FOR Y% = 9 TO NUMFACTOR INPUT #1, LOADING(x%, Y%) PRINT #3, LOADING(x%, Y%); " "; PRINT #4, LOADING(x%, Y%); CHR$(9);

"************************************************************* ********" PRINT #3, "TABLE PRINT#3,

Scores for"; ANALYSISLABEL$; "of"; N$

PRINT#3, "" PRINT#3, "" IF NUMFACTOR = 0

IF LOADING(Y%, x%) < CURRENTLOADING AND DISPLAYEDVAR(Y%) 1 THEN DISPLAYEDVAR(Y%) = 1

FOR Y% = 1 TO NUMV ARIABLES REM Look at positive loadings IF LOADING(Y%, x%) > CURRENTLOADING AND DISPLAYEDVAR(Y%) 1 THEN

PRINT #4, VARIABLE(Y%); CHR$(9); "("; PRINT #4, LOADING(Y%, x%); ")"; CHR$(9);

DISPLAYEDVAR(Y%) = 1

RULEFOUND = 0 RULECOUNT = 0

PRINT #4, VARIABLE(Y%); CHR$(9); "("; PRINT #4, LOADING(Y%, x%); ") ";" ";

FOR Z% = 1 TO RULENUMBER

RULEFOUND = 0 RULECOUNT = 0 REM Used to space table

IF RULE(Z%).ARTEFACT = VARIABLE(Y%) THEN RULECOUNT = RULECOUNT + 1 IF RULECOUNT > 1 THEN PRINT #4, CHR$(9); CHR$(9); CHR$(9); CHR$(9); PRINT #4, "("; PARAMNUM= 1 DO WHILE RULEPARAM(Z%, PARAMNUM).PARAMETER "RULEEND " PRINT #4, RTRIM$(RULEPARAM(Z%, PARAMNUM).PARAMETER); PRINT #4, RULEPARAM(Z%, PARAMNUM).OPERATOR; PRINT #4, RULEPARAM(Z%, PARAMNUM).V ALUE; PARAMNUM = PARAMNUM + 1 LOOP RULEFOUND = 1 PRINT #4, ") "; RULE(Z%).PROBABILITY; "%" END IF NEXT Z% IF RULEFOUND = 0 THEN PRINT "NO RULES WERE LOCATED THAT RELATED TO ARTEFACT"; VARIABLE(Y%) STOP END IF END IF NEXT Y%

FOR Z% = 1 TO RULENUMBER IF RULE(Z%).ARTEFACT = VARIABLE(Y%) THEN RULECOUNT = RULECOUNT + 1 IF RULECOUNT > 1 THEN PRINT #4, CHR$(9); CHR$(9); CHR$(9); "

"·

PRINT #4, "("; PARAMNUM= 1 DO WHILE RULEPARAM(Z%, PARAMNUM).PARAMETER "RULEEND " PRINT #4, RTRIM$(RULEP ARAM(Z%, PARAMNUM).PARAMETER); PRINT #4, RULEPARAM(Z%, PARAMNUM).OPERATOR; PRINT #4, RULEPARAM(Z%, PARAMNUM).V ALUE; PARAMNUM = PARAMNUM + 1 LOOP RULEFOUND = 1 PRINT #4, ") "; RULE(Z%).PROBABILITY; "%" END IF NEXT Z%

CURRENTLOADING = CURRENTLOADING + 1 REM Increase size of loading to print

IF RULEFOUND = 0 THEN PRINT "NO RULES WERE LOCATED THAT RELATED TO ARTEFACT"; VARIABLE(Y%) STOP END IF END IF NEXT Y%

LOOP REM Look through variables for lower loadings until threshold found

CURRENTLOADING = CURRENTLOADING - 1 REM Reduce size of loading to print

302

Appendix 2

LOOP REM Look through variables for lower loadings until threshold found

PRINT #4, "Table Major loadings and associated rules for"; ANAL YSISLABEL$; " of "; N$ PRINT#4,

''*************************************************************

**********II PRINT#4, "" PRINT #4, "Negative loadings" PRINT #4, "-----------------"

PRINT#4, "" FOR x% = 1 TO NUMFACTOR

REDIM DISPLA YEDV AR(l TO NUMV ARIABLES) CURRENTLOADING = -500

REDIM DISPLA YEDV AR(l TO NUMV ARIABLES) REM Used to print loadings in order

DO WHILE CURRENTLOADING

CURRENTLOADING = .99 REM Largest loading to look for initially exceeds this

1 THEN PRINT #4, CHR$(9); CHR$(9); CHR$(9); "

"·

PRINT#4, "("; PARAMNUM= 1 DO WHILE RULEPARAM(Z%, PARAMNUM).PARAMETER "RULEEND " PRINT #4, RTRIM$(RULEPARAM(Z%, PARAMNUM).PARAMETER); PRINT #4, RULEPARAM(Z%, PARAMNUM).OPERATOR; PRINT #4, RULEPARAM(Z%, PARAMNUM).V ALUE; PARAMNUM = PARAMNUM + 1 LOOP RULEFOUND = 1 PRINT #4, ") "; RULE(Z%).PROBABILITY; "%" END IF NEXT Z% IF RULEFOUND = 0 THEN PRINT "NO RULES WERE LOCATED THAT RELATED TO ARTEFACT"; VARIABLE(Y%) STOP END IF END IF NEXT Y% CURRENTLOADING = CURRENTLOADING.01 REM Reduce size of loading to print LOOP REM Look through variables for lower loadings until threshold found

************* REM Orders and prints component loadings, and associated rules PRINT PRINT "Tabulating component loadings ..." THRESHOLD = .3 REM Loadings above this will be considered PRINT#3, "" PRINT #3, "THRESHOLD VALUE FOR LOADINGS:"; THRESHOLD PRINT"" PRINT #4, CHR$(12) PRINT#4,

";

PRINT#4, "" PRINT #4, "Negative loadings" PRINT #4, "-----------------" REDIM DISPLA YEDV AR(l TO NUMV ARIABLES) CURRENTLOADING = -.99 DO WHILE CURRENTLOADING

1 THEN PRINT #4, CHR$(9); CHR$(9); CHR$(9); CHR$(9); PRINT #4, "("; PARAMNUM= 1 DO WHILE RULEPARAM(Z%, PARAMNUM).PARAMETER "RULEEND II PRINT #4, RTRIM$(RULEPARAM(Z%, PARAMNUM).PARAMETER); PRINT #4, RULEPARAM(Z%, PARAMNUM).OPERATOR; PRINT #4, RULEPARAM(Z%, PARAMNUM).V ALUE; PARAMNUM = PARAMNUM + 1 LOOP RULEFOUND = 1 PRINT #4, ") "; RULE(Z%).PROBABILITY; "%" END IF NEXT Z% IF RULEFOUND = 0 THEN PRINT "NO RULES WERE LOCATED THAT RELATED TO ARTEFACT"; VARIABLE(Y%) STOP END IF END IF NEXT Y% CURRENTLOADING = CURRENTLOADING + .01 LOOP NEXT x% PRINT #4, CHR$(12) END SUB

304

APPENDIX 3 DATA FOR MODEL CEMETERIES

********************************************************** TABLE Al

Key to artefact and social identity abbreviations in data tables

********************************************************** NUMBER/ LETTER

DESCRIPTION

-------------

-------------------

A. B: C: D: E: F: G:

H: 1: 2: 3: 4: 5: 6: 7: 8: 9:

10: 11: 12: 13: 14: 15: 16: 17: 18: 19: 20: 21: 22: 23: 24: 25: 26: 27: 28: 29: 30: 31: 32: 33: 34: 35: 36: 37-46: 47: 48: 49:

NUMBER/ LETTER

Burial number Age Sex Wealth level Clan Shaman/leadership role Rank Choice of artefacts

50: 51: 52: 53: 54: 55: 56: 57: 58: 59: 60: 61: 62: 63: 64: 65: 66: 67: 68: 69: 70: 71:

Flint scraper Flint blade Copper blade Plain pottery Decorated pottery Pottery bowl Pottery jug Pottery cup Stone bead Shell bead Copper bead Flint arrowhead Bone awl Copper awl Shell bracelet Copper bracelet Bone ring Copper ring Stone axe Copper axe Stone mace Pig mandible Boar tusk Clay figurine Bone pendant (Type A) Bone pendant (Type B) Animal bones Perforated teeth Bone pin Copper pin Copper plate Copper spiral Ochre Shells Charcoal remains Copper disc Random variables Single bracelet Single spectacle fibula Single fibula of other type 305

DESCRIPTION Multiple bracelets Multiple fibulae of other type Multiple spectacle fibulae Beads Belt fittings Lunate fibula Hairpin/coil sets Anklet Rodlink chain Ring/disc jangles Presence of gold Heavy bronze ring Gold wire coil Heavy chain Ox figurine Toy/charm Miscellaneous items "Rusty Iron" Metal vessel Pins "Clay coffins" Small knife

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA2 Cemetery data for preliminary model (50 burials) A

B

C

DEF

12 14 5 2 2 12 19 1 12 3 2 2 6 3 2 13 3 3 4 4 5 6 5 2 2 031579 48778901 9

1 2 3 4 5 6 7 8 9 10

28 4 54 48 2 4 44 47 3 21 59 65 7 40 78 72 40 33 60 56 66 2 39 3 9 3 49 17 41 1 37 2 37 41 26 2 1 27 18 64 43 55 4 66 17 62 8 2 7 66

M M

2 3 2 2 2 3 2 1 3 2 2 3 3 2 2 2 3 3 2 3 2 3 3 3 3 3 1 1 2 2 1 3 1 1 2 2 3 2 1 3 2 2 3 1 3 3 2 3 3 2

0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1

11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

45 46 47 48 49 50

F F M M

F M

F M M

F M

F M M

F M M

F M

F M

F M M

F M

F M M

F M

F M M M

F F F F M M M

F M

F F F M

1 2 2 1 1 2 1 2 2 2 2 2 1 1 1 1 2 2 2 1 1 1 2 1 2 2 1 2 1 2 1 1 1 1 1 2 1 1 1 1 2 2 1 2 2 2 2 2 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

1 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 1 1 0 0 1 0 0 0 1 1 1 1 1 0 0 0 1 0 1 0 1 1 1 1 1 0 0 1

1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 0 0

1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1

0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 1 1 0 0 0 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 1 1 0 1 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 1

1 1 0 0 0 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 1 1 0 1 1 1 0

1 0 1 0 1 0 1 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 1 1 0 1 0 1 1 1 0 1 0 0

0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 1 1 0 1 1 0 0 0 1 0 1

0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0

TABLEA3 Cemetery data for preliminary model (100burials) A

B

C

DEF 1214522121911 5 6 5 2 2 9

1 2 3 4 5 6 7 8 9 10

45 56 1 11 24 70 2 29 22 24 4 56 24 7 76 6 35 42

M M

3 2 3 3 2 2 3 3 1 1 3 2 2 3 2 3 3 2

11

12 13 14 15 16 17 18

F M M M

F F F F F M

F M

F F F F

2 2 2 1 2 2 1 1 1 2 2 2 1 1 1 2 2 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1

1 1 0 0 1 0 0 1 1 0 0 0 1 0 0 0 1 0

0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0

1 1 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 1

0 1 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1

0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0

1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0

2 3 2 2 6 3 2 13 3 3 4 4 031579 48778901 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0

306

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

1 0 0 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1

1 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 0 0

1 1 0 0 1 0 1 1 1 1 1 0 1 1 0 0 1 0

1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0

Appendix 3 TABLEA3 (Continued) A

B

C

DEF 121452212191 5 6 5 2 2 9

19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

3 3 42 58 40 21 16 51 3 2 71 74 2 41 50 73 67 3 31 36 3 73 71 65 32 39 27 63 29 76 57 19 44 28 24 1 63 3 55 2 33 67 75 8 4 39 2 69 3 1 3 33 25 50 47 61 74 31 16 7 48 2 23 1 1 33 66 44 37 2 44 57 4 22

F

2 2 2 2 1 3 3 2 3 3 3 2 3 2 2 2 3 3 1 3 3 1 3 3 3 1 1 1 1 3 2 3 2 2 2 3 2 3 2 3 1 1 2 2 3 1 3 3 3 3 3 1 2 2 3 2 1 1 1 3 2 3 3 3 2 2 2 1 1 2 2 2 3 3

71

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92

M M

F M M M

F M

F M M M M

F M

F M M M M

F F F F F F F M M

F F M M

F M

F F M M M

F F M M M

F F F M

F M

F F M M M M

F F F F M M

F M

F M M

F F M

F M

1 1 1 2 1 1 2 2 1 2 1 2 2 2 2 1 2 2 1 1 2 2 1 1 2 2 2 1 1 1 1 2 2 1 2 2 1 2 2 1 2 2 2 1 2 1 2 1 1 1 1 2 1 1 1 2 1 1 2 1 2 2 2 2 2 1 1 2 1 2 2 2 2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

1 0 1 1 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 0 1 0 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1 0 0 0 0 1 0 0

0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 1 1 1 1 0 0 0

1 1 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0

12 3 2 2 6 3 2 13 3 3 4 4 031579 48778901 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0

0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

1 0 1 0 0 0 0 1 0 1 1 0 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 0 1 1 0 1

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0

0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0

0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

307

0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

1 0 1 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1

0 0 0 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 0 0 0 1 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 1 1 0 1 0 1 0 0 1 0

0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE A3 Continued A 93 94 95 96 97 98 99 100

B 29 9 49 3 49 5 44 1

C M M M F F F F M

DEF12145221219112322632133344 9 5 6 5 2 2 0 3 1 5 7 9 3 2 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 21000000100000000000000000110 1 2 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 3 2 0 0 0 0 0 0 0 100001001000000101 2 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 3 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

4 8 7 7 8 9 0 1

1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 10001 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0

TABLE A4 Cemetery data for preliminary model (200 burials) A 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

B 12 52 55 22 27 2 47 66 6 78 2 6 4 1 1 3 3 32 7 4 71 71 4 22 4 3 30 56 27 79 58 68 48 4 28 26 73 15 3 19 3 23 28 60 56 70 4 69 4 1 4 2 43 46 74 2 31 1 2 67

C F M M F F F F M M M F M M F F F M F M M F F M M F F F M M M M M F F M M M F F M M M M M M M F M M F F F F F F F F M M F

DEF12145221219112322632133344 9 5 6 5 2 2 0 3 1 5 7 9 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 3 2 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 2 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 1 2 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 32000000010000000100000011000 3 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 2 2 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 3 2 0 0 0 0 100100001 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 3 2 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 2 2 0 0 0 0 0 1010000100100100011 3 1000010100001 0 0 0 0 2 2 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 3 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 3 1000000100001 0 0 0 0 2 2 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 3 2 0 0 0 0 100100001001010100001 31000010000000000000000010000 1 2 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 10000001 0 0 0 0 0 0 0 0 0 3 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 3 2 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 1 3 2 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 2 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 2 2 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 2 2 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 3 10000001 0 0 0 0 0 0 0 0 0 3 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 1 2 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 1 2 1000000100000100100000010101 3 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 2 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 2 2 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 2 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 2 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 3 2 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 1 32000000000001000000000010000 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 100001 0 0 0 0 0 0 0 1 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 1 2 2 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 2 2 0 11010011000010000010011001 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12010001011000010000001001001 2 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 3 1000010000001 0 0 0 0 2 2 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0

4 8 7 7 8 9 0 1

0 0 0 0 0

0 1 0 0 1

0 0 0 0 0

0 1 0 0 0

0 0 0 0 0

0 1 1 0 0

0 1 1 1 1

1 1 1 0 0

1 0 0 0 0

0 0 0 0 0

0 0 0 1 0 0 0 0 0

0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0

0 1 0 1 0 0 0 0 0

0 1 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0

0 1 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 1

1 1 1 1 0 1 1 1 1 1 1 0 1 0 0 1 0 0 1 0 1 10001 1 0 0 0 1001 1 0 0 1 1 0 0 1 1 0 1 1

1 0 0 0 1 0 0 0 0 0 0 0 0 1 1

0 0 0 0 0 1 1 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 1 1 0 0 0 0 0

1 0 1 1 1 1 1101 0 0 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0

0 0 0 0 100001 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1

1 0 1 1 1 0 0 1 0 0 1 1 0 1 1 0 1 1 0 10001 1 1 0

0 0 0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 0 0 0 0

0 1 1 1001 0 0 0 1 0 1 1 0 0

0 0 1 0 0 0 0 0 0

0 0 0 1 1

0 0 0 1 0

0 0 0 0 0

1 0 0 0 1

0 0 0 0 0 0 101

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0

0 0

0 0

0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 11101 0 0 0 1 0 1 1 1 0 0 0

308

Appendix 3 TABLEA4 (Continued) A

B

C

DEF 121452212191 5 6 5 2 2 9

61 62 63 64 65 66 67 68 69 70

8 78 4 66 38 67 58 51 2 63 4 5 9 2 3 69 70 14 57 2 65 3 4 3 68 74 27

F F

3 2 3 3 3 2 2 2 3 2 3 3 3 3 2 2 1 3 2 3 3 3 2 3 1 3 2 3 3 2 1 2 3 2 2 3 3 2 3 2 3 3 3 2 1 2 3 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3 3 3 1 2 3 3 2 2 1 3 3

71

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135

10

3 18 31 66 57 50 60 1 3 21 6 4 2 63 11 73 62 37 3 42 1 1 58 33 22 6 30 1 3 1 46 71 66 69 37 3

M M

F M M

F M

F M M

F M

F F F M M

F F M

F M

F F F F F F F F M M

F F M

F F F F F M M

F M M M M M M M

F M M

F M

F M M

F M M M

10 F 1 M

55 35 3 11 1 22 36 28 4

F F M

F F F M M M

2 2 1 2 1 2 2 2 1 2 2 2 1 2 2 2 1 1 2 1 2 1 1 1 2 1 2 2 1 1 1 2 2 1 1 1 2 1 2 1 1 1 2 1 2 2 1 2 2 1 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 1 2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0

0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 1 1 1 0 0 0 1 1 0 0 0 0 1 1 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 1 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 0 1 1 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0

0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

12 3 2 2 6 3 2 13 3 3 4 4 031579 48778901 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0

0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

309

0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 1 1 0 1

1 1 0 1 1 0 1 0 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 1 1 1

1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE A4 (Continued) A 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B 46 7 31 3 31 78 33 3 68 72 67 43 56 8 77 55 2 63 8 56 58 26 57 29 30 37 69 4 33 48 58 34 74 24 36 69 21 24 51 1 69 76 79 52 74 4 73 23 59 21 1 3 33 66 33 58 2 20 37 61 36 55 3 4 3

C F F M M F M F F F F F M F F F M F M M F F F F F M M M F M F F F M F M F M M M F M F M M M M M M M M M M F F F F M M M F F M M M F

DEF12145221219112322632133344 9 5 6 5 2 2 0 3 1 5 7 9

4 8 7 7 8 9 0 1

2 1 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 3 2 0 0 0 0 10010001 0 0 0 0 0 0 0 0 0 0 12010011 0 0 0 0 0 0 0 10110001 0 2 2 0 1 1 0 1 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 2 101001010000001 0 0 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 3 2 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 1 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 3 2 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 3 2 0 1100001 0 0 0 0 0 0 0 0 0 0 0 10111 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 1 0 1 0 1 2 1 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 3 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 2 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 3 1000010000001 0 0 0 0 0 0 0 0 0 0 1 2 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 1 0 2 2 0 1001 0 0 0 0 0 0 0 0 10101 0 0 0 0 3 2 0 1101001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 2 0 0 101 1 0 0 0 0 0 0 0 1000100001 3 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 3 10000001000001 0 0 0 0 0 0 0 0 0 2 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 3 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 2 2 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 12001011001000010000001001 1 2 1 0 0 1 0 1 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 1 2 10110001010000001 0 0 0 0 0 0 1 2 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 1 0 0 0 0 2 2 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 1 2 2 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 2 2 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1 2 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 3 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 2 2 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 0 1 0 0 3 1000010100001 0 0 0 0 0 0 0 0 0 0 3 2 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 3 2 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 10000001 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 1001 0 0 0 0 0 0 0 100000010001 1101001010100001010000101 3 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 2 2 0 1001001 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 1001 1000000100010100001 3 2 0 0 0 0 0 0 0 10001 0 0 0 0 0 0 0 0 0 0 3 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 2 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 2 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 12000111000010000101000101 3 100000010000100010000001 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 0 0 101 0 1 0 0 0 0 1 0 0 0 0 0 1 1 1 0 101 0 1001 1 1 0 0 0 1 1 1 1 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 11 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 0 101 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0

101 0 1 0 0 1 0 0 0 0 1 0 11 0 1001

0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0

TABLE AS Cemetery data for preliminary model (400 burials) A 1 2 3 4

B 4 15 1 60

C M M M F

DEF12145221219112322632133344 9 5 6 5 2 2 0 3 1 5 7 9 2 1 3 2

1 2 2 1

0 0 0 0

0 0 0 1

0 1 0 0

0 0 0 0

0 1 0 0

1 1 0 0

1 0 0 0

0 1 1 0

0 0 0 0

0 0 0 0

0 1 0 0

1 0 0 0

0 0 0 0

0 0 0 1

0 0 0 0

1 1 0 1

0 1 0 0

0 1 0 1

4 8 7 7 8 9 0 1 0 0 0 0

0 0 0 1

0 0 0 0

310

0 0 0 1

1 0 0 0

1 1 1 0

0 1 1 0

0 0 0 0

0 0 0 0

Appendix 3 TABLEAS (Continued) A

B

5 6 7 8 9

37 M 71 M 34 M

10 11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

72 73 74 75 76 77 78

C

10 M M

66 15 14 61 2 1 26 53 2 24 15 3 62 7 51 64 2 4 43 21 66 47 1 19 9 4 2 44 24 3

F F F F F M

F M M

F M

F F M

F F M M

F F M

F M

F F F F F

M 10 F

25 71 11 66 60 21 49 21 3 45 60 68 27 66 4 61 25 2 2 57 28 2 1 2 1

F F F F F M M M

F F F M

F M

F F M M M M

F M

F M

F

10 M 2 M

40 63 4 53 21 73 31 63 64 67 2 29

F M M M

F M M M M M

F F

DEF 121452212191 5 6 5 2 2 9 1 1 1 3 2 3 2 2 3 3 3 2 3 3 2 3 3 3 2 2 2 3 1 3 2 1 3 2 3 3 3 2 3 3 3 2 1 3 2 2 3 2 2 3 2 2 2 2 1 3 2 3 3 3 2 2 3 3 3 3 3 3 1 1 3 2 3 2 1 2 2 2 3 3

1 2 1 2 2 2 2 2 1 2 2 2 1 2 1 2 1 1 1 1 1 2 1 2 1 1 1 2 2 1 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 1 2 1 1 2 2 2 2 1 1 2 1 2 2 2 2 1 1 2 1 1 2 2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 0 1

1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 1 1 1 1 0 1

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0

1 0 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0 0 0 1 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0 1 1 1 1 1 1

1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0

1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0

12 3 2 2 6 3 2 13 3 3 4 4 031579 48778901 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 1 1 0 0 1 0 0 1 0 0 1 0 1 0 1 1 0 0 1 0 0 1 0 1 1 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 1 1 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0

0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0

0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

311

0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 0

1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1 1 0 1 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 0 0 1 0 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 0 0

0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 1 1 0

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1

0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE A5(Continued) A 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153

B 58 2 39 34 3 54 30 33 57 53 2 73 24 56 4 78 1 21 6 38 3 4 6 73 50 2 3 31 1 58 30 40 22 60 75 62 5 16 4 2 3 30 3 3 45 74 2 6 16 67 33 69 41 8 32 51 1 38 45 67 56 5 69 48 3 74 3 3 1 2 67 61 5 1 1

C F F F F F M M F M F F M M F M F F M F M F M F M M M F M F F F F F M M F M F F F F F F M M F M M M M F M F M F M M M F F F F M F F F F F M M M M M M F

DEF12145221219112322632133344 9 5 6 5 2 2 0 3 1 5 7 9

4 8 7 7 8 9 0 1

3 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 2 1000001 0 0 0 0 0 0 0 0 0 0 0 0 1000101 1 2 1 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 0 2 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 3 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 2 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 2 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 3 2 0 0 0 0 0 0 0 100001 0 0 0 0 0 0 0 0 0 1 0 0 0 2 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 2 2 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 3 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 2 2 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 1 0 3 10000101 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1101 3 2 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 3 100001 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 2 2 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 2 2 0 0 0 0 0 0 0 10000100000100011001 2 1000001000001 0 0 0 0 0 0 0 0 0 0 0 1 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10001101 2 2 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 1 0 2 2 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 3 1000000100001 0 0 0 0 0 0 0 0 0 0 101 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 10010000001 0 0 0 12001100000010000000001011000 3 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 11 1 0 2 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 3 2 0 110100100001 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 2 2 0 0 0 0 1101 1 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 1 1 0 1 1 1 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 3 2 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 2 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 0 1 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 11 0 0 2 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 101 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 101 3 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1101 1 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 1 3 10000001000001 0 0 0 0 0 0 0 0 0 0 0 1 0 3 2 0 0 0 0 1001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 1000000100001 0 0 0 0 0 0 0 0 0 0 1001 2 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 2 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 3 10110101 0 0 0 0 0 0 0 0 10100001101 2 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 2 2 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 10001 3 2 0 1101001 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 2 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 3 10000001 0 0 0 0 0 0 0 0 10000001 0 0 0 2 1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 2 100100100100000010001001 1 0 0 3 2 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 2 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 0 1 0 0 0 3 1000000100000100100000011 0 0 3 2 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 1 3 2 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1001 0 0 0 0 0 0 0 0 0 1 2 2 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 3 100000010000010010000001 1 0 0 3 2 0 0 0 0 0 0 0 100001 0 0 0 0 0 0 0 0 0 0 1 1 0 3 2 0 0 0 0 1001 0 0 0 0 0 0 0 10000001 1 1 0 3 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1101 1 1 0 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 2 2 0 0 1 1 1 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 101 0

312

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Appendix 3 TABLEAS (Continued) A

B

C

DEF 121452212191 5 6 5 2 2 9

154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227

56 57 51 70 12 52 29 1 41 3 26 77 1 4 3 20 69 41 58 62 74 28 42 43 4 19 62 27 45 4 49 66 24 65 53 22 2 73 2 12 3 71 23 37 39 32 21 30 54 43 68 3 3 3 68 12 4 4 65 21 16 20 3 54 1 56 32 3 42 58 58 57 32 2

M

2 2 3 3 3 3 1 3 3 3 2 2 3 3 3 2 3 2 1 3 3 2 2 2 2 3 2 1 2 2 3 2 3 2 2 2 3 2 3 3 3 2 3 1 3 2 1 2 1 2 1 2 3 2 2 3 3 2 2 3 2 1 3 3 2 1 1 3 3 2 3 1 3 3

F F M

F M

F M M M

F M

F M

F M

F M

F M M M

F F M M M M M M M

F M M M

F M M M M M

F M

F M

F F F M

F M

F M M M M

F M

F M M

F M

F F M

F F M M

F F F M

1 2 1 2 1 2 2 2 1 2 2 1 1 1 1 1 1 1 1 2 1 2 2 2 1 1 1 1 1 1 1 1 2 2 1 2 2 1 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 1 1 1 2 1 1 2 2 2 1 2 1 2 1 2 1 2 2 1 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 1 0 1 1 0 0 0 1 1 1 0

0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 1 1 0 0 0 1 1 1 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 1 1 1 0 1 1 1 1 0 0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1

0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0

1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0

0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0

12 3 2 2 6 3 2 13 3 3 4 4 031579 48778901 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1

0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0

0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 0 1 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 1 1 1 0 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 1 1 1 1 0 1 0 1 0 0 1 1 1 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0

0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0

313

0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 1 1 0 0 0

0 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0 1 0 1 1 0

1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 0 0

0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE AS (Continued) A

228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301

B

49 42 37 40 4 34 63 2 42 62 44 4 23 27 1 55 32 27 1 75 53 25 67 31 4 68 24 75 4 29 7 31 74 23 1 31 60 19 2 54 38 1 63 8 63 35 50 74 44 27 49 3 29 52 2 10 55 60 51 73 24 1 27 1 20 16 30 23 63 11 1 54 52 47

C

F M F M F F M F F F M F F F F F M F F F M M F M F F M M M M M F M F M M M M F F M F F M F M M M F M M M M F M M F F F F M M M F M M F F M M M M M F

DEF12145221219112322632133344 9 5 6 5 2 2 0 3 1 5 7 9

4 8 7 7 8 9 0 1

2 2 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 2 2 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 2 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 1 0 3 2 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1000101 3 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 3 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1001000000101 2 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 3 100001 0 0 0 0 0 0 0 0 0 0 100010111 1 2 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 3 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 101001 1 10000001 0 0 0 0 0 0 0 0 2 2 0 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 3 100000010000010010000001 3 2 0 1001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 1000000100100100011101 2 1 0 1 1 0 1 1 1 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 2 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 2 2 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 3 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 3 2 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 2 1 0 1 1 1 1 0 0 0 1 1 0 0 0 1 1 1 1 0 0 1 0 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 3 101 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 100000010001 0 0 0 0 0 0 12000010011000010100001011 2 1 0 1 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 2 2 0 0 0 0 0 0 0 1000001010100000111 2 10000111000010001001 0 0 0 2 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 2 2 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 2 2 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 3 10000001000001 0 0 0 0 0 0 0 0 0 2 2 0 1001001 100000010001001101 3 2 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 10010000001 2 1 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 3 2 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 2 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 0 0 1 1 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 2 2 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 2 2 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 3 2 0 0 0 0 1001 0 0 0 0 0 0 0 10000001 2 2 0 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 1 0 1 1 1 3 2 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1100001 0 0 0 0 0 0 0 0 0 0 101011011 3 2 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 2 2 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 2 100000100000100000010001 1 1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 2 2 0 1 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 2 2 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 2 101001010100001000100001001 3 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 3 2 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 3 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 100000011 3 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 2 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 1 0

314

0 1 1 0

0 0 0 1

0 0 0 0

0 1 0 0 1 1 1 1 0 0 1 0 0 1111 1 1 0 1 0 0 1 0 101 1 0 0 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0

0 0 1 1 1 1 0 0 0 1 0 1

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1

1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 1 1 1 1 1 1 101

0 1 0 1

0 0 0 0 0

0 1 0 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1 1101 0 0 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 1 1 0 1 1 0 1 0 0

0 0 1 0 0 0 0 0 1 0 0 0 0 0

0 1 1 1 1 0 1 0 0 1 1 1 0 0 1 0

0 0 0 0 0 0

1 1 0 0 0 0 0 1 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Appendix 3 TABLEAS (Continued) A

B

C

DEF 121452212191 5 6 5 2 2 9

302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375

13 67 26 54 4 71 6 8 44 59 21 47 51 51 29 22 53 13 30 63 4 2 1 39 29 16 1 61 3 14 14 75 2 51 9 2

F F

2 3 2 3 3 2 3 3 1 2 2 3 2 3 3 2 3 3 3 3 3 3 2 1 1 2 3 1 3 3 3 2 2 1 3 3 3 3 1 2 3 3 2 2 2 1 3 3 2 2 1 1 2 3 2 3 2 2 3 2 2 3 2 3 1 2 2 1 2 2 2 2 3 1

M M

F F F F F M

F F F M M

F M M M M M

F M M M M

F F F M

F M M

F F F 10 F 2 M 40 M 66 F 7 M 4 F 70 M 22 M 61 M 28 F 75 F 69 M 3 F 34 M 28 F 73 M 41 F 2 F 6 M 22 M 26 F 60 F 1 F 3 F 28 F 26 F 71 M 11 F 42 F 38 F 34 M 16 M 60 M 45 F 24 M 31 M 3 M 26 M

1 2 2 2 1 2 2 1 2 2 2 2 1 1 2 1 2 2 2 2 2 1 2 1 1 1 1 1 1 2 1 2 2 1 2 2 1 1 2 1 1 1 2 2 2 2 1 2 2 1 2 1 1 2 1 1 2 2 2 1 1 2 1 2 2 2 2 2 1 2 1 2 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 0 1 1 1 0 0

0 1 1 1 0 0 0 0 0 1 0 1 0 1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 1

0 0 1 1 1 0 1 0 0 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 1 0 1 0 1 1 1 0 1

0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 1 0 1 0 1

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0

0 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1

0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1

12 3 2 2 6 3 2 13 3 3 4 4 031579 48778901 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 1 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 1 1 1 0 1 1 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1

315

0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 1 1 1 1 1

1 0 0 0 1 1 1 0 0 1 0 1 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 1 0 1 1 0 1 1 1 0 0 1 0 1 1 1 1 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 1

1 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0

1 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 1 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE AS (Continued)

A

376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400

B

C

12 26 71 1 28 47 42 7 37 4 30 3 30 76 13 3 38 2 67 3 72 22 76 33 1

M F M M M M M F M F M M M F F F M F F M M F F M M

DEF12145221219112322632133344 9 5 6 5 2 2 0 3 15 7 9

4 8 7 7 8 9 0 1

3 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 11000011001000010011001 2 1 0 0 1 1 10 10 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 1 1 10 0 0 10 0 0 0 1 0 0 0 0 3 2 0 0 10 10 0 0 0 0 0 0 0 0 0 1 0 0 0 1 3 2 0 1 0 0 10 0 1 0 0 0 0 0 0 0 0 0 1 0 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0 10010010001101 3 2 0 0 0 0 10 0 1 0 0 0 1 0 0 0 1 0 0 0 0 3 2 0 0 0 0 0 0 0 100001 0 0 0 0 0 0 0 3 2 0 0 10 10 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 10 1 1 0 0 0 0 10 0 0 0 0 0 1 0 0 3 1 0 1 0 0 10 10 0 0 0 0 0 0 0 1 0 0 0 1 3 2 0 0 0 0 100100001 0 0 0 0 0 0 0 2 100000110000010000010000101 12 0 0 1 1 0 1 0 1 0 0 10 0 0 0 1 10 0 0 3 2 0 0 0 0 0 0 0 100001 0 0 0 0 0 0 0 3 2 0 0 10 0 0 0 0 0 0 0 0 10 0 1 0 0 0 1 3 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 2 2 0 0 10 10 0 1 0 0 10 0 0 0 1 0 0 0 0 2 2 0 11010011000010101000011 2 2 0 1 0 0 0 0 0 0 10 0 0 0 1 0 1 0 0 0 0 2 2 0 1 10 0 1 0 1 0 0 10 0 0 0 0 0 0 0 0 3 1000010000001 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 1 0 1

1 0 0 0 1 1 1

0 0 0 0 1 1 0

0 0 0 1 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0

10 11 10 0 0 0 1 0 1 10

1 0 0 0 0 0 0

1 0 0 0 0 1 0

0 0 0 0 0

0 0 0 0 1

0 1 1 0 0

0 0 0 0 0 1 101 1 1 0 0 0 0 10 1 1 0 0 10 0 1 0 0 101 1 1 0 0 0 1

TABLE A6 Cemetery data for model lA

A

1 2 3 4 5 6 7 8 9 10 11

12 13 14 15 16 17 18 19 20 21

CE

B

D

28M

2

4

1

M

54F 48F 2 4

44F 47 M 3

M

21 F 59 M 65F 7

1 3

0

2 2

0 0

3 3 1 3 3

0 0

2

0

2 1 2

0

2

2 3

0 0

2 F M

M

40F 78F

F

1 1 1

2 1 1 1 2 1 1 1

0

0 0 0

0 0

72F

2

2

0

40M 33F 60F 56F 66F

2 1 1 2 2

2 3 3 3

0 0 0 0 0

22

2

23 24 25 26 27 28 29 30 31 32

39 F M M F 49 F 17 M 41 F

2 2 2 2 2 2 2 2

3 2 3 2 2 3 1 2

0 0 0 0 0 0 0 0

1

33

34 35 36 37 38 39 40 41 42

F

3 9 3

F

1

1

3

0

37 M

1

1

1

2 F

2 2 2 1 2 1 1 2 1 1 1

3 1 1

0 0 0

2

0

3

0

3

0

2

0

1

0

2

0

1 1

0

37 M 41 F 26M 2 1

F M

27 M 18 F 64F 43 M 55M

0

42522 9 1 1 0 1 1 0 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1

0 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1

0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1

1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

1122211113622913 92287 3574 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1

0 0 1 1 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1 0 1

0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0

0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

56 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

316

0 1 0 1 1 1

0 1

05 1 0 1 0 0 0 1 0

0 1 0 0 0 1

0 0

1 0 1

1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1

1 0 0 0 1 0 1 1 0 1 1 1

0 1 0 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 1 0 0 1

0 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0

1 0 1 1 0 1 0 1 1

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0

1 1 0 1

0 1

1 1 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Appendix 3 TABLEA6 (continued) A

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116

B

4 66 17 62 8 2 7 66 1 57 26 41 40 26 3 3 5 30 15 35 4 2 4 37 5 3 3 29 73 32 29 58 66 1 8 15 16 73 4 25 2 56 58 73 3 11 62 68 71

46 25 1 2 26 67 4 49 40 6 1 8 16 9 1 72 77 36 3 2 59 36 4 3 65

C

M M M M F M M M F M F F M M M F M M M M F F M F M F M F F M F F F F F M F M M M F F F M F M M F M F F F F M F F F M M F F F M F M M F M M F F F M M

E

2 2 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 2 1 2 1 1 2 1 2 1 2 1 1 1 2 2 2 2 1 2 2 1 1 2 1 2 1 2 2 2 1 2 1 2 2

D

3 2 2 3 3 3 3 2 3 3 3 1 3 2 2 3 3 2 2 1 2 2 3 3 3 2 3 3 2 2 3 2 2 3 3 1 2 2 3 3 3 3 2 1 3 3 2 2 3 3 1 2 3 2 3 3 2 2 2 3 3 2 2 3 2 1 1 2 3 1 2 2 3 1

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913 92 2 8 7 3 5 7 4 9 1 0 1 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 1 0 0 1

0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0

0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0

317

0 0 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0

1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 1 1 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1

0 5 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0

0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0

1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 0 0 0 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA6 (continued) A

117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190

B

4 59 55 4 2 49 2 54 60 42 4 62 64 44 45 7 25 35 1 78 21 54 40 39 48 7 74 22 4 43 1 2 56 73 47 1 42 32 2 64 2 43 60 34 4 58 19 2 38 49 67 35 4 40 65 2 32 37 15 53 1 6 40 47 63 3 1 69 3 9 2 35 30 4

C

F F F F M F M F M F F F F F M M F M F M F M F F F F M F F F M F M F F F F M F M F F F F F M F M M M F M M F M M F F F F F F F F M M M F M M M M M M

E

1 1 2 1 2 2 2 1 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 2 1 2 1 2 1 1 1 1 2 1 1 2 1 1 1 2 1 1 2 2 1 1 2 2 1 2 2 1 1 2 1 1 1 1 2 1 2 2

D

3 2 3 3 3 2 3 2 3 3 3 2 2 2 2 3 2 2 3 1 2 3 2 2 2 3 2 3 2 2 3 3 3 1 1 3 1 3 3 2 3 1 1 1 3 1 2 3 2 3 1 3 3 1 2 3 2 1 3 2 3 3 1 2 1 3 3 1 3 3 3 1 3 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913 92 2 8 7 3 5 7 4 9 1 0 1 1 0 0 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 1 0 0 1 0 0 0 1 1 0

0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0

0 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0

0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

318

1 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 1 1 1 1 0 1 0 0

0 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 1 1

0 5 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1

1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0

Appendix 3 TABLEA6 (continued) A

191 192 193 194 195 196 197 198 199 200

B

1 2 45 28 29 1 72 57 3 74

CED

F

M 1 M 2 M 1 M 1 M 1 M 1 F 1 F 1 M 2 M 2

3 3 1 3 1 2 3 1 3 3

0 0 0 0 0 0 0 0 0 0

42522 9 1 0 0 1 1 1 0 1 1 0 1

0 0 0 0 0 0 1 0 0 1

0 0 1 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0

1122211113622913 92287 3574 0 0 1 1 1 0 0 0 0 1

0 0 1 0 1 0 0 0 0 0

0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0

0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 1 0 0

0 0 1 0 0 0 0 0 0 1

56 0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 1 0 0 0 0

1 0 1 1 1 1 1 1 0 0

05 0 1 0 0 0 0 0 0 1 1

1 1 0 0 0 1 0 0 1 0

0 0 0 0 0 0 0 0 0 0

1 0 1 1 1 1 1 1 0 0

TABLEA7 Cemetery data for model 2A A 1 2 3

4 5 6

7 8 9

10 11

12 13 14 15 16 17 18 19 20 21 22

23 24 25 26 27 28 29 30 31 32 33

34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

B 2

CED

G

F

M

1

3

2

0

22F 36M 46 M

2 2

1 1

1 2

0 0

F 74 M 1 M 74 F

2 1 2 2 2

2 3 2 3 1

1 2 1 2 2

0 0 0 0 0

21 M 52 F 9 F 2 M 30M 75F

1 2 2 2 2 2

2 1 2 3 2 2

2 1 2 2 2 2

0 0 0 0 0 0

77 M

2

2

1

0

18 F 42 M 60 M

1 2 2

2 2 2

2 2 2

0 0 0

3 1

M F

1 2

2 3

1 2

0 0

76 M 42 M 28F

2 1 1

2 2 3

2 2 2

0 0 0

5

0

1

M

1

3

2

16 M

1

1

1

1

3

F

2

3

2

0

1

F

2

3

2

0

60M 40 M 37 F

1 2 2

1 2 2

1 1 2

0 0 0

4

2

F

61 M 43F

2

1

0

1 1 1220

1

1

4

F

1

3

2

0

2

F

2

3

2

0

44 M

1

1

2

0

33F 43F 12 F

2 2 2

1 2 3

2 2 2

0 0 0

3

2

3

2

0

21 F 1 F 26 M

F

1 2 1

2 1 2

1 1 2

0 0 0

5

1

2

2

0

1

3

2

0

2

2

1

0

75M 1 M 2 F

2 1 2

3 2 3

2 1 2

0 0 0

4

2

3

2

0

1

1

1

0

2

1

1

0

1

1

2

0

2

1

2

0

2

2

2

0

2

2

1

0

F

47F 7

F

F

55

13 F 8 M 20 M 43 M 45 M

56

3

F

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

319

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 1 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0

0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

3 5 74 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0 1 0 0 0

0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1

0 5 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0

1 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 1 0 0 1 1 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE A7 (Continued)

A

57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

B

CE

D

G

F

39 F 2 1 45M 1 2 24 F 2 1 26M2320 58 F 2 2 62 M 1 2 3F 1320 15 M 2 1 4F 2210 7F 1320 14 F 2 2 59 M 1 3 25F 2 3 71F 2 2 2Ml220 4F 2320 2F 1320 27 M 1 3 14 F 1 2

2

0

2 1

0 0

2 2

0 0

2

0

8

F

0 0

2 2

0 0

2 2

0 0

3

2

0

2 M 1 2 lF 2320 79M2320 25M 2 1 3 M 1 3 62 F 2 3 lF 1320 67 F 2 3 2F 2210 57 M 2 1 20 M 1 2 68F 2 1

2

0

2 2 2

0 0 0

2

0

2

0

1 1

0 0

6

2

0

2 2 2 2 1

0 0 0 0 0

1 2

0 0

2 2 2 2 2 1 2

0 0 0 0 0 0 0

2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

M

1

1 2

1

2

40M2220 70 M 2 3 64 F 2 1 13 F 1 2 15 M 2 2 53 F 2 2 96 5M1320 97 11 M 2 2 98 45 F 1 2 99 38 M 1 3 100 34F 1 2 101 60 M 1 1 102 77 M 1 2 103 19 F 1 1 104 62M 1 1 105 77 F 2 3 1069F 1320 107 2 F 1 3 108 45F 2 2 109 1M2 11 110 4 F 1 3 111 40 M 1 2 112 30 M 2 1 113 64 F 1 2 114 18 F 2 2 115 17 F 2 2 116 30 M 2 2 117 52 M 1 2 118 28 M 1 2 119 44 F 2 2 120 70 F 2 2 121 23 M 1 3 122 52 F 1 1 123 68 M 2 3 124 50 F 1 1 125 74 F 1 2 126 15 M 1 2 127 3 F 2 2 128 30 M 2 3 129 23 F 1 1

2 1 2 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2 1

21211253323313784221122211113622913 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

320

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1

1 1 0 1 1 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 1 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0

0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0

0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 5 74 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0

1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0

0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 1 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1

1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 0

0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 1 0 1 0 1 1 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 0 1 0 1 1 1 1 0 0 1 0 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0

Appendix 3 TABLE A7 (Continued)

A

130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

72 60 69 56 50 63 66 35 2 45 55 2 43 49 4 63 54 4 57 39 57 39 2 25 4 2 18 71 1 23 43 26 26 16 70 23 1 22 2 14 3 44 65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32 1 30 17 75 50 76 34 25 51

CED

M 1 M 1 M 2 M 1 F 2 M 1 F 1 F 1 F 1 M 1 F 1 M 2 F 2 M 2 F 1 F 1 F 2 F 2 F 1 F 1 F 1 F 1 F 1 M 2 M 2 F 1 M 1 F 1 F 1 F 2 F 1 F 1 F 2 M 1 M 2 M 1 F 1 M 2 F 2 M 2 F 2 F 2 M 1 F 1 M 1 F 1 M 2 F 1 F 2 F 1 M 2 F 1 M 1 F 1 F 1 M 2 F 2 F 2 M 1 F 1 F 2 F 1 M 2 F 1 M 2 M 2 M 1 M 2 M 1 F 2 M 2

G

1 1 2 2 2 2 2 2 3 2 2 3 2 1 3 1 2 3 2 1 2 2 2 3 3 2 2 3 3 2 1 1 1 1 1 1 2 3 3 1 2 2 2 1 3 2 3 3 2 1 2 1 2 3 3 2 3 3 2 3 2 1 1 2 2 3 3 1 1 2 2

1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

321

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 1 0 1 0 1 1 1 0 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1

0 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 1

0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

3 5 74 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1

0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 0

0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1

0 5 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA8 Cemetery data for model 3A A

B

1 2 3 4 5 6 7

2 22 36 46 1 74 1 8 74 9 21 10 52 11 9

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

2 30 75 77 18 42 60 3

1 76 42 28 5

16 3

1 60 40 37 4

61 43 4

CE

D

M F M M F M M F M F F M M F M

1 2 2 2 1 2 2 2 1 2 2 2 2 2 2

F

1

M M M F M M F M M F F M M F F M F

2 2 1 2 2 1 1 1 1 2 2 1 2 2 2 1 1

F

1

21 1 26

F M F F F F F F M

2 1 2 2 2 2 1 2 1

44

5

F

1

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

47

2 4

F F M M F F

1 2 2 1 2 2

13

F

1

8

M M M M F F M F M F M

2 1 2 2 2 2 1 2 2 2 1

2 44

33 43 12 3

7

75 1

20 43 45 3

39 45 24 26 58 62 3 15

G

3

2

1 1

1

2 3 2 3

1 2

2

1 2

1 2 2 2

1

1

2 3 2 2 2 2 2 2 2 3 2 2 3 3

2 2 2 2

1 2 2 2

1 2 2 2 2 2

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1

1

1

3 3

2 2

1

1 1

0 0 0 0 0 0

2 2 2

1 2 3 3

1 1 2 3 3 2

1

2

1 1 2 2 2 2 2 2 2 2

1 1

2 2 3 2 3 2 3 3

2 2 2

1 1 1 1

1 1

2 2

1 2

1 2

1 2 2 2 2 2

1 2 2

1

1 2 2 2 2 2

F

1

3 2 2 3

M 4 F 7 F 14 F 59 M 25 F 71 F 71 2 M 72 4 F

2 2 1 2 1 2 2 1 2

2 3 2 3 3 2 2 3

1

1 2

1 2 2 2 2 2

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21211253323313784221122211113622913 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

322

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 0

0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 1 1 1 0 0

0 0 1 1 0 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 1 1 0 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

3 5 74 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0

0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0

0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0

5 6 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0

1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 1 0

0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1

0 5 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1

1 1 1 1 0 1 1 1 0 1 0 0 1 0 0 1 1 0 1 1 1 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 1

Appendix 3 TABLE AS (Continued)

A 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146

B

CED

G

F

2F 27 M 14 F

1320 1 3 1 2

2 2

0 0

8

1

2

0

2 2 2

0 0 0

2

0

2

0

1 1

0 0

2

2

0

40M 2 2 70 M 2 3 64F 2120 13 F 1 2 15 M 2 2 53 F 2 2 5M1320 11M2210 45 F 1 2 38M1320 34 F 1 2 60 M 1 1 77 M 1 2 19 F 1 1 62 M 1 1 77F 2 3 9 F 1 3 2 F 1 3 45 F 2 2 1 M 2 1 4 F 1 3 40 M 1 2 30 M 2 1 64 F 1 2 18 F 2 2 17 F 2 2 30 M 2 2 52 M 1 2 28 M 1 2 44 F 2 2 70 F 2 2 23 M 1 3 52 F 1 1 68 M 2 3 50 F 1 1 74 F 1 2 15 M 1 2 3 F 2 2 30 M 2 3 23 F 1 1 72 M 1 1 60 M 1 1 69 M 2 2 56 M 1 2 50 F 2 2 63 M 1 2 66 F 1 2 35 F 1 2 2 F 1 3 45 M 1 2 55 F 1 2 2 M 2 3 43 F 2 2 49 M 2 1 4 F 1 3 63 F 1 1 54 F 2 2

2 2

0 0

2 2 1

0 0 0

2

0

2 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

F

3

2M1220 lF 2320 79M2320 25M 2 1 3 M 1 3 62 F 2 3 lF 1320 67 F 2 3 2F 2210 57 M 2 1 20 M 1 2 68F 2 1 6

M

1

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

323

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0

0 1 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1

0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1

0 1 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

3 5 74 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 1 0 0 0 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 1 1 0 1 0 1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 1

5 6 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0

0 0 0 0 0 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1

0 5 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

1 0 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA8 (Continued) A

147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

4 57 39 57 39 2 25 4 2 18 71

1 23 43 26 26 16 70 23 1 22 2 14 3 44 65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32 1 30 17 75 50 76 34 25 51

CE

D

F F F F F F M M F M F F F F F F M M M F M F M F

2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 2 1 2 1 1 2 2 2 2

3 2 1 2 2 2 3 3 2 2 3 3 2 1 1 1 1 1 1 2 3 3 1 2

F

2

M F M F M F F F M F M F F M F F M F F F M F M M M M M F M

1 1 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 1 2 1 2 1 2 2 1 2 1 2 2

G

F

2 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2

1

0

2 1 3 2 3 3 2 1 2 1 2 3 3 2 3 3 2 3 2 1 1 2 2 3 3 1 1 2 2

2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21211253323313784221122211113622913 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1

0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1 1 1 0

0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 1

TABLEA9 Cemetery data for model 4A A

B

CE

D

F

1

28M

2

1

0

2 3 4 5

4

1

3

0

2 1

2 2

0 0

F M

1 1

3 3

0 0

44F 47 M

2

1

0

M

1 1

3 3

0 0

21 F 59M 65F

1 2 1

2 2 1

0 0 0

6 7 8 9 10

11

12

M

54F 48F 2 4 3

42522 9 1 1 0 1 0 0 0 0 1 0 1 0 1

0 0 1 0 0 0 0 0 0 0 1 1

1 0 0 0 0 0 1 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0

1122211113622913 92287 3574 1 0 0 0 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 1 0 0 1 0 0 1 0 1

0 0 1 1 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 1 0 0 1 0

56 0 0 1 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0

324

0 1 0 0 1 1 0 1 1 1 0 1

05 0 0 0 0 0 0 1 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0

1 1 0 0 1 0 0 1 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

3 5 74 0 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 0 1 0

0 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 0

0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1

0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 0

1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1

0 5 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 0 0

Appendix 3 TABLEA9 (Continued) A

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86

B

7 40 78 72 40 33 60 56 66 2 39 3 9 3 49 17 41 1 37 2 37 41 26 2 1 27 18 64 43 55 4 66 17 62 8 2 7 66 1 57 26 41 40 26 3 3 5 30 15 35 4 2 4 37 5 3 3 29 73 32 29 58 66 1 8 15 16 73 4 25 2 56 58 73

C

M

F F F M

F F F F F F M M

F F M

F F M

F M

F M

F M M

F F M M M M M M

F M M M

F M

F F M M M

F M M M M

F F M

F M

F M

F F M

F F F F F M

F M M M

F F F M

E

1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 1 1 2 1 1 1 2 2 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 2 1 2 1

D

2 2 3 2 2 3 3 1 3 3 2 3 2 2 3 1 2 3 1 3 1 1 2 3 3 2 1 2 1 1 3 2 2 3 3 3 3 2 3 3 3 1 3 2 2 3 3 2 2 1 2 2 3 3 3 2 3 3 2 2 3 2 2 3 3 1 2 2 3 3 3 3 2 1

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913 92 2 8 7 3 5 7 4 9 1 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 1 0 1 0 1 0 1

0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 1 0 0 1 1

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 1 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 0 1 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1

0 1 0 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

325

0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0

0 0 1 0 1 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0

0 5 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0

1 1 0 0 0 1 1 0 0 0 0 1 1 1 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0 1 0 1 0 1 1 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA9 (Continued) A

87 88 89 90 91 92 93 94 95 96

97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160

B

3 11 62 68 71

46 25 1 2 26 67 4 49 40 6 1 8 16 9 1 72 77 36 3 2 59 36 4 3 65 4 59 55 4 2 49 2 54 60 42 4 62 64 44 45 7 25 35 1 78 21 54 40 39 48 7 74 22 4 43 1 2 56 73 47 1 42 32 2 64 2 43 60 34

C

F M M F M F F F F M F F F M M F F F M F M M F M M F F F M M F F F F M F M F M F F F F F M M F M F M F M F F F F M F F F M F M F F F F M F M F F F F

E

1 2 1 2 1 2 1 1 1 2 2 2 2 1 2 2 1 1 2 1 2 1 2 2 2 1 2 1 2 2 1 1 2 1 2 2 2 1 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 2 1 2 1 2 1 1 1 1

D

3 3 2 2 3 3 1 2 3 2 3 3 2 2 2 3 3 2 2 3 2 1 1 2 3 1 2 2 3 1 3 2 3 3 3 2 3 2 3 3 3 2 2 2 2 3 2 2 3 1 2 3 2 2 2 3 2 3 2 2 3 3 3 1 1 3 1 3 3 2 3 1 1 1

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913 92 2 8 7 3 5 7 4 9 1 0 0 0 0 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1

0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1

0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

326

1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 1 1 1

0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0 0 0 0

0 5 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0

0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1

Appendix 3 TABLEA9 (Continued) A

161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

4 58 19 2 38 49 67 35 4 40 65 2 32 37 15 53 1 6 40 47 63 3 1 69 3 9 2 35 30 4 1 2 45 28

29 1 72

57 3 74

C

F M F M M M F M M F M M F F F F F F F F M M M F M M M M M M M M M M M M F F M M

E

2 1 1 2 1 1 1 2 1 1 2 2 1 1 2 2 1 2 2 1 1 2 1 1 1 1 2 1 2 2 1 2 1 1 1 1 1 1 2 2

D

3 1 2 3 2 3 1 3 3 1 2 3 2 1 3 2 3 3 1 2 1 3 3 1 3 3 3 1 3 3 3 3 1 3 1 2 3 1 3 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913 92 2 8 7 3 5 7 4 9 1 0 1 1 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 0 1

0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1

0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0

0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 0 0

0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1

0 5 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 1 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 0

TABLEAlO Cemetery data for model 4B A 1 2 3 4 5 6 7 8 9 10

11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

B 28 4 54 48 2 4 44

47 3 21 59 65 7 40 78 72 40 33 60 56 66 2 39 3 9 3 49 17

C M M F F F M F M M F M F M F F F M F F F F F F M M F F M

E 2 1 2 1 1 1 2 1 1 1 2 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2

D

1 3 2 2 3 3 1 3 3 2 2 1 2 2 3 2 2 3 3 1 3 3 2 3 2 2 3 1

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

425221122211113622913 92 2 8 7 3 5 7 4 9 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

327

0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0

0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 1 0 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 1 1 1 1 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE AlO (Contimued)

A

29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

B

CE

41 F lF 37 M 2 F 37 M 41 F 26M 2F

2 130 1 2 2 2

1

1 1 2 1 1 1

M

1

2

M

2

7M130 66M220 1 F 1 57 M 1 26 F 1 41 F 1 40M130 26M220 3

M

3F 5M130 30M 15 M 35 M 4F 2F 4M130 37 F 5

M

3F 3M130 29F 73F 32 M 29F 58 F 66F

F

2

0

1 3 1 1 2

1 0 0 0 0

3 2

0 0

230

27 M 18 F 64F 43 M 55M 4 M 2 66M220 17 M 1 62M 1 8F 130

1

1

0

1

0 0

2

0

0 1 0 1 0

3

0

1

3

0

3 3 3

0 0 0 0

3

1

2

0

2 2

0 0

1

0

220 220

0 0 0 0 0 1 0 0 0 0 0 0 0 1 1

0 0 0 0

2

3

0

1

1

3

0

0 0 0 0 0 0 0 0 0 0 0 0

120

0

2

0

3

0

1 2

0 0

2

0

3

0

25 M 2F 56F 58 F 73M

2 3 230 1 3 2 2 1 1

0

3

8F 15 M 16 F 73M

0 0 0 0 1 1

0

2

F

1

0

0 0 0 0

1

0 0 1 0

1

130 1 1 1

42522 9 1

2 1

3 2 2 3

1 2 1 1 1 1 1

230 1 1 1 1

1

1

3

0

11M230 62 M 1 68F 2

2 2

0 0

0 0 0 0 1 0 0 0 0 0 0

71 M

1

3

0

1

46 F 25 F lF

2

3

0

1

1

0

0 0 0 0 1 0 0 0

4

2

M

F

F

26 M 97 67 F 98 4 F 99 49 F 100 40M 101 6 M 102 1 F 96

D

0 0 0

120 1

3

0

2 2 2 2

2 3 3 2 2 2 3

0 0 0 0 0 0 0

1

2 2

1

0 0

1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1122211113622913 92287 3574 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1

0 1 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1

0 0 0 0 0 0 0 0 0 0

1 0 1

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 1

0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 1

0 0 0 0 0 1

0 0

1 0 1

0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0

56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

328

0 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0

05 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1

0 1 1 1 1

0 1 0 0 0 0 0 1

0 0 0 0 0 0 0 0 1 0 0 1

0

1 0 1

0 10 1 0 1 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1

0 0 0 0 1

0 0 1

0 1

0 0 1 1 1

0 1

0 1

0 1 1

0 0 0 0 0 0 0 1 1 1

1 1 1 1

0 0 0

Appendix 3 TABLE AlO (Contimued) A

103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176

B

8 16 9 1 72

77 36 3 2 59 36 4 3 65 4 59 55 4 2 49 2 54 60 42 4 62 64 44

45 7 25 35 1 78 21 54 40 39 48 7 74 22 4 43 1 2 56 73 47 1 42 32 2 64 2 43 60 34 4 58 19 2 38 49 67 35 4 40 65 2 32 37 15 53

C

F F M F M M F M M F F F M M F F F F M F M F M F F F F F M M F M F M F M F F F F M F F F M F M F F F F M F M F F F F F M F M M M F M M F M M F F F F

E

1 1 2 1 2 1 2 2 2 1 2 1 2 2 1 1 2 1 2 2 2 1 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 2 1 2 1 2 1 1 1 1 2 1 1 2 1 1 1 2 1 1 2 2 1 1 2 2

D

3 2 2 3 2 1 1 2 3 1 2 2 3 1 3 2 3 3 3 2 3 2 3 3 3 2 2 2 2 3 2 2 3 1 2 3 2 2 2 3 2 3 2 2 3 3 3 1 1 3 1 3 3 2 3 1 1 1 3 1 2 3 2 3 1 3 3 1 2 3 2 1 3 2

F

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913 92 2 8 7 3 5 7 4 9 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

5 6 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

329

1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 1 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1

0 5 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 1 0 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEAlO (Contimued) A

B

177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

CE

1 6 40 47 63 3 1 69 3 9 2 35 30 4 1 2 45 28 29 1 72 57 3 74

F F F F M M M F M M M M M M M M M M M M F F M M

D

1 2 2 1 1 2 1 1 1 1 2 1 2 2 1 2 1 1 1 1 1 1 2 2

3 3 1 2 1 3 3 1 3 3 3 1 3 3 3 3 1 3 1 2 3 1 3 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

42522 9 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1

0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1

0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1122211113622913 92287 3574 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

56 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0

05 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 1 1 1 0 0

TABLEAll Cemetery data for model 4C C

E

D

G

F

2 12 1 12 5 3 3 2 3 3 13 7 8 4 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5

M 22 F 36 M 46 M

1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 1 2 2 1 2 2 1 1 1 1 2 2 1 2 2 2 1 1 1 2 1 2 2 2 2 1 2 1 1

3 1 1 2 3 2 3 1 2 1 2 3 2 2 2 2 2 2 2 3 2 2 3 3 1 3 3 1 2 2 2 1 2 3 3 1 1 2 3 3 2 1 2 2

2 1 2 1 2 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2 1 1 2 1 1 2 2 2 2 2 2 2 2 1 1 2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

A

B

1

2

2 3 4 5 6 7 8 9 10

1 1

M

74 F 21 M 52 F

11 9

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

F

74 M

F

2 M 30 M 75 F 77 M 18 F 42 M 60 M 3 M 1

F

76 M 42 M 28 F 5

M

16 M 3 1

F F

60 M 40 M 37 F 4

F

61 M 43 F 4 F 2 F 44 M 33 F 43 F 12 F 3 F 21 F 1 F

26 M

44 5

F

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

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330

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1 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 0

Appendix 3 TABLE All A

B

C

45 47 F 467F 47 75 M 481M 49 2 F 504F 51 13 F 52 8 M 53 20 M 54 43 M 5545M 563F 57 39 F 5845M 59 24 F 6026M 61 58 F 6262M 633F 64 15 M 654F 667F 67 14 F 6859M 6925F 7071F 712M 724F 732F 74 27 M 75 14 F 76 8 F 772M 781F 7979M 8025M 81 3 M 8262F 831F 84 67 F 852F 86 57 M 87 20 M 88 68F 89 6 M 9040M 91 70 M 9264F 93 13 F 94 15 M 95 53 F 965M 9711M 98 45 F 9938M 100 34 F 101 60 M 102 77 M 103 19 F 104 62 M 105 77F 106 9 F 107 2 F 108 45 F 109 1 M 110 4 F 111 40 M 112 30 M 113 64 F 114 18 F 115 17 F 116 30 M 117 52 M 118 28 M

(Continued)

E

D

G

F

2 12 1 12 5 3 3 2 3 3 13 7 84 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5

1 3 2210 2 3 1210 2 3 2320 1 1 2 1 1 1 2 1 2220 2210

2

0

2

0

2

0

1 1 2 2

0 0 0 0

2

2

0

1

0

2

0

2

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1

0

2 2

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2

0

2

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1

1220 2 1 2320 2 2 1220 1320 2 1 2210 1320 2 2 1320 2320 2220 1220 2320 1320 1 3 1 2 1

3

1220 2320 2320 2120 1 3 2320 1320 2

3

2210 2

1

2

0

1 2

2 1

1 1

0 0

1

2

2

0

2

0

2 2 1

0 0 0

2

0

2 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 1 2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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Theoretical and Quantitative Approaches to the Study of Mortuary Practice TABLE All A

119

120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191

B

(Continued)

C

44 70 23 52 68 50 74 15 3 30 23 72

60 69 56 50 63 66 35 2 45 55 2 43 49 4 63 54 4 57 39 57 39 2 25 4 2 18 71

1 23 43 26 26 16 70 23 1 22 2 14 3 44 65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32

F F M F M F F M F M F M M M M F M F F F M F M F M F F F F F F F F F M M F M F F F F F F M M M F M F M F

F M F M F M F F F M F M F F M F F M F F F

E

D

G

F

2 12 1 12 5 3 3 2 3 3 13 7 8 4 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5

2

2

1

0

2 1 1 2 1 1 1 2 2 1 1 1 2 1 2 1 1 1 1 1 1 2 2 2 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 2 1 2 1 1 2 2 2 2 2 1 1 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 1 2 1

2 3 1 3 1 2 2 2 3 1 1 1 2 2 2 2 2 2 3 2 2 3 2 1 3 1 2 3 2 1 2 2 2 3 3 2 2 3 3 2 1 1 1 1 1 1 2 3 3 1 2 2 2 1 3 2 3 3 2 1 2 1 2 3 3 2 3 3 2 3 2 1

1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1

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1

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1 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1

Appendix 3 TABLEAll A

B

192 193 194 195 196 197 198 199 200

(Continued)

C 1 30 17 75 50 76 34 25 51

M F M M M M M F M

E

D

G

F

2 1 2 2 1 2 1 2 2

1 2 2 3 3 1 1 2 2

1 2 2 2 2 2 2 1 2

0 0 0 0 0 0 0 0 0

2 12 1 12 5 3 3 2 3 3 13 7 84 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 384460 10 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5 1 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0

0 1 0 0 1 0 1 0 0

0 0 0 1 1 0 0 0 0

0 0 1 0 1 1 1 0 1

0 0 0 0 0 1 1 0 0

0 0 1 0 0 1 1 0 1

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0

0 1 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 1

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0

1 0 0 1 0 0 0 1 1

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

1 1 1 0 1 0 1 0 1

TABLEA12 Cemetery data for model 4D A

B

C

E

D

G

F

1 2 3 4 5 6 7 8 9

2 22 36 46 1 74 1 74 21 52 9 2 30 75 77 18 42 60 3 1 76 42 28 5 16 3 1 60 40 37 4 61 43 4 2 44 33 43 12 3 21 1 26 5 47 7 75 1 2 4 13 8 20 43 45 3 39 45 24

M F M M F M M F M F F M M F M F M M M F M M F M M F F M M F F M F F F M F F F F F F M F F F M M F F F M M M M F F M F

1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 1 2 2 1 2 2 1 1 1 1 2 2 1 2 2 2 1 1 1 2 1 2 2 2 2 1 2 1 1 1 2 2 1 2 2 1 2 1 2 2 2 2 1 2

3 1 1 2 3 2 3 1 2 1 2 3 2 2 2 2 2 2 2 3 2 2 3 3 1 3 3 1 2 2 2 1 2 3 3 1 1 2 3 3 2 1 2 2 3 2 3 2 3 3 1 1 1 1 2 2 1 2 1

2 1 2 1 2 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2 1 1 2 1 1 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 1 2 2 1 1 2 2 2 1 2 2 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

10

11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

2 12 1 12 5 3 3 2 3 3 13 7 84 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 384460 10 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

333

0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 0 0

0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 1 1 0 1 0 0 1 1 0 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA12 (Continued) A

B

C

E

D

G

F

2 12 1 12 5 3 3 2 3 3 13 7 8 4 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5

60 61 62 63 64 65 66 67 68 69 70

26 58 62 3 15 4 7 14 59 25

M F M F M F F F M F F M F F M F F M F M M M F F F F M M F M M M F F M F M M F M F M M F M F F F F M F M M F F F M M M F F M F M F F M F M

2 2 1 1 2 2 1 2 1 2 2 1 2 1 1 1 1 1 2 2 2 1 2 1 2 2 2 1 2 1 2 2 2 1 2 2 1 2 1 1 1 1 1 1 1 2 1 1 2 2 1 1 2 1 2 2 2 1 1 2 2 1 1 2 1 1 1 2 2

3 2 2 3 1 2 3 2 3 3 2 2 3 3 3 2 3 2 3 3 1 3 3 3 3 2 1 2 1 2 2 3 1 2 2 2 3 2 2 3 2 1 2 1 1 3 3 3 2 1 3 2 1 2 2 2 2 2 2 2 2 3 1 3 1 2 2 2 3

2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

71 71 2

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

4 2 27 14 8 2 1 79 25 3 62 1 67 2 57 20 68 6 40 70 64 13 15 53 96 5 97 11 98 45 99 38 100 34 101 60 102 77 103 19 104 62 105 77 106 9 107 2 108 45 109 1 110 4 111 40 112 30 113 64 114 18 115 17 116 30 117 52 118 28 119 44 120 70 121 23 122 52 123 68 124 50 125 74 126 15 127 3 128 30

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

334

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

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0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 1 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 0 1 0 1 1 1 1 0 0 1 0 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 1 1 1

Appendix 3 TABLEA12 (Continued) AB 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

CED 23 72 60 69 56 50 63 66 35 2 45 55 2 43 49 4 63 54 4 57 39 57 39 2 25 4 2 18 71 1 23 43 26 26 16 70 23 1 22 2 14 3 44 65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32 1 30 17 75 50 76 34 25 51

F M M M M F M F F F M F M F M F F F F F F F F F M M F M F F F F F F M M M F M F M F F M F M F M F F F M F M F F M F F M F F F M F M M M M M F M

G 1 1 1 2 1 2 1 1 1 1 1 1 2 2 2 1 1 2 2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 2 1 2 1 1 2 2 2 2 2 1 1 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 1 2 1 2 1 2 2 1 2 1 2 2

1 1 1 2 2 2 2 2 2 3 2 2 3 2 1 3 1 2 3 2 1 2 2 2 3 3 2 2 3 3 2 1 1 1 1 1 1 2 3 3 1 2 2 2 1 3 2 3 3 2 1 2 1 2 3 3 2 3 3 2 3 2 1 1 2 2 3 3 1 1 2 2

1 1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2

F 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

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0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

335

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0

0 0 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

3 5 74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1

0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 1 0 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 1 0 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA13 Cemetery data for model SA A

1 2 3 4 5 6 7 8 9 10

11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

72 73 74 75

B 28 4 54 48 2 4 44 47 3 21 59 65 7 40 78 72 40 33 60 56 66 2 39 3 9 3 49 17 41 1 37 2 37 41 26 2 1 27 18 64 43 55 4 66 17 62 8 2 7 66 1 57 26 41 40 26 3 3 5 30 15 35 4 2 4 37 5 3 3 29 73 32 29 58 66

C M M F F F M F M M F M F M F F F M F F F F F F M M F F M F F M F M F M F M M F F M M M M M M F M M M F M F F M M M F M M M M F F M F M F M F F M F F F

E 2 1 2 1 1 1 2 1 1 1 2 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 1 1 2 1 1 1 2 2 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1

D

1 3 2 2 3 3 1 3 3 2 2 1 2 2 3 2 2 3 3 1 3 3 2 3 2 2 3 1 2 3 1 3 1 1 2 3 3 2 1 2 1 1 3 2 2 3 3 3 3 2 3 3 3 1 3 2 2 3 3 2 2 1 2 2 3 3 3 2 3 3 2 2 3 2 2

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

42522 9 1 1 0 0 1 0 0 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1

1 0 1 0 0 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 1 1

1 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1122211113622913 92 2 8 7 3 5 7 4 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 0

0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 1 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0

336

0 1 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1

0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 5 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 0 0 1 0 0 1 1 0 1 1 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0

Appendix 3 TABLEA13 (Continued) A

76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149

B

1 8 15 16 73 4 25 2 56 58 73 3 11 62 68 71

46 25 1 2 26 67 4 49 40 6 1 8 16 9 1 72 77 36 3 2 59 36 4 3 65 4 59 55 4 2 49 2 54 60 42 4 62 64 44

45 7 25 35 1 78 21 54 40 39 48 7 74 22 4 43 1 2 56

C

F F M F M M M F F F M F M M F M F F F F M F F F M M F F F M F M M F M M F F F M M F F F F M F M F M F F F F F M M F M F M F M F F F F M F F F M F M

E

1 2 1 1 1 1 2 2 1 2 1 1 2 1 2 1 2 1 1 1 2 2 2 2 1 2 2 1 1 2 1 2 1 2 2 2 1 2 1 2 2 1 1 2 1 2 2 2 1 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1

D

3 3 1 2 2 3 3 3 3 2 1 3 3 2 2 3 3 1 2 3 2 3 3 2 2 2 3 3 2 2 3 2 1 1 2 3 1 2 2 3 1 3 2 3 3 3 2 3 2 3 3 3 2 2 2 2 3 2 2 3 1 2 3 2 2 2 3 2 3 2 2 3 3 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913 92 2 8 7 3 5 7 4 9 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 1 1 1 0 0 1

0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 1 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0

0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0

0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1

0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

337

1 0 0 1 0 1 0 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 1 0 0

0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0

0 5 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0

1 0 0 1 0 1 1 0 0 1 0 0 0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 1 0 1 0 1 0 1 1 1 1 0 0 1 0 0 1 1 1 0 1 0 0 1 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA13 (Continued) A

150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

73 47 1 42 32 2 64 2 43 60 34 4 58 19 2 38 49 67 35 4 40 65 2 32 37 15 53 1 6 40 47 63 3 1 69 3 9 2 35 30 4 1 2 45 28 29 1

CE

D

F

F F F F M F M F F F F F M F M M M F M M F

2 2 2 1 2 1 2 1 1 1 1 2 1 1 2 1 1 1 2 1 1

1 1 3 1 3 3 2 3 1 1 1 3 1 2 3 2 3 1 3 3 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

M

2

2

0

M F F F F F F F F M M M F M M M M M M M M M M M M 72 F 57 F 3 M 74 M

2 1 1 2 2 1 2 2 1 1 2 1 1 1 1 2 1 2 2 1 2 1 1 1 1 1 1 2 2

3 2 1 3 2 3 3 1 2 1 3 3 1 3 3 3 1 3 3 3 3 1 3 1 2 3 1 3 3

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

42522 9 1

1 1 0 1 1 1 1 0 0 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1

1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1122211113622913 92287 3574

1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0

1 1 0 1 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1

1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1

56

1 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0

05

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 0 0 1 0

0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0 1 0 0 1 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1

TABLEA14 Cemetery data for model 5B A

B

CE

D

G

F

1

2

1

3

2

0

2 3 4 5

22F 36M 46M

2 2 2

1 1 2

1 2 1

0 0 0

1

6 7 8

74 M

1 2 2 2

3 2 3 1

2 1 2 2

0 0 0 0

2 1 2 3

2 1 2 2

0 0 0 0

2

2

0

9 10

11

12 13 14

1

M

F M

74 F 21 M 1 52 F 2 9 F 2 2 M 2 30M2220 75 F 2

21211253323313784221122211113622913 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 1 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 1 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0

0 1 0 0 0 1 0 1 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 1 0 1 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 1 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0

338

0 0 0 1 0 1 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0

0 0 1 0 1 0 0 1 1 0 0 1 1 0

0 0 1 0 0 0 0 1 1 0 0 0 0 1

0 0 0 0 0 0 0 1 1 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 1 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0

3 5 74 0 0 0 0 0 0 0 1 0 0 0 0 1 1

0 0 0 0 0 0 0 1 0 0 0 0 0 1

0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 1 1 0 0 1 1 0 0 0 0 1 1

0 5 1 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 1 0 0 0 0 0 1 0 0 0

0 1 0 1 0 1 1 1 1 1 1 0 1 0

Appendix 3 TABLEA14 (Continued) A

15 16 17 18 19 20 21 22

23 24 25 26 27 28 29 30 31 32 33

B

CED

G

F

77 M 18 F 42 M 60M

2

2

1

0

1 2 2

2 2 2

2 2 2

0 0 0

3 1

M F

1 2

2 3

1 2

0 0

76 M 42 M 28F

2 1 1

2 2 3

2 2 2

0 0 0

5

M

1

3

2

0

16 M

1

1

1

1

3

F

2

3

2

0

1

F

2

3

2

0

60M 40 M 37 F

1 2 2

1 2 2

1 1 2

0 0 0

4

2

2

1

0

61 M 43F

F

1 1

1 2

1 2

1 0

4

F

1

3

2

0

2

F

2

3

2

0

44 M

1

1

2

0

33F 43F 12 F

2 2 2

1 2 3

2 2 2

0 0 0

3

34 35 36 37 38 39 40 41 42 43

2

3

2

0

21 F 1 F 26 M

1 2 1

2 1 2

1 1 2

0 0 0

44

5

1

2

2

0

45 46 47 48 49 50 51 52 53 54

47F

1

3

2

0

2

2

1

0

75M 1 M 2 F

2 1 2

3 2 3

2 1 2

0 0 0

4

2

3

2

0

1

1

1

0

2

1

1

0

1

1

2

0

2

1

2

0

2

2

2

0

3

F 39 F 45 M

2 2 1

2 1 2

1 2 2

0 0 0

24 F 26 M 58F 62 M

2 2 2 1

1 3 2 2

1 2 2 2

0 0 0 0

55

56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

7

F

F F

F

13 F 8 M 20 M 43 M 45M

1

3

2

0

15 M

3

F

2

1

2

0

4 7

2 1

2 3

1 2

0 0

14 F 59M 25F 71F 2 M

2 1 2 2 1

2 3 3 2 2

1 2 2 2 2

0 0 0 0 0

72

4

F

2

3

2

0

73 74 75 76

2

F

1

3

2

0

27 M 14 F

1 1

3 2

2 2

0 0

8

F

1

3

2

0

77

2

M

1

2

2

0

78 79 80 81 82 83 84 85 86 87

1

F

2

3

2

0

79M 25M

2 2

3 1

2 2

0 0

3

F F

M

1

3

2

0

62 F

2

3

2

0

1

F

1

3

2

0

67 2 57 20

F

2

3

2

0

F

2

2

1

0

M

2

1

2

0

M

1

2

1

0

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1

1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

339

1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 0 1 1 1 1 1 0 1 0 0 0 1 0 0 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1 1 0 1 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0

0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0

0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

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0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0

3 5 74 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0

0 0 1 1 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0

0 5 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0

0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0

1 0 1 1 0 0 0 1 1 1 0 1 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 1 1 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA14 (Continued) A

B

CE

D

88 68F 2 1 89 6 M 1 2 90 40M2220 91 70 M 2 3 92 64F 2120 93 13 F 1 2 94 15 M 2 2 95 53 F 2 2 96 5 M 1 3 97 11 M 2 2 98 45 F 1 2 99 38M 1 3 100 34F 1 2 101 60 M 1 1 102 77M 1 2 103 19 F 1 1 104 62M 1 1 105 77 F 2 3 1069F 1320 107 2 F 1 3 108 45F 2 2 109 1M2 11 110 4 F 1 3 111 40 M 1 2 112 30 M 2 1 113 64 F 1 2 114 18 F 2 2 115 17 F 2 2 116 30 M 2 2 117 52 M 1 2 118 28 M 1 2 119 44 F 2 2 120 70 F 2 2 121 23 M 1 3 122 52 F 1 1 123 68 M 2 3 124 50 F 1 1 125 74 F 1 2 126 15 M 1 2 127 3 F 2 2 128 30 M 2 3 129 23 F 1 1 130 72 M 1 1 131 60 M 1 1 132 69 M 2 2 133 56 M 1 2 134 50 F 2 2 135 63 M 1 2 136 66 F 1 2 137 35 F 1 2 138 2 F 1 3 139 45 M 1 2 140 55 F 1 2 141 2 M 2 3 142 43 F 2 2 143 49 M 2 1 144 4 F 1 3 145 63 F 1 1 146 54 F 2 2 147 4 F 2 3 148 57 F 1 2 149 39 F 1 1 150 57 F 1 2 151 39 F 1 2 152 2 F 1 2 153 25 M 2 3 154 4 M 2 3 155 2 F 1 2 156 18 M 1 2 157 71 F 1 3 158 1 F 1 3 159 23 F 2 2 160 43 F 1 1

G

F

1

0

2

0

2

0

2 2 1

0 0 0

2 1

0 0

2 2 2 2 2 2 1 2

0 0 0 0 0 0 0 0

2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 1 2 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 1 1 2 2 2 1 2 2 2 2

21211253323313784221122211113622913 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

340

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0

0 0 1 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 1 1

0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0

0 0 0 1 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 5 74 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1

0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1

0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1

0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1

5 6 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0

1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0

0 5 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0

1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 0 1 0 0 1 1

Appendix 3 TABLEA14 (Continued) A

B

161 162 163 164 165 166 167 168 169 170

26 26 16 70 23 1 22 2 14 3

171

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

CED

F F M M M F M F M F

G

1 2 1 2 1 1 2 2 2 2

1 1 1 1 1 2 3 3 1 2

44 F

2

65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32 1 30 17 75 50 76 34 25 51

1 1 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 1 2 1 2 1 2 2 1 2 1 2 2

M F M F M F F F M F M F F M F F M F F F M F M M M M M F M

F

1 2 2 2 1 2 2 2 1 1

0 0 0 0 0 0 0 0 0 0

2

1

0

2 1 3 2 3 3 2 1 2 1 2 3 3 2 3 3 2 3 2 1 1 2 2 3 3 1 1 2 2

2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 0 1 1 0 0 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0

0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 1 0 0 1 1 0 0 1 0 1 0 1 0 1 1 0 0

0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 0 0

0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0

0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

3 5 74 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1 0 1 0 0

0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 1

0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0

5 6 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0

0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0

0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 0 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 1 1 1 1 0 0

TABLEA15 Cemetery data for model 6A A 1 2 3 4 5 6 7 8 9 10

11

B

CED

G

F

46 M 14 M 68F 24 F

1 2

2 3

3 3

0 0

1 1

2 1

3 2

0 0

3

M

2

2

1

0

3 M 61 M 28M 70 M

2 1 1 1

3 1 1 3

3 2 2 3

0 0 0 0

1

2

1

2

0

M

22F

2330

12 13 14 15 16 17 18 19 20 21

46F 73Ml 34 F 3 M 35F 56M 60F 4

2

50 M

2

22

5

M

23 24 25 26

66F 62 F 26 M 19 F

2

F

F

2

3

3

0

1

1 2 111

0

1

1

2

0

2 3 2130 1 1 1 1

3

0

2 2

0 0

1

2

0

1

2

0

2

3

3

0

1 1 1 2

1 1 2 3

2 2 3 3

0 0 0 0

212112533233137842211222111136229133 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

341

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 1 0 0 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 1

0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1

1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0

0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0

1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 5 74 0 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 0 0 1 1 0 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1 1 0 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

1 0 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 1 1 0 0 0 1 1 1 0

0 1 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 0 0 0 1

0 5 6 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0

1 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0

0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA15 (Continued) A 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

97 98 99

100 101

B

CE

D

G

F

13 F 1 69M 1 26 M 1 25M1210 4 F 2 1 M 1 2 M 2 79M 1 42F 2

3 3 3

3 3 3

0 0 0

3 2 1 1 1

3 3 2 2 2

0 0 0 0 0

2

1

2

0

3 M 2 1 6F 1210 36 M 2 1 71F 1 1 25 M 2 1 lF 2230 7F 2330 65 M 1 1 24 M 1 3 4F 2330 4F 2230 20 F 2 2 46F 1 3 79F 1 1 23 M 1 1 16 M 2 1 64M 1 1 71F 2 2 68F 2 3 3F 2330 55 M 2 2 16 F 1 3 45M 1 1 44F 1 2 26 F 2 1 36 M 1 2 25M 1 2 44 M 1 1

M

2

0

1 2 2

1 0 0

2

0

3

0

3 3 3 2 2 3 3 3

0 0 0 0 0 0 0 0

3 3 2 3 2 1 3 2

0 0 0 0 0 0 0 0

4 1

1 1

2 2

0 0

18 F 40F 32 F 54M 6F 62 M 30F 52 F 50 F 40 F

1 2 1 1 1 1 1 3 1330 1 2 2 1 2 2 1 3 2 1

3 2 2 3

0 0 0 0

3 2 3 3 2

0 0 0 0 0

1

1

M M

M

1

2 2

3

3

0

56F 1 2 71M 1 1 79F 1 1 30 F 1 1 16 F 1 1 2 M 2 3 2F 1330 31 F 2 3

3 3 1 1 3 3

0 0 0 0 0 0

3

0

9 M 11 M

3 3

0 0

1 2

3 2

24 F

1

2

1

0

4

M

2

1

2

0

2 M 70 M 26F 32 F 74 M 43 F 48M

2 2 2 1 1

2 2 2 2 1

3 1 1 3 2

0 0 0 0 0

1

1

2

0

1

2

3

0

4

M

2

1

2

0

28 M 76 M 72F 30 F

2 2 2 2

1 1 1 2

3 3 2 3

0 0 0 0

212112533233137842211222111136229133 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

342

0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0

0 1 1 0 0 0 1 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0

0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

3 5 74 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 0 0 0 1 1 1 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0

1 1 1 1 0 1 0 1 0 1 0 1 0 1 0 0 0 1 1 0 0 0 1 1 1 0 1 0 0 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0

0 0 0 0 1 0 1 0 1 0 1 0 1 0 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 0 1 0 1 1 1 1 0 0 0 0 1 1 1 1 1

0 5 6 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0

1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 1 0 0 1 1 0 1 1

0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0

Appendix 3 TABLEA15 (Continued) A

B

CED

G

F

102 4 M 1 103 12 F 2 1041M1330 105 78M 1 106 16 M 1 107 38 M 1 108 53 M 2 109 25F 1 110 2 M 1 111 75 M 1 112 67 M 2 113 4 M 2 114 60 M 2 115 40 M 1 116 23 M 2 117 49 F 1 118 61 F 1 119 41 F 2 120 46 F 2 121 3 F 1 122 41 F 2 123 72 M 1 124 36 M 1 125 13 F 2 126 54 M 1 127 1 M 2 128 73 F 2 129 1 M 1 130 41 M 2 131 4 F 2 132 35 F 1 133 59 M 2 134 46 M 2 135 72 M 1 136 31 F 1 137 4 M 1 138 20 F 2 139 41 M 1 140 30 M 2 141 1 M 1 142 4 M 2 143 8 M 1 144 4 F 1 145 18 F 2 146 2 F 1 147 62 M 2 148 34 F 1 149 24 F 1 150 5 M 1 151 15 M 2 152 38 F 1 153 4 F 2 154 2 M 1 155 4 M 2 156 22 M 1 157 2 F 2 158 1 F 2 159 62 M 1 160 23 F 2 161 1 F 1 162 13 M 1 163 48 F 2 164 2 F 2 165 26 F 2 166 22 F 1 167 7 M 2 168 28 F 1 169 9 F 1 170 1 F 1

1 2

2 3

0 0

1 2 3 2 1 1 2 2 1 1 2 3 2 2 3 1 1 2 3 1 3 1 1 1 3 1 3 2 3 1 1 1 1 2 3 3 3 3 1 2 2 3 3 1 3 1 1 1 3 1 3 2 1 2 3 3 2 2 3 1 2 1 3 1 1 3

2 3 3 3 2 2 3 3 2 3 1 3 3 3 3 1 2 1 3 3 3 2 2 3 3 1 3 1 3 2 2 2 2 3 3 3 3 3 1 1 3 3 3 2 3 2 3 1 3 1 3 1 2 3 3 3 1 1 3 2 1 2 3 1 2 3

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

171

4

M

1

2

3

0

172 173 174 175

78 54 70 67

F M F M

2 1 1 2

1 1 2 1

1 2 3 3

0 0 0 0

212112533233137842211222111136229133 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 1 1 1 0 0 0 0 0 1 1 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 1 1 0 1 1 1 0 1 1 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

343

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0

1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 0 1 1 1

0 0 0 0 1 1 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

0 0 0 1 1 1 1 0 0 1 1 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

0 0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

3 5 74 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0

5 6 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0

1 0 1 1 1 1 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 1 1 0

0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0 1 1 0 1 1 0 1 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1

0 5 6 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0

1 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 1 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 0 1 0 1 1 1 0 1 1

0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA15 (Continued) A

B

176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195

5 70 33 44 4 1 69 11

4 14 47 15 59 22 51 34 66 32 13 28 74 46 3 1

196

197 198 199 200

11

CE

F M M M M F M M F M M M F M F M F F F M M F F M M

D

2 1 1 1 1 1 1 1 1 2 1 1 1 2 2 1 1 2 1 1

3 1 3 1 1 1 1 2 1 2 2 1 2 1 1 1 3 1 3 2

2

2 2 1 2

G

F

3 2 3 3 2 1 3 1 2 1 3 3 3 2 1 1 3 3 3 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

2

1

0

1 3 3 3

2 3 3 3

0 0 0 0

212112533233137842211222111136229133 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0

0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 1 1 0 0 0 1 0 0 0

0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0

0 1 1 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1

0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 0 1 0 0 1 1

1 0 1 0 1 0 0 1 0 1 1 0 1 0 1 1 1 1 1 1 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 1 1 1 0 1 1

TABLEA16 Cemetery data for model 7A AB

CE

1

28

2

4

3

4 5

54 48 2

6

4

7

44

47 3 10 21 11 59 12 65 13 7 14 40 15 78 16 72 17 40 18 33 19 60 20 56 21 66 22 2 23 39 24 3 25 9 26 3 27 49 28 17 29 41 30 1 31 37 32 2 33 37 34 41 35 26 36 2 37 1 38 27 39 18 40 64 41 43 8

9

M M F F F M F M M F M F M F F F M F F F F F F M M F F M F F M F M F M F M M F F M

D

2 1 2 1 1 1 2 1 1 1 2 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 1

1 3 2 2 3 3 1 3 3 2 2 1 2 2 3 2 2 3 3 1 3 3 2 3 2 2 3 1 2 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1

1

1

2 2 2 1 2 1 1 2 1 1

3 1 1 2 3 3 2 1 2 1

0 0 0 0 0 0 0 0 0 0

42522 9 1 1 0 1 1 0 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1

0 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0

1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

112221111362291333344 92287 3574 56 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1

0 0 1 1 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0

0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0

0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

344

0 1 0 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 1 1

0578901 1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1 0 0

0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0

1 1 0 0 0 1 1 0 0 1 1 1 0 0 1 0 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0

0 0 0 0 0 1 1 1 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1

0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1

3 5 74

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0

5 6

0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 0 1 1 0 0 0 1 0

1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 1 1 1 0 1

0 5 6

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0

1 1 1 0 0 1 0 0 1 1 1 0 1 1 1 0 1 0 0 0 0 0 1 1 0

0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0

Appendix 3 TABLEA16 (Continued) A B

42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71

55 4 66 17 62 8 2 7 66 1 57 26 41 40 26 3 3 5 30 15 35 4 2 4 37 5 3 3 29 73 32 29 58 66 1 8 15 16 73 4 25 2 56 58 73 3 11 62 68

72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 71 92 46 93 25 94 1 95 2 96 26 97 67 98 4 99 49 100 40 101 6 102 1 103 8 104 16 105 9 106 1 107 72 108 77 109 36 110 3 111 2 112 59 113 36 114 4 115 3

C

M M M M M F M M M F M F F M M M F M M M M F F M F M F M F F M F F F F F M F M M M F F F M F M M F M F F F F M F F F M M F F F M F M M F M M F F F M

E

1 2 2 1 1 1 2 1 2 1 1 1 1 1 2 1 1 1 1 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 2 1 2 1 1 2 1 2 1 2 1 1 1 2 2 2 2 1 2 2 1 1 2 1 2 1 2 2 2 1 2 1 2

D

1 3 2 2 3 3 3 3 2 3 3 3 1 3 2 2 3 3 2 2 1 2 2 3 3 3 2 3 3 2 2 3 2 2 3 3 1 2 2 3 3 3 3 2 1 3 3 2 2 3 3 1 2 3 2 3 3 2 2 2 3 3 2 2 3 2 1 1 2 3 1 2 2 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

4 2 5 2 2 112221111362291 92 2 8 7 3 5 7 4 9 1 1 0 1 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 1 0 0

0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 1 1 0 0 1 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

1 0 1 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

1 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0

1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

3 3 3 3 4 4 0 5 7 8 9 0 1

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0

345

1 0 0 1 1 1 0 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 1 0 0 0 1 0 1 0

0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 1 1 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1

0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1

0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0

1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 0 0

0 1 0 1 1 1 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 0 1 1 0

0 1 0 1 0 1 0 0 1 1 0 0 1 1 0 0 0 1 1 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 1 1 0 1 1 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 0 1 1 0 1 0 1 0 1 1 0 1

0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 0 0 0 1 1 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 1 1

0 1 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 1 1 1 1 1 0 0 1 0 1 1 1 1 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1

1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA16 (Continued) A B

116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189

C

65 4 59 55 4 2 49 2 54 60 42 4 62 64 44 45 7 25 35 1 78 21 54 40 39 48 7 74 22 4 43 1 2 56 73 47 1 42 32 2 64 2 43 60 34 4 58 19 2 38 49 67 35 4 40 65 2 32 37 15 53 1 6 40 47 63 3 1 69 3 9 2 35 30

M F F F F M F M F M F F F F F M M F M F M F M F F F F M F F F M F M F F F F M F M F F F F F M F M M M F M M F M M F F F F F F F F M M M F M M M M M

E

2 1 1 2 1 2 2 2 1 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 2 1 2 1 2 1 1 1 1 2 1 1 2 1 1 1 2 1 1 2 2 1 1 2 2 1 2 2 1 1 2 1 1 1 1 2 1 2

D

1 3 2 3 3 3 2 3 2 3 3 3 2 2 2 2 3 2 2 3 1 2 3 2 2 2 3 2 3 2 2 3 3 3 1 1 3 1 3 3 2 3 1 1 1 3 1 2 3 2 3 1 3 3 1 2 3 2 1 3 2 3 3 1 2 1 3 3 1 3 3 3 1 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 112221111362291 92 2 8 7 3 5 7 4 9 1 1 0 1 1 0 0 1 0 1 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 1 0 0 1 0 0 0 1 1

1 0 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0

0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0

1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 1 1 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0

3 3 3 3 4 4 0 5 7 8 9 0 1

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

346

0 1 1 0 1 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 1 1 1 1 0 1 0

1 0 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 1

0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0

0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0

1 1 1 1 0 0 1 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 0 0 1 0 1 1 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 1 0 1 1 1 0 1 1 1 0 1 0 1

0 0 1 0 1 1 0 1 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0

1 1 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 0 1 1 0 1 0 1 1 0 1 1 1 0 0 1 0 1 1 1 0 1 0

0 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 1 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 0 1 1 0 1 0 0 0 1 0 1

0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0

Appendix 3 TABLEA16 (Continued) A B

190 191 192 193 194 195 196 197 198 199 200

C

4 1 2 45 28 29 1 72 57 3 74

M M M M M M M F F M M

E

2 1 2 1 1 1 1 1 1 2 2

D

3 3 3 1 3 1 2 3 1 3 3

F

0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 112221111362291 92 2 8 7 3 5 7 4 9 1 0 0 0 1 1 1 0 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 1 0 0 0 0 1

0 0 0 1 0 1 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0

0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 1 0 0

0 0 0 1 0 0 0 0 0 0 1

0 0 0 0 0 0 0 1 1 0 0

3 3 3 3 4 4 0 5 7 8 9 0 1

5 6 0 0 0 0 0 0 1 0 0 0 0

0 1 0 1 1 1 1 1 1 0 0

1 0 1 0 0 0 0 0 0 1 1

1 1 1 0 0 0 1 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 0 0 0 1 1 1

1 1 0 0 0 0 1 1 0 0 1

0 0 1 0 0 1 0 0 0 1 0

1 1 1 1 1 1 1 0 0 0 0

1 1 1 1 1 0 0 1 0 1 0

1 1 0 1 0 1 0 1 1 1 0

TABLEA17 Cemetery data for model7B A

1 2 3 4 5 6 7 8 9 10

11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55

B

28 4 54 48 2 4 44 47 3 21 59 65 7 40 78 72 40 33 60 56 66 2 39 3 9 3 49 17 41 1 37 2 37 41 26 2 1 27 18 64 43 55 4 66 17 62 8 2 7 66 1 57 26 41 40

C

M M F F F M F M M F M F M F F F M F F F F F F M M F F M F F M F M F M F M M F F M M M M M M F M M M F M F F M

E

2 1 2 1 1 1 2 1 1 1 2 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 1 1 2 1 1 1 2 2 1 1 1 2 1 2 1 1 1 1 1

D

1 3 2 2 3 3 1 3 3 2 2 1 2 2 3 2 2 3 3 1 3 3 2 3 2 2 3 1 2 3 1 3 1 1 2 3 3 2 1 2 1 1 3 2 2 3 3 3 3 2 3 3 3 1 3

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 11222 11113622 92 2 8 7 3 5 7 4 9 1 1 0 1 1 0 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1 0 0 0 1 0 1 1 1 1

0 0 0 1 0 0 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 0

1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 1 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

0 0 1 1 0 0 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1

0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

5 6

9 133 3 3 4 4 4 4 4 4 4 0 5 7 8 9 0 12 3 4 5 6

0 1 0 1 1 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1

0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

347

1 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0

1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 0 1 1

0 0 0 1 0 0 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 0 1 0 0 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 1 0 0 0 0

0 1 0 1 1 1 0 1 0 0 1 0 1 1 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 0 0 0

1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 1 0 0 1

0 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 0

1 0 0 1 1 0 1 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1

1 0 1 0 1 1 0 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 0 1 0 1 1 0 1 0 1 1 0

1 1 0 0 1 0 0 0 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 1 1 1

1 0 1 1 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 1 0

0 0 1 1 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 1 0

1 0 0 1 0 0 1 1 0 1 0 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 0 1 1 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA17 (Continued) A

56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72

73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95

B

26 3 3 5 30 15 35 4 2 4 37 5 3 3 29 73 32 29 58 66 1 8 15 16 73 4 25 2 56 58 73 3 11 62 68 71

46 25 1 2 96 26 97 67 98 4 99 49 100 40 101 6 102 1 103 8 104 16 105 9 106 1 107 72 108 77 109 36 110 3 111 2 112 59 113 36 114 4 115 3 116 65 117 4 118 59 119 55 120 4 121 2 122 49 123 2 124 54 125 60 126 42 127 4 128 62 129 64

C

M M F M M M M F F M F M F M F F M F F F F F M F M M M F F F M F M M F M F F F F M F F F M M F F F M F M M F M M F F F M M F F F F M F M F M F F F F

E

2 1 1 1 1 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 2 2 1 2 1 1 2 1 2 1 2 1 1 1 2 2 2 2 1 2 2 1 1 2 1 2 1 2 2 2 1 2 1 2 2 1 1 2 1 2 2 2 1 2 2 2 1 2

D

2 2 3 3 2 2 1 2 2 3 3 3 2 3 3 2 2 3 2 2 3 3 1 2 2 3 3 3 3 2 1 3 3 2 2 3 3 1 2 3 2 3 3 2 2 2 3 3 2 2 3 2 1 1 2 3 1 2 2 3 1 3 2 3 3 3 2 3 2 3 3 3 2 2

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 112221111362291 92 2 8 7 3 5 7 4 9 1 1 0 0 0 1 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 1 1 0 0 1 0 1 1 0 0 1 0 1 1 1 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 0 1 0 1 0 1 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 1 0 1 1

3 3 3 3 4 4 4 4 4 4 4 0 5 7 8 9 0 12 3 4 5 6

5 6 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

348

0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 1 0 1 0 0 0 1 0 0 0 1 0

1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 1 1 1 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1

0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0

0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0

1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 1 0 0 0 0 1 1 1 0 0 1 1 1 0 0 1 0 0

1 1 1 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 1 0 0 0 1 1 0 0 1 1

0 0 0 1 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 0 1 0 1 0 1 1 1

0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 1 0 1 1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 0

0 1 1 0 0 0 0 0 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 1 1 1 1 1 1 0

1 1 1 1 0 1 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 0 0 0 1 1 1 0 0 1 0 0 1 1

1 1 1 1 1 1 1 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 0 0 1 0 1 1 1 0 1 1 0 1 0 0 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 1 0 1 1 0 0 1

0 1 0 0 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 1 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 0 1 0 1 1 0

0 1 1 1 1 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 0 1 1 1 1 1 0 1 1 0 1 1 1

0 1 1 1 0 1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1

1 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 0 0 0 0 1 0 1 1 0 0 1 1 0 0 0 1 1 1 1 1

Appendix 3 TABLEA17 (Continued) A

130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

C

44 F 45 M 7 M 25 F 35 M 1 F 78 M 21 F 54 M 40 F 39 F 48 F 7 F 74 M 22 F 4 F 43 F 1 M 2 F 56 M 73 F 47 F 1 F 42 F 32 M 2 F 64 M 2 F 43 F 60 F 34 F 4 F 58 M 19 F 2 M 38 M 49 M 67 F 35 M 4 M 40 F 65 M 2 M 32 F 37 F 15 F 53 F 1 F 6 F 40 F 47 F 63 M 3 M 1 M 69 F 3 M 9 M 2 M 35 M 30 M 4 M 1 M 2 M 45 M 28 M 29 M 1 M 72 F 57 F 3 M 74 M

E

D

F

1 2 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 2 1 2 1 2 1 1 1 1 2 1 1 2 1 1 1 2 1 1 2 2 1 1 2 2 1 2 2 1 1 2 1 1 1 1 2 1 2 2 1 2 1 1 1 1 1 1 2 2

2 2 3 2 2 3 1 2 3 2 2 2 3 2 3 2 2 3 3 3 1 1 3 1 3 3 2 3 1 1 1 3 1 2 3 2 3 1 3 3 1 2 3 2 1 3 2 3 3 1 2 1 3 3 1 3 3 3 1 3 3 3 3 1 3 1 2 3 1 3 3

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 2 5 2 2 1122211113622913333444 92 2 8 7 3 5 7 4 5 6 9 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 1 0 0 1 0 0 0 1 1 0 0 0 1 1 1 0 1 1 0 1

1 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 1

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0

0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0

1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1

1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

349

1 0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 0 0

0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 1 1

4 4 4 4 0 5 7 8 9 0 12 3 4 5 6 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 0 0 1 0 0 1 0

0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 1 1 1 0

1 0 0 0 1 1 1 1 0 1 1 0 1 1 1 0 0 0 1 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1

0 1 1 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 1 1 1 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 0 1 0 1 1

1 1 0 0 1 0 0 1 0 1 1 0 1 1 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0

1 1 1 1 0 1 1 0 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1

0 0 1 1 1 0 1 1 0 0 0 1 0 1 1 1 1 1 0 1 0 1 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 0

0 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 1 1 1 0 1 0 1 1 0 1 1 0 0 0 0 1 0 1 1 1 1 0

0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0

0 1 1 0 1 1 1 0 0 0 0 1 0 0 1 0 1 1 0 1 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0

1 0 0 0 0 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 1 0 1 0 1 1 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 1 1 1

0 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 1 0 0 0 1 1 1 0 1 1 0 0 0 1 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA18 Cemetery data for model 7C A

B

1 2 3 4 5 6 7

2 22 36 46 1 74 1 8 74 9 21 10 52 11 9

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

2 30 75 77 18 42 60 3

1 76 42 28 5

16 3

1 60 40 37 4

61 43 4 2

44

33 43 12 3

21 1 26

44 5

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

47 7

75 1 2 4

13 8

20 43 45 3

39 45 24 26 58 62 3 15 4

7 14 59 25

71 71 2 72

4

73 2 74 27

CE

M F M M F M M F M F F M M F M F M M M F M M F M M F F M M F F M F F F M F F F F F F M F F F M M F F F M M M M F F M F M F M F M F F F M F F M F F M

D

G

F

1 2 2 2 1 2 2 2 1 2

3 1 1 2 3 2 3 1 2 1

2 1 2 1 2 1 2 2 2 1

0 0 0 0 0 0 0 0 0 0

2

2

2

0

2

3

2

0

2 2

2 2

2 2

0 0

2 1

2 2

1 2

0 0

2 2

2 2

2 2

0 0

1 2

2 3

1 2

0 0

2

2

2

0

1 1 1

2 3 3

2 2 2

0 0 0

1

1

1

1

2 2

3 3

2 2

0 0

1

1

1

0

2

2

1

0

2

2

2

0

2

2

1

0

1

1

1

1

1 1 2 1 2

2 3 3 1 1

2 2 2 2 2

0 0 0 0 0

2

2

2

0

2 2 1 2 1 1 1 2 2 1 2 2

3 3 2 1 2 2 3 2 3 2 3 3

2 2 1 1 2 2 2 1 2 1 2 2

0 0 0 0 0 0 0 0 0 0 0 0

1

1

1

0

2 1 2

1 1 1

1 2 2

0 0 0

2

2

2

0

2 2 1 2 2

2 1 2 1 3

1 2 2 1 2

0 0 0 0 0

2

2

2

0

1 1 2 2 1 2 1 2

2 3 1 2 3 2 3 3

2 2 2 1 2 1 2 2

0 0 0 0 0 0 0 0

2

2

2

0

1 2 1 1

2 3 3 3

2 2 2 2

0 0 0 0

2121125332331378422112221111362291333344 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

350

0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 1 0 1 1 0 1 1 1 0 1 0 0 0 1 1 1 0 0 0 1

0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0

0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

3 5 74 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 1

0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1 0 0

0 5 7 8 9 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0

1 1 1 0 1 1 0 0 1 0 1 0 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 1 1 0 0 0 1 0

1 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 0 1 0 0 1 1 0 1 0 0 1 0

0 1 1 0 1 0 0 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1

0 0 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 1 0

0 0 1 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 1 0 1

1 0 0 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 1 1 0 1 1 0 1 1 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 0 1 0 0

Appendix 3 TABLEA18 Cemetery data for model 7C A

B

75 14 76 8 77 2 78 1 79 79 80 25 81 3 82 62 83 1 84 67 85 2 86 57 87 20 88 68 89 6 90 40 91 70 92 64 93 13 94 15 95 53 96 5 97 11 98 45 99 38 100 34 101 60 102 77 103 19 104 62 105 77 106 9 107 2 108 45 109 1 110 4 111 40 112 30 113 64 114 18 115 17 116 30 117 52 118 28 119 44 120 70 121 23 122 52 123 68 124 50 125 74 126 15 127 3 128 30 129 23 130 72 131 60 132 69 133 56 134 50 135 63 136 66 137 35 138 2 139 45 140 55 141 2 142 43 143 49 144 4 145 63 146 54 147 4 148 57

C

E

D

G

F

F F M F M M M F F F F M M F M M M F F M F M M F M F M M F M F F F F M F M M F F F M M M F F M F M F F M F M F M M M M F M F F F M F M F M F F F F F

1 1 1 2 2 2 1 2 1 2 2 2 1 2 1 2 2 2 1 2 2 1 2 1 1 1 1 1 1 1 2 1 1 2 2 1 1 2 1 2 2 2 1 1 2 2 1 1 2 1 1 1 2 2 1 1 1 2 1 2 1 1 1 1 1 1 2 2 2 1 1 2 2 1

2 3 2 3 3 1 3 3 3 3 2 1 2 1 2 2 3 1 2 2 2 3 2 2 3 2 1 2 1 1 3 3 3 2 1 3 2 1 2 2 2 2 2 2 2 2 3 1 3 1 2 2 2 3 1 1 1 2 2 2 2 2 2 3 2 2 3 2 1 3 1 2 3 2

2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 12 1 12 5 3 3 2 3 3 13 7 84 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 3 3 3 4 4 384460 10 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5 7 8 9 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1

0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

351

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1

0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 1 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0

0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0

1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0 0 1

0 0 0 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0

0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0

0 1 1 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 0 0 0 0 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 0 0

0 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 0 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 1 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1

1 0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 0 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 0 0 0

1 1 0 1 1 0 0 1 1 1 0 0 1 0 0 1 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0

1 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 1 0 1 0 1 0 0 1 0 0 0 1 1 0 1

1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA18 (Continued) A

149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

39 57 39 2 25 4 2 18

CE

F F F F M M F M

71 F

1 23 43 26 26 16 70 23 1 22 2 14 3 44 65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32 1 30 17 75 50 76 34 25 51

F F F F F M M M F M F M F

F M F M F M F F F M F M F F M F F M F F F M F M M M M M F M

D

1 1 1 1 2 2 1 1 1 1 2 1 1 2 1 2 1 1 2 2 2 2 2 1 1 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 1 2 1 2 1 2 2 1 2 1 2 2

1 2 2 2 3 3 2 2 3 3 2 1 1 1 1 1 1 2 3 3 1 2 2 2 1 3 2 3 3 2 1 2 1 2 3 3 2 3 3 2 3 2 1 1 2 2 3 3 1 1 2 2

G

2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1

2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2121125332331378422112221111362291333344 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

352

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1

0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1

0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

3 5 74 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1

0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 0

0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1

0 5 7 8 9 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1

1 0 1 0 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 1

1 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 1 1 0 1 0

0 0 1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1

1 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1 1 0 1 1 0 1 0 1 0 0 0 1 0 0 1 0

0 1 1 1 0 1 1 0 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 0 0 1 1 1 0 0 0 0 0 1 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA19 Cemetery data for model 7D AB

CE

1 2 3 4 5 6 7

2 22 36 46 1 74 1 8 74 9 21 10 52 11 9

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70

2 30 75 77 18 42 60 3

1 76 42 28 5

16 3

1

60 40 37 4 61 43 4 2

44 33 43 12

M F M M F M M F M F F M M F M F M M M F M M F M M F F

7

75 1

1

1

2 2 2

2 2 2

M F

1 1

F F

1 2

M F

1 2 2 2 2 1 2 1 1 1 2 2 1 2 2 1 2 1 2 2 2 2 1 2 2 2 1 1 2 2 1 2 1 2 2 1 2 1

1 2 3 3 1 1 2 3 3 2 1 2 2 3 2 3 2 3 3 1 1 1 1 2 2 1 2 1 3 2 2 3 1 2 3 2 3 3 2 2 3 3

13

F

8

M

20 43 45

M

3

39 45 24 26 58 62 3 15 4

7 14 59 25

71 71 2

72 4 73 2

2 2 1 2 2 2 1 2 2 2 2 2 2 1 2 2 1 2 2 1 1 1 1 2 2

3 1 1 2 3 2 3 1 2 1 2 3 2 2 2 2 2 2 2 3 2 2 3 3 1 3 3

M F F

2 4

21 1 26 5 47

1 2

M

F F F F F M F F F M M F F

3

D

M M F F M F M F M F M F F F M F F M F F

G

2 1 2 1 2 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2 1 1 2 1 1 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 1 2 2 1 1 2 2 2 1 2 2 1 2 2 2 2 2 1 2 1 2 2 2 2 2 2

F

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

212112533233137842211222111136229133334444444 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 3 5 74 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

00000000000000000000000000000101011000111010 0 0 1 1 0 1 1 1 0 0 10 10 10 0 0 0 0 0 0 0 0 0 00000000000000011111001000000010001000100010 10000101011101000000000000000010010101011100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 10 10 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 0 0 0 0 0 1 1 10 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 10 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 1 1 10 0 1 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 1 1 1 1 0 1 10 0 1 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 1 1 10 0 1 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 1 0 1 0 0 1 1 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 10 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 1 1 1 0 1 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 10 10 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 10 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 10 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

353

5 6

0 5 7 8 9 0 12 3 4 5 6

0 0 0 0 0 10 0 1 1 1 1 1 10 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0

0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1

0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 1 0

0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1

0 0 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 0 1 0 1 1 0

1 1 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 1 1 0 1 1 0 1 0 0 0 1 0 1 1 1 1 0 1 1 0 0 0 0 1 0 1 0 1 1 0 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0

1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 0 0 1 0 1 0 1 1 0 0 1 1 0 1 0 0 0 1 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0 1 0 0

1 0 0 1 0 1 1 1 1 1 0 1 1 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 1 0 0

0 1 0 0 1 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 1 1 0 0 1 1 0

1 1 0 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 1 0 0

1 0 1 1 1 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0

0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 1 1 1 0 1 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 0 1 0 1 0 0 1

0 1 0 0 0 1 1 1 1 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 1 1 0 0 0 1 0 0 1 0 1 0 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1

1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 1 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 1

0 1 0 1 1 0 0 1 1 1 1 1 0 0 1 1 0 0 0 1 0 0 1 1 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA19 (Continued) A

B

C

E

D

G

F

2 12 1 12 5 3 3 2 3 3 13 7 84 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 3 3 3 4 4 4 4 4 4 4 384460 10 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5 7 8 9 0 12 3 4 5 6

27 14 8 2 1 79 25 3 62 1 67 2 57 20 88 68 89 6 90 40 91 70 92 64 93 13 94 15 95 53 96 5 97 11 98 45 99 38 100 34 101 60 102 77 103 19 104 62 105 77 106 9 107 2 108 45 109 1 110 4 111 40 112 30 113 64 114 18 115 17 116 30 117 52 118 28 119 44 120 70 121 23 122 52 123 68 124 50 125 74 126 15 127 3 128 30 129 23 130 72 131 60 132 69 133 56 134 50 135 63 136 66 137 35 138 2 139 45 140 55 141 2 142 43 143 49 144 4 145 63 146 54

M F F M F M M M F F F F M M F M M M F F M F M M F M F M M F M F F F F M F M M F F F M M M F F M F M F F M F M F M M M M F M F F F M F M F M F F F

1 1 1 1 2 2 2 1 2 1 2 2 2 1 2 1 2 2 2 1 2 2 1 2 1 1 1 1 1 1 1 2 1 1 2 2 1 1 2 1 2 2 2 1 1 2 2 1 1 2 1 1 1 2 2 1 1 1 2 1 2 1 1 1 1 1 1 2 2 2 1 1 2

3 2 3 2 3 3 1 3 3 3 3 2 1 2 1 2 2 3 1 2 2 2 3 2 2 3 2 1 2 1 1 3 3 3 2 1 3 2 1 2 2 2 2 2 2 2 2 3 1 3 1 2 2 2 3 1 1 1 2 2 2 2 2 2 3 2 2 3 2 1 3 1 2

2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

74 75 76 77 78 79 80 81 82 83 84 85 86 87

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0

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0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0

354

1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 0 0 0 1 1 1 1 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 0 0 1

0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1

1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1

0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 0 1 1 0

0 0 0 0 1 1 1 0 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1

0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

0 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1

0 1 0 1 0 0 1 0 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 1 0 1

1 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 1

1 0 1 1 0 1 1 0 0 1 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 0 1 0 0 1 1 0 1 1 1

1 1 1 1 0 1 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1

1 0 0 1 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 0 1 0 1 1

0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0 1 0 1 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 0 1 1 1 0 1 1 0 1 1 1 0 1 1 0 0 0 0

1 1 1 1 1 0 0 1 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 0 0 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 0 1 0 0 1 0 0

1 0 1 0 0 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 0 0 0 1 0 1 1 1 0 1 1 1 0 1 1 0 0 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0

1 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0 0

0 1 1 1 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 1 1

Appendix 3 TABLEA19 (Continued) A

147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172

173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

4 57 39 57 39 2 25 4 2 18 71

1 23 43 26 26 16 70 23 1 22 2 14 3 44 65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32 1 30 17 75 50 76 34 25 51

CE

D

2 1 1 1 1 1 2 2 1 1 1 1 2 1 1 2 1 2 1 1 2 2 2 2

3 2 1 2 2 2 3 3 2 2 3 3 2 1 1 1 1 1 1 2 3 3 1 2

2

M 1 F 1 M 1 F 1 M 2 F 1 F 2 F 1 M 2 F 1 M 1 F 1 F 1 M 2 F 2 F 2 M 1 F 1 F 2 F 1 M 2 F 1 M 2 M 2 M 1 M 2 M 1 F 2 M 2

F F F F F F M M F M F F F F F F M M M F M F M F F

G

F

2 1 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2

1

0

2 1 3 2 3 3 2 1 2 1 2 3 3 2 3 3 2 3 2 1 1 2 2 3 3 1 1 2 2

2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

212112533233137842211222111136229133334444444 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 3 5 74

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0

0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

355

0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1

0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0

0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0

0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1

0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0

5 6

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 1 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 0 1 0 1 0 0

1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0 1 0 1 1

0 5 7 8 9 0 12 3 4 5 6

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 1 0 1 0 0 0 0 0 1 1 1 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1

0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 1 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 1 1

0 1 1 1 0 1 1 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1

0 1 1 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 1

0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 1 0 0

0 1 1 1 1 0 1 0 1 1 0 1 1 0 0 1 1 0 0 1 0 1 0 0 1 0 1 1 0 1 1 1 0 1 0 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1

1 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 0 0 0 0 1 1 0 1 1 1

1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 1 1 0 0 0

0 0 0 1 0 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 0 0 0 1 0 1 0 0 1 1 1 1 1 0

0 0 0 0 1 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 0 1 0 1 1 0 0 0 1 0 1 0 1 1 1 0 0 1 0

0 1 1 1 1 0 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 1 0 1 1 1 0 1 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA20 Cemetery data for model8A A

B

1 2 3 4 5 6 7 8 9

70 M 2 M 74 M

10 11

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68

C E

11 M F F F M M M M M M F M F F F M M F M F M M M M F M M M M M M M M M F F M M F M M M M M F M M F F M M M M M M M M M M M M M F M M

3 44 67 4 39 60 3 21 56 1 50 57 21 20 1 2 74 28 67 1 33 51 62 67 75 33 50 61 20 41 37 3 36 53 1 77 63 65 4 2 2 46 1 66 3 42 61 28 78 57 48 62 45 31 61 48 16 74 2 61 51 21 26 2

2 1 2 2 1 1 2 2 2 2 2 2 1 1 2 2 1 1 2 2 1 2 2 1 1 2 2 1 1 1 2 1 1 1 1 1 2 1 2 2 1 2 1 1 1 2 1 2 1 1 2 1 2 2 1 2 1 2 2 1 2 1 2 2 1 1 2 2

D

F

12 14 5 2 2 12 19 1 12 3 2 2 6 3 2 1 5 6 5 2 2 031579 4 8 7 9

1 3 2 3 3 2 3 2 1 2 3 3 2 3 2 2 1 1 3 3 1 3 3 2 1 2 2 2 2 2 2 3 2 2 2 3 3 2 3 2 2 1 2 3 3 1 3 3 2 3 2 2 2 1 2 2 1 2 1 2 3 2 3 2 2 2 3 2

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

1 0 1 0 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0

1 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0

0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 0 1 0 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 0

1 0 1 1 0 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 1 1 0 1 0 1 1 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 0 0 0

0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1

0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 1 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 1 0 1 1 1 1 0 0 1 1 0 0 1 1 1 1 1 1 1 0 0 1 0 1 0 1 1 0 0 0 1 0 1 1 1 1 0 1 0 1 0 0 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0

356

1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 1 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 1 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0 1 0 1 1 0 0 0

Appendix 3 TABLEA20 (Continued) A

B

C E

D

F

12 14 5 2 2 12 19 1 12 3 2 2 6 3 2 1 5 6 5 2 2 031579 4 8 7 9

69 70

56 34 53 1 24 56 45 39 75 8 31 2 38 4 31 1 34 3 2 3 78 1 2 32 32 5 30 29 41 24 2 75 55 69 51 31 13 51 17 51 56 2 62 31 33 4 51 1 62 65 3 1 34 59 4 79

M M M F F M M M F M M M M M M M M M M M M F M M M M M M F F M M F M M M M F F M M M M M M M F F F M M M M F M M F M M M M M F F M M M M M M M F M M

1 2 1 1 2 1 1 1 1 1 2 1 1 1 1 1 2 1 2 2 1 1 2 1 1 2 2 1 2 1 2 1 1 1 1 1 2 1 1 2 2 1 1 1 1 2 1 1 2 1 2 1 1 1 2 1 1 2 1 2 1 2 1 1 1 1 2 2 2 2 2 2 2 1

2 2 2 3 2 1 1 2 3 3 2 3 1 3 2 3 2 2 3 3 3 3 3 3 2 3 1 2 1 2 3 3 3 3 3 1 2 3 2 2 3 2 2 2 1 3 3 2 1 3 3 3 2 2 3 2 3 3 3 2 3 2 3 3 2 2 1 3 2 2 1 3 3 3

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0

71 72

73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142

71

4 74 62 4 69 1 36 55 55 58 1 75 64 42 33 4 27

1 1 1 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 0 0 1 0 1 1 1 0 0 0 1 1 0 1 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 0 1

0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 1 0 1 1 0 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1

0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 1 0 1 0 1 0 1 1 1 1 0 0 0 0 0 0 0 1

0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 1 0 0 1 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 1 1 1 1 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0 1 1 1 0 1 1 1 0 0 0

0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 1 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0

0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

357

0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA20 (Continued) A

B

143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170

3 47 36 4 13 52 52 70

171

2

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

38 1 4 1 77 27 37 22 65 45 34 14 13 34 51 13 3 2 14 16 25 8 7 67 25 5 14 28 15

71

4 24 24 67 3 67 2 34 63 15 4 2 5 71

1 4 8 3 4

CE M M F M M F M M M M M M M M M M F M M M M M F M F M M F M M M M M M M F M F M M M M F F M M M M M M M M M F M M F M

D

F

1 1 2 1 1 1 2 1 1 1 1 1 2 2 1 1 2 1 1 1 2 2 2 1 2 2 1 2

3 2 1 2 3 2 1 3 1 3 3 2 2 3 1 3 1 2 2 2 3 2 3 3 3 3 3 2

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1

3

0

2 1 2 1 1 1 2 2 1 2 2 2 1 1 2 2 1 2 1 1 2 2 1 1 1 1 2 2 2

2 3 3 3 3 3 2 3 2 1 1 3 3 3 3 3 3 3 3 3 1 2 3 3 2 3 3 2 3

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

121452212191123226321 9 5 6 5 2 2 0 3 15 79 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0

0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1

0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 1 0 0 1 1 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 1 0 0 1 1 0 0 1 1

0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 0 1 0 1 1 1 0 0 1 0 0 0 1 1 0 0 1 0 1 1 0 0 1 1 1 1 0 0 0

0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0 1 1 1

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 1 0 1 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 1 0 1 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0

0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 1 0 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0

0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1

4 8 7 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

TABLEA21 Cemetery data for model8B A

B

1

72

2

33 2 39 51 58

3 4

5 6 7 8

9

1

35 69

CE

F F F F M F F M M

D

2 1 2

3 2 3

G

F

2 1 2

0 0 0

1

1

1

0

1

2

2

0

1

1

1

0

1 2 1

2 1 2

1 1 1

0 0 0

21211253323313784221122211113622913 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0

0 1 0 1 0 1 0 0 0

0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 1 0

0 1 0 1 0 1 1 1 1

0 0 0 1 0 1 0 1 0

0 1 0 1 0 1 1 1 1

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 1

0 1 0 1 0 1 1 1 1

0 1 0 0 0 0 1 0 1

0 0 0 1 0 1 0 0 0

358

0 0 0 0 0 0 0 1 1

0 1 0 1 0 1 0 0 0

1 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

3 5 74 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 1 0 0 0 0

1 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0

0 1 0 1 1 1 1 0 1

1 0 1 0 0 0 0 1 0

0 5 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0

0 1 1 0 0 0 0 0 0

Appendix 3 TABLEA21 (Continued) A

B

10 2 11

59

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

3

67 57 1 21 1 21 27 16 16 21 1 7 3

33

27 3

4

39 3 62 33 59 34 42 35 73 36 72 37 8 38 27 39 20 40 1 41 75 42 3 43 46 44 47 45 2 46 36 47 22 48 57 49 24 50 57 51 64 52 53 53 44 54 64 55 1 56 2 57 25 58 18 59 69 60 17 61 38 62 52 63 36 64 9 65 42 66 1 67 49 68 8 69 17 70 2 71 14 72 4 73 21 74 58 75 23 76 12 77 31 78 2 79 34 80 4 81 58 82 73

CED

F F M M M M F F M M

G

F

1

1

1

0

2 2

1 2

2 1

0 0

2 2 1 2

2 2 1 2

2 2 1 2

0 0 0 0

1 1

2 3

1 2

0 0

2

2

2

0

M

1

2

1

0

F

2

3

2

0

M

1

2

1

0

M M F M M F M

1 2 1 2

2 3 3 3

2 2 2 2

0 0 0 0

2 1

2 1

2 1

0 0

2 1

3 1

2 2

0 0

M

1

1

1

0

2 1 2

2 1 1

1 2 2

0 0 0

2

2

2

0

1 1

2 2

2 1

0 0

2 1

1 1

1 1

1 1

F

1

2

1

0

F F F F M

1 1 1

2 3 3

2 2 2

0 0 0

2

2

2

0

1

3

2

0

F F M M F M M

1 2 2 2 2 1 1

2 1 3 3 1 1 2

1 1 2 2 1 2 1

0 0 0 0 0 0 0

F F M M

2

2

2

0

1 2 2

3 3 2

2 2 1

0 0 0

M

2

2

1

0

F

2

3

2

0

M M F F M M M F M F F M M M M F M M F F M F M M

1

1

1

0

1 2 1 1

2 1 2 2

1 2 2 2

0 0 0 0

F M M M F F M M M

1

1

1

0

2 1 1 1

2 2 2 2

1 1 2 1

0 0 0 0

1

1

1

0

2

1

1

0

2

2

2

0

1 1

2 1

1 2

0 0

1 2

1 2

1 2

0 0

1 1 2 2 2

3 2 2 1 2

2 2 1 2 1

0 0 0 0 0

1 2

2 1

1 1

0 0

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1

0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

1 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 1 1 1

1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1

1 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 1 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1

1 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 1 1 1

0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0

359

0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0

0 1 0 1 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 0 0 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1 1 0 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0

0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0

0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0

0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0

3 5 74 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 1 0 1 1 0 1 0 1 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 1 1 1 0 1 1 1 1 0 0 1 1 1 0 1 1 0 0 0 1 0

0 1 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 1 1 1 0 1

0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 1 0 1 0 1 1 1 0 1 0 0 0 0 0 0 1 0 1 0 1 1 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 1 0 1 1 1 0 0 1 1 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA21 (Continued) A

B

23 30 64 73 76 88 24 89 77 90 7 91 43 92 39 93 50 94 66 95 1 96 58 97 3 98 7 99 51 100 1 101 36 102 3 103 63 104 33 105 30 106 27 107 17 108 77 109 16 110 23 111 39 112 3 113 11 114 78 115 62 116 24 117 22 118 23 119 51 120 70 121 45 122 45 123 1 124 32 125 48 126 39 127 18 128 65 129 42 130 3 131 36 132 59 133 4 134 51 135 59 136 34 137 64 138 73 139 4 140 1 141 16 142 55 143 26 144 54 145 1 146 2 147 1 148 33 149 17 150 43 151 3 152 56 153 63 154 79 155 3 156 27 83 84 85 86 87

C

E

D

G

F

2 12 1 12 5 3 3 2 3 3 13 7 8 4 2 2 1 12 2 2 1 1 1 13 6 2 2 9 13 3 8 4 4 6 0 1 0 1 3 12 9 9 2 2 8 7 3 5 74 5 6 0 5

M M M F F M F M F F M F F F F F M M M M M F F F M F M M F F M M M M M F F M F M F M M M M M F M M M F F M M M M M M F F F M M M F M M F M F F F M F

1 2 2 1 1 1 2 1 1 1 1 1 1 1 2 2 2 1 2 2 2 1 1 1 1 2 2 1 1 2 2 1 2 2 2 1 1 1 1 1 2 1 2 2 1 1 1 1 2 1 1 2 1 2 1 2 2 2 2 1 2 1 1 2 1 2 1 2 1 1 2 2 1 1

2 2 3 1 2 2 3 1 2 1 1 1 2 1 1 2 2 3 2 3 2 2 2 2 3 1 2 1 1 2 2 2 3 2 2 2 2 2 3 1 2 1 2 2 2 3 2 1 2 1 3 2 1 1 2 2 3 3 3 2 1 2 2 1 3 2 2 2 2 1 2 1 2 3

2 1 2 1 1 1 2 1 1 2 2 2 1 1 1 1 1 2 1 2 1 1 1 2 2 2 1 2 1 1 1 2 2 1 2 1 2 1 2 2 2 1 1 2 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 2 1 2 2 1 2 2

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 0 0

0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0

0 1 0 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0

0 1 0 1 1 1 0 1 1 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 0 0 1 0 0

0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 1 0 1 0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 1 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0

360

0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0

0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0

1 0 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 1 1 0 0 0 1 0 1 1 0 0 1 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1

0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0

0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 0 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 1 1 1 0 1 0 0 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 1 0 1 1 0 1 0 1 0 1 1 0 0 1 1

0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 0 0 0 1 1 0 0 1 1 0 1 1 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 0 1 1 1 1 0 1 0 0 1 0 1 0 1 0 0 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 1 1 0 1

Appendix 3 TABLEA21 (Continued) A

157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

B

CED

19 65 9 34 23 32 48 36 33 17 3 4 60 21 1 77 75 3 28 53

F F F

64 1 55 34 61 3 4 76 37 40 62 16 74 26 63 2 74 37

F

M

F F F M M

M M M M M

F M

F

M M M 44 M 19 M

44

33 27 75

M M

F F F F M M

F F F M

F M

M F M F F F M

G

1 1 1 1 1 1 2 2 1 2 1 2 2 1 1 1 2 1 1 1 2 2 1 1 1 1 2 1 1 1 2 2 1 1 2 2 1 2 2 2 1 2 2 2

2 2 3 1 2 1 3 2 1 1 2 3 1 1 3 2 2 2 2 1 2 1 2 3 1 2 1 2 1 2 3 2 1 2 2 3 1 3 2 1 1 2 3 2

2 2 2 2 2 2 2 1 1 1 1 2 1 1 2 2 2 1 1 1 2 1 2 2 1 1 1 1 1 2 2 2 1 2 1 2 2 2 1 2 1 2 2 2

F

21211253323313784221122211113622913 3 84 4 6 0 10 1 3 12 9 9 2 2 8 7

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0

1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1

1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 1

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 1

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

TABLEA22 Cemetery data for Ramsauer model (random seed 1) AB

CE

1 2 3 4

2 Fl 22 F 2 36F2 46F2 5 1 Fl 6 74 F 2 7 1 F2 8 74 F 2 9 21 F 1 10 52 F 2

HD

G

2 1 1 1

1 1 2 1

1 3 2 1

2

1

2

1 2 1 1 1

1 1 3 1 2

4 1 1 4 2

11 9

F 2

1

2

1

12 2 13 30 14 75 15 77 16 18 17 42 18 60 19 3 20 1 21 76 2242Fl

F F F F F F F F F F

2 2 1 1 2 2 2 1 2 2 1

1 1 2 1 2 1 1 1 1 1 1

3 1 2 4 1 1 2 1 2 4 3

2 2 2 2 1 2 2 1 2 2

4445555555555666666666677 7 89 0 12 3 4 5 6 7 8 9 0 12 3 4 5 6 7 8 9 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 0 1

0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0

1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0

0 1 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0

0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1

0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0

0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1

0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1

0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

361

0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0

3 5 74 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0

1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1

0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0

5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 0 0 1 0 1 0 0 1 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 1 1 0 1 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 1 1 0 1 1 1 0 1 1 1

0 5 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 1 0 1 0 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1 0 0 0 1 0 1 0 1 0 0 0 1 1 1 1 0 0

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA22 (Continued) A

B

CE

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

28 5 16 3 1 60 40 37 4 61 43 4 2

F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F F

44

44

33 43 12 3 21 1 26 5 47 7 75 1 2 4 13 8 20 43 45 3 39 45 24 26 58 62 3 15 4 7 14 59 25

45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 71 2 72 4 73 2 74 27 75 14 76 8 77 2 78 1 79 79 80 25 81 3 82 62 83 1 84 67 85 2 86 57 87 20 88 68 89 6 90 40 91 70 92 64 93 13 94 15 95 53 96 5 97 11

1 1 1 2 2 1 2 2 2 1 1 1 2 1 2 2 2 2 1 2 1 1 1 2 2 1 2 2 1 2 1 2 2 2 2 1 2 2 2 1 1 2 2 1 2 1 2 2 1 2 1 1 1 1 1 2 2 2 1 2 1 2 2 2 1 2 1 2 2 2 1 2 2 1 2

H

D

G

1 2 1 2 2 1 1 2 1 1 2 2 2 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 1 1 2 2 1 1 2 2 1 1 2 2 2 2 1 2 1 2 2 1 2 2 1 2 1 2 2 1 2 2 2 2 1 2 1 2 1 1 2 2 1 2 2 1 1 2 1

3 2 1 1 3 1 1 1 1 1 1 1 1 2 1 1 2 2 1 1 1 1 1 1 3 2 1 1 2 2 2 2 2 1 1 2 1 1 1 1 3 2 1 2 1 1 1 2 1 1 1 2 2 1 1 1 1 1 1 1 2 1 2 3 1 2 1 1 1 1 1 1 1 1 1

1 1 2 2 1 1 2 1 2 3 3 2 1 1 3 1 2 1 3 2 4 3 1 1 1 2 2 3 2 1 2 2 1 1 1 1 2 2 3 3 1 1 1 2 1 1 4 2 3 1 1 2 1 1 1 1 2 2 1 1 2 1 2 1 1 2 2 3 2 2 1 1 1 2 2

4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 89 0 12 3 4 5 6 7 8 9 0 12 3 4 5 6 7 8 9 0 1 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 1

0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1

0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 1 1 0

0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0

0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1

0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

362

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 0

Appendix 3 TABLEA22 (Continued) A

B

CE

45 F 1 98 99 38 F 1 100 34 F 1 101 60 F 1 102 77 F 1 103 19 F 1 104 62 F 1 105 77 F 2 106 9 F 1 107 2 F 1 108 45 F 2 109 1 F 2 110 4 F 1 111 40 F 1 112 30 F 2 113 64 F 1 114 18 F 2 115 17 F 2 116 30 F 2 117 52 F 1 118 28 F 1 119 44 F 2 120 70 F 2 121 23 F 1 122 52 F 1 123 68 F 2 124 50 F 1 125 74 F 1 126 15 F 1 127 3 F 2 128 30 F 2 129 23 F 1 130 72 F 1 131 60 F 1 132 69 F 2 133 56 F 1 134 50 F 2 135 63 F 1 136 66 F 1 137 35 F 1 138 2 F 1 139 45 F 1 140 55 F 1 141 2 F 2 142 43 F 2 143 49 F 2 144 4 F 1 145 63 F 1 146 54 F 2 147 4 F 2 148 57 F 1 149 39 F 1 150 57 F 1 151 39 F 1 152 2 F 1 153 25 F 2 154 4 F 2 155 2 F 1 156 18 F 1 157 71 F 1 158 1 F 1 159 23 F 2 160 43 F 1 161 26 F 1 162 26 F 2 163 16 F 1 164 70 F 2 165 23 F 1 166 1 F 1 167 22 F 2 168 2 F 2 169 14 F 2 170 3 F 2 171 44 F 2

H

D

G

1 2 2 2 2 1 1 1 2 1 2 1 2 1 1 1 1 2 1 2 2 1 1 1 1 2 2 2 2 1 2 1 1 2 1 2 1 1 2 2 1 1 1 1 2 1 2 1 1 1 1 2 2 1 1 2 1 1 1 2 2 2 2 1 2 2 2 1 2 1 2 1 1 1

1 2 1 1 3 1 1 3 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 3 1 1 3 1 2 1 1 1 1 2 2 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 2 3 2 1 1 1 1 1 1 1 1 1 1

1 2 3 1 1 2 3 1 1 2 1 3 4 1 2 1 1 2 2 3 2 4 3 1 1 1 4 1 1 4 2 2 2 1 2 2 2 1 1 1 2 1 2 1 2 1 1 3 2 2 2 2 2 1 1 1 3 3 1 1 1 2 1 2 3 2 1 1 4 1 3 3 4 1

4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 89 0 12 3 4 5 6 7 8 9 0 12 3 4 5 6 7 8 9 0 1 0 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 1

0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0

0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 1 0

0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0

0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0

0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0

0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

363

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1

0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1

Theoretical and Quantitative Approaches to the Study of Mortuary Practice

TABLEA22 (Continued) A

B

CE

172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200

65 17 2 18 18 1 29 56 14 31 9 2 42 55 4 35 48 47 66 32 1 30 17 75 50 76 34 25 51

F F F F F F F F F F F F F F F F F F F F F F F F F F F F F

1 1 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 1 2 1 2 1 2 2 1 2 1 2 2

H

D

G

1 2 2 2 2 2 1 1 1 1 1 1 1 2 1 2 2 2 2 2 1 1 2 2 2 2 2 1 2

1 1 1 1 1 1 2 1 1 2 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2

2 1 1 1 2 2 1 3 1 2 1 1 2 2 1 2 1 4 1 2 1 1 2 4 2 4 2 1 1

4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 89 0 12 3 4 5 6 7 8 9 0 12 3 4 5 6 7 8 9 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0

0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0

1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0

364

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0

1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0