Community Quality-of-Life Indicators: A Guide for Community Indicators Projects 303110207X, 9783031102073

Table of contents :
Preface
Contents
About the Author
Part I: Introduction
Chapter 1: Introduction
Community
Community Indicators
Community Indicators Projects
Summary
Progress Check
Progress Check Answers
References
Chapter 2: Theoretical Foundations
Introduction
The Concept of Personal Utility
The Concept of Opulence
The Concept of the Just Society
The Concept of Human Need Satisfaction
The Concept of Sustainability
Other Theoretical Concepts
Summary
Progress Check
Progress Check Answers
References
Chapter 3: An Example
Introduction
Initiating an Indicators Project
Forming an Indicators Project Committee
Developing an Initial Set of Indicators
Refining the Initial Set of Indicators and Collecting Data
Validating Objective Indicators with Subjective Ones
Translating System Indicators into Program and Policy Indicators
Disseminating the Indicators´ Findings
Summary
Progress Check
Progress Check Answers
Reference
Part II: Planning
Chapter 4: Organizing
Introduction
Holding a Feasibility Meeting
Developing an Organizational Structure
The Chair
The Steering Committee
Task Forces
Visioning
Finding an Organization that Will Conduct the Process
Securing Funding
Developing and Sustaining a Budget
Public Website
Advisory Group
In-Person Presentations
Media
Newsletters
Print Pieces
Social Media
Summary
Progress Check
Progress Check Answers
References
Chapter 5: Making Decisions About Indicators
Introduction
Deciding on the Geographic Boundaries and Units Within
Selecting the Quality-of-Life Dimensions
Top-Down Approach to Selecting Quality-of-Life Dimensions
Bottom-Up Approach to Selecting Quality-of-Life Dimensions
Selecting the Quality-of-Life Indicators
Top-Down Approach to Selecting Quality-of-Life Indicators
Bottom-Up Approach to Selecting Quality-of-Life Indicators
Identifying Performance Indicators of Sponsoring Organizations
Eliminating Indicators Lacking Available Data and Statistics
Further Eliminating Indicators that Do Not Meet Standard Criteria
Putting Things Together (Secondary Data)
Considering Subjective Indicators (Primary Data)
Identifying Important Social Issues and Complementarity
Validating the Objective Indicators
Timeliness
Granularity to Local Geographies
Unavailability of Data in Useable Format
Issues of Public Accessibility
Incorporating Local Perceptions in Telling a Story
A Relational Approach to Selecting Quality-of-Life Indicators
Summary
Progress Check
Progress Check Answers
References
Part III: Implementation
Chapter 6: Data Collection
Secondary Data
Dealing with the Time Element of the Data
Manipulating the Data
Primary Data
Goals Associated with Survey
Survey Questionnaire
Sampling and Data Collection Method
Validating the Measures
Weighting the Sample and Data Analysis
Summary
Progress Check
Progress Check Answers
References
Chapter 7: Data Analysis
Data Variables and Measurement Scales
Variables
Measurement Scales
Statistical Analysis
Descriptive Statistics
Measures of Frequency
Measures of Central Tendency
Measures of Dispersion
Measures of Position
Inferential Statistics
Making Estimates about Populations
Hypothesis Testing
Developing a Composite Index
The Simple Average Method
The Item-Total Correlations Method
The Cost-Adjustment Method
The Balanced Method
The Weighting-by-Experts Method
Data Mining
Summary
References
Chapter 8: Data Reporting
Introduction
The Public Report
Content
Format
The Research Report
Translating Outcome Indicators into Action Indicators
Summary
Reference
Chapter 9: Promotion
Introduction
Printing and Distributing the Reports
Launching a Public-Relations Campaign
Conducting E-Marketing
Other Forms of Promotion
Stimulating Community Action
Summary
Progress Check
Progress Check Answers
References
Chapter 10: Follow-Up
Introduction
Annual Reviews
What?
Why?
When?
Where?
Who?
Measuring the Impact of Indicator Reports
Awareness
Knowledge
Attitude
Action
Summary
Progress Check
Progress Check Answers
References
Appendix
Data Sources
Websites Providing Useful Information
Web Sites of Community Indicators Projects
Towns, Cities, and Counties
Regions
States and Provinces
Countries
Indicators Projects with Special Focus
Other Web Resources
In-house Publications
Bibliography

Citation preview

Community Quality-of-Life and Well-Being

M. Joseph Sirgy

Community Quality-ofLife Indicators A Guide for Community Indicators Projects

Community Quality-of-Life and Well-Being Series Editor Rhonda Phillips, Purdue University, West Lafayette, IN, USA Editorial Board Members Meg Holden, Urban Studies Program, 2nd Floor, Simon Fraser University, Vancouver, BC, Canada Charlotte Kahn, Boston Foundation, Boston, USA Youngwha Kee, Soongsil University, Dongjak-Gu, Korea (Republic of) Alex C. Michalos, Faculty of Arts, Brandon University, Brandon, MB, Canada Don R. Rahtz, Sadler Center, College of William & Mary, Williamsburg, USA Joseph Sirgy, Virginia Polytechnic Institute, Blacksburg, VA, USA

The Community Quality of Life and Well-being book series is a collection of volumes related to community level research, providing community planners and quality of life researchers involved in community and regional well-being innovative research and application. Formerly entitled, Community Quality of Life Indicators: Best Practices, the series reflects a broad scope of well-being. Next to best practices of community quality-of-life indicators projects the series welcomes a variety of research and practice topics as related to overall community well-being and quality of life dimensions, whether relating to policy, application, research, and/or practice. Research on issues such as societal happiness, quality of life domains in the policy construct, measuring and gauging progress, dimensions of planning and community development, and related topics are anticipated. This series is published by Springer in partnership with the International Society for Quality-of-Life Studies, a global society with the purpose of promoting and encouraging research and collaboration in quality of life and well-being theory and applications.

M. Joseph Sirgy

Community Quality-of-Life Indicators A Guide for Community Indicators Projects

M. Joseph Sirgy Virginia Tech Blacksburg, VA, USA

ISSN 2520-1093 ISSN 2520-1107 (electronic) Community Quality-of-Life and Well-Being ISBN 978-3-031-10207-3 ISBN 978-3-031-10208-0 (eBook) https://doi.org/10.1007/978-3-031-10208-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to community indicators researchers worldwide

Preface

This book contributes to the literature of community indicators research. The book is designed as a training manual for graduate students taking a course in community development and community researchers who are interested in further education (and possibly getting certified) in community indicators research. The International Society for Quality-of-Life Studies (ISQOLS) offers a curriculum on community indicators research leading to certification as a Community Indicator Researcher. The certification program is administered through the Management Institute for Quality-of-Life Studies (MIQOLS). The complete curriculum is provided on ISQOLS Web site at www.isqols.org. This book is designed to help community indicator researchers (and those who are stepping into this position) enhance their professional knowledge of the subject matter to enhance professional competence as Community Indicator Researchers. The book is divided into three major parts. The first part is essentially an introduction; the second part focuses on issues related to planning community indicators projects. The third part focuses on issues related to implementation. Part I (Introduction) contains three chapters. The first chapter (Chap. 1) describes the basic concepts of community indicators projects: “community,” “community indicators,” and “community indicators projects.” The second chapter (Chap. 2) discusses the theoretical foundations of community indicators research. Five theoretical concepts are described guiding the formulation of community indicator projects. The third chapter (Chap. 3) provides an example of a community indicators project as an illustration of the entire process without delving too much into details. Part II (Planning) contains two chapters. Chap. 4 (Organizing) describes organizational aspects involved in planning—how to identify sponsors, secure funding, develop an organizational structure, etc. Chap. 5 (Making Decisions about Community Indicators) discusses issues related to the selection of a quality-of-life model, selection of indicators, and so on. Part III (Implementation) focuses on issues related to data collection, data analysis, data reporting, promotion, and follow-up. Specifically, Chap. 6 focuses on data collection. Two types of data collection are described: secondary and primary data collection. The process of these two data collections is described in some detail. vii

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Preface

Chap. 7 focuses on data analysis. In this chapter, we discuss how variables are construed and typical measurement scales. This is followed by a discussion of descriptive and inferential statistics commonly used in community indicators projects. Chap. 8 focuses on data reporting—aspects related to preparing two reports, namely the public report and the research report. Chap. 9 deals with promotion issues—printing and distributing the reports and promotion techniques commonly used. Finally, the last chapter (Chap. 10) describes the last stage of the project, namely follow-up. In this chapter, we discuss how annual reviews are conducted by answering questions such as What, Why, When, Where, and Who. We also discuss how the impact of community indicators projects is measured. Every chapter has a list of learning objectives. These objectives are designed to further guide the reader to the major points in the chapters and track learning progress. In addition, the Progress Checks provide a list of questions that should help the reader with learning. There are also answers to the questions pertaining to the Progress Checks at the end of every chapter. The reader is encouraged to answer the questions after reading through the chapter before examining the answers. The answers are designed to reinforce learning of the concepts discussed in the chapter. Blacksburg, VA, USA

M. Joseph Sirgy

Contents

Part I

Introduction

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

2

Theoretical Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

3

An Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45

Part II

Planning

4

Organizing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65

5

Making Decisions About Indicators . . . . . . . . . . . . . . . . . . . . . . . . .

83

Part III

Implementation

6

Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

7

Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

8

Data Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167

9

Promotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

10

Follow-Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

ix

About the Author

M. Joseph Sirgy (https://sites.google.com/a/vt.edu/joe-sirgy-personal-website/) is a management psychologist (Ph.D., U/Massachusetts, 1979), the Virginia Tech Real Estate Professor Emeritus of Marketing at Virginia Polytechnic Institute and State University (USA), and an Extraordinary Professor at the WorkWell Research Unit at North West University—Potchefstroom Campus (South Africa). He has published extensively in the area of business administration, business ethics, and quality of life (QOL). He co-founded the International Society for Quality-of-Life Studies (ISQOLS) in 1995, served as its Executive Director/Treasurer from 1995 to 2011, and as a development Co-Director (2011–present). In 1998, he received the Distinguished Fellow Award from ISQOLS. In 2003, ISQOLS honored him as the Distinguished QOL Researcher for research excellence and a record of lifetime achievement in QOL research. He also served as a President of the Academy of Marketing Science (2002–2004) from which he received the Distinguished Fellow Award in the early 1990s and the Harold Berkman Service Award in 2007 (lifetime achievement award for serving the marketing professoriate). In the early 2000s, he helped co-found the Macromarketing Society and the Community Indicators Consortium and has served as a board member of these two professional associations. He co-founded the journal, Applied Research in Quality of Life, the official journal of the International Society for Quality-of-Life Studies, in 2005; and he has served as a co-founding editor (1995–present). He also served as an editor of the QOL section in the Journal of Macromarketing (1995–2016). He received the Virginia Tech’s Pamplin Teaching Excellence Award/Holtzman Outstanding Educator Award and University Certificate of Teaching Excellence in 2008. In 2010, ISQOLS honored him for excellence and lifetime service to the society. In 2010, he won the Best Paper Award in the Journal of Happiness Studies for his theory of the balanced life; in 2011, he won the Best Paper Award in the Journal of Travel Research for his goal theory of leisure travel satisfaction. In 2012, he was awarded the EuroMed Management Research Award for outstanding achievements and groundbreaking contributions to well-being and qualityxi

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About the Author

of-life research. In 2019, the Macromarketing Society honored him with the Robert W. Nason Award for extraordinary and sustained contributions to the field of Macromarketing. He is currently serving as an editor-in-chief of the Journal of Macromarketing (2020–present) He also was the editor of ISQOLS/Springer book series on International Handbooks in QOL (2008–2015), Community QOL Indicators: Best Cases (2004–2015), Applied Research in QOL: Best Practices (2008–2012). He is currently the co-editor of Springer book series on Human Well-Being and Policy Making (2015–present). His recent books include: • Shultz II, Clifford J., Don R. Rahtz, and M. Joseph Sirgy (Eds.) (2022). Community, Economy, and COVID-19: Lessons from Multi-Country Analyses of a Global Pandemic. Cham, Switzerland: Springer Nature. • Sirgy, M. Joseph (2022). The Balanced Life: Using Strategies from Behavioral Science to Enhance Wellbeing. Cambridge: Cambridge University Press. • Sirgy, M. Joseph (2021). The Psychology of Quality of Life: Wellbeing and Positive Mental Health. 3rd edition. Dordrecht: Springer. • Sirgy, M. Joseph (2020). Positive Balance: A Theory of Well-Being and Positive Mental Health. Dordrecht: Springer Publishing. • Sirgy, M. Joseph, Richard J. Estes, El-Sayed El-Aswad, and Don R. Rahtz (2019). Combatting Jihadist Terrorism through Nation Building: A Qualityof-Life Perspective. Dordrecht: Springer Publishing. • Estes, Richard J. and M. Joseph Sirgy (2018). Advances in Well-Being: Toward a Better World. London: Rowman & Littlefield Publishers. • Uysal, Muzaffer, Stefan Kruger, and M. Joseph Sirgy (Eds.) (2018). Managing Quality of Life in Tourism and Hospitality: Best Practices. Oxfordshire, UK: CABI Publishers. • Estes, Richard J. and M. Joseph Sirgy (Eds.) (2017). The Pursuit of Well-being: The Untold Global History. Dordrecht, Netherlands: Springer Publishing. • Sirgy, M. Joseph, Rhonda Phillips, and Don Rahtz (Eds.) (2013). Community Quality-of-Life Indicators: Best Cases VI. Dordrecht, Netherlands: Springer Publishing. • Sirgy, M. Joseph (2012). The Psychology of Quality of Life: Hedonic WellBeing, Life Satisfaction, and Eudaimonia. 2nd edition. Dordrecht, Netherlands: Springer Publishing. • Reilly, Nora P., M. Joseph Sirgy, and C. Allen Gorman (Eds.) (2012). Work and Quality of Life: Ethical Practices in Organizations. Dordrecht, Netherlands: Springer Publishing. • Uysal, Muzaffer, Richard Perdue, and M. Joseph Sirgy (Eds.) (2012). Handbook of Tourism and Quality-of-Life Research: Enhancing the Lives of Tourists and Residents. Dordrecht, Netherlands: Springer Publishing. • Land, Kenneth C., Alex C. Michalos, and M. Joseph Sirgy (Eds.) (2012). Handbook of Social Indicators and Quality-of-Life Research. Dordrecht, Netherlands: Springer Publishing.

About the Author

xiii

In relation to community indicators research, the author has been involved in community indicators research for many years. He has authored many publications in this area. He was also a co-editor of Springer’s book series on Community Quality-of-Life Indicators: Best Cases (2004–2015) with professors Rhonda Phillips (Purdue University) and Don Rahtz (College of William & Mary). He has been an instructor of the Community Indicators Research Certification Program (a certification program of the International Society for Quality-of-Life Studies, ISQOLS; https://isqols.org/Certification) for many years, and he wrote the training manual for the program. The book is based on this training manual. Finally, he has been involved as a member of a taskforce of a regional community indicators project in Southwest Virginia: The New Century Council.

Part I

Introduction

As previously mentioned, Part I (Introduction) contains three chapters. The first chapter (Chap. 1) describes the basic concepts of community indicators projects: “community,” “community indicators,” and “community indicators projects.” The second chapter (Chap. 2) discusses the theoretical foundations of community indicators research. Five theoretical concepts are described guiding the formulation of community indicator projects. The third chapter (Chap. 3) provides an example of a community indicators project as an illustration of the entire process without delving too much into details.

Chapter 1

Introduction

In this introductory chapter, we will discuss basic concepts such as “community,” “community indicators,” and “community indicators projects.” Learning Objectives In this chapter the reader should be able to answer the following questions: 1. What is a “community”? 2. What are “community indicators”? 3. What are good examples of community indicators from well-established community quality-of-life indices? 4. What are “community indicators projects”?

Community A community, is a grouping of people based on some geographic, demographic, or other social criterion. Most community indicators projects focus on geographic units as explicitly defined through a country’s census. For example, the U.S. Census Bureau identify six levels of geographic units: block, block group, census track, county, state, and national. As such, “communities of place are connected through geography, governance, or common characteristics that bind people together, whether implicitly or explicitly” (Sung & Phillips, 2018, p. 65). A block is a statistical area bounded by visible features (e.g., roads, streams, and railroad tracks) or nonvisible features (e.g., property lines, city or county limits, and school districts). It is considered to be the smallest geographic unit in the U.S. Census. There are more than 11 million blocks in the 2010 U.S. Census

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_1

3

4

1

Introduction

(US Census Bureau, 2019). The American Community Survey (ACS)1 [https:// www.census.gov/programs-surveys/acs/] provides information about a population down to a block-level analysis. A block group is a geographical unit considered between a census track and a census block. Typically, block groups have a population of 600–3000 people.2 A census track is a geographic unit typically containing a population between 2500 and 8000 people. They tend to be located in census metropolitan areas that have a core population of 50,000 or more.3 For example, in the State of Alabama, the 2010 Census contains 1181 census tracks, 3438 block groups, and 252,266 blocks (US Census Bureau, 2019). A county, on the other hand, is a primary legal division in most states in the U.S., Most counties reflect governmental units. Counties are referred to as “parishes” in the State of Louisiana.4 Most community indicator projects focus on large geographic units such as towns, cities, and counties (legal geographic jurisdictions), or an amalgam of towns/cities/counties that conjoin to serve a specific region. For example, I was involved with a community indicators project—Vital Signs Project of the New Century Council—that reflected the interests of residents of 12 counties in western Virginia, USA. This is not to say that a “community” cannot be defined using non-geographic criteria (demographics or other social criteria). There are many community indicators projects that focus on non-geographic population. For example, the Annie E. Casey Foundation have long conducted a community indicators projects referred to as the Kid Count.5 This project tracks 16 areas of child well-being across four domains: health, education, family and community and economic well-being by each state in the U.S. As such, the “community” in this context is defined by a demographic characteristic (i.e., children) and crossed by geography (i.e., state). Other community indicators projects focus on a particular area of community wellbeing (e.g., public health). Examples include the Community Health Improvement Plan for Austin/Travis County (Texas),6 the Dallas County Mental Health Indicator Parity project (Prabhakar et al., 2009), the Jacksonville’s Race Relations Progress Report (Warner, 2009).

1

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the population in the United States (https://www.census.gov/programs-surveys/ acs/about.html). 2 Definition is from the United States Census Bureau Glossary [Glossary (census.gov)]. 3 Definition is from the United States Census Bureau Dictionary [Census tract (CT) - Census Dictionary (statcan.gc.ca)]. 4 Definition is from the United States Census Bureau Terms and Definitions [Terms and Definitions (census.gov)]. 5 See information about this community indicators project from the foundation’s website at: 2019 KIDS COUNT Data Book - The Annie E. Casey Foundation (aecf.org). 6 Information about this community indicators project can be accessed from: http://austintexas.gov/ sites/default/files/files/Health/CHA-CHIP/2018_Travis_County_CHIP_FINAL_9.12.18.pdf

Community Indicators

5

Community Indicators Community indicators are measures of specific aspects of community well-being. Aspects of community wellbeing may include economic well-being of the residents residing in the designated community, their social wellbeing, their environmental wellbeing, their health and safety, etc. For example, the Center for Disease Control and Prevention (CDC) in the U.S. has much data about public health indicators. These indicators reflect many health-related topics: alcohol use, arthritis, asthma, autism, birth defects, breastfeeding, cancer, chronic kidney disease, diabetes, heart disease, etc. Within each topic indicators are concrete measures used to guide data collection. For example, the CDC captures data related to alcohol use using the following indicators. Specifically, prevalence of alcohol use is captured using the following indicator: Percent of adults aged 18 and over who had at least one heavy drinking day (five or more drinks for men and four or more drinks for women) in the past year. In contrast, mortality related to alcohol use is usually captured through two indicators: Number of alcoholic liver disease deaths and Number of alcoholinduced deaths, excluding accidents and homicides.7 A good example of community indicators is the AARP’s Livability Index (www. livabilityindex.aarp.org). The community indicators inherent in the Livability Index was developed by the AARP Public Policy Institute. The institute rates every neighborhood and community in the United States using 60 indicators spread across seven categories of livability: housing, neighborhood, transportation, environment, health, engagement, and opportunity. The institute uses more than 50 national sources of data. AARP defines a “livable community” to be a community that is safe and secure, has affordable and appropriate housing and transportation options, and offers supportive community services. These community conditions and services coalesce to enhance the quality of life of elderly residents by encouraging personal independence, fostering aging residents to engage in the community’s civic, economic, and social life. See Box 1.1 for more information about the AARP’s Livability Index. Box 1.1 AARP’s Livability Index Captures Community Quality of Life of Local Places in the United States The Livability Index (www.livabilityindex.arrp.org/) is a composite index of 60 indicators categorized into seven broad dimensions of community livability: housing, neighborhood, transportation, environment, health, engagement, and opportunity. Metric values and policy points of the indicators within each dimension are combined to create dimension score. Each dimension contains (continued)

7

Information about these indicators can be accessed from: https://www.cdc.gov/nchs/fastats/ alcohol.htm

6

1 Introduction

Box 1.1 (continued) 4–9 metrics and 2–5 policy points. Metrics capture how livable a community currently is; policy points capture steps communities take to increase their future livability. In turn, dimension scores are then averaged to create an overall livability score for the community question in question (selected U.S. neighborhood, city, or county). Housing (affordability and access): The metrics involved in this dimension are housing accessibility (% of housing units in a neighborhood with zero-step entry—can be entered by foot, wheelchair, or walker), housing options (% of housing units in a neighborhood that are not single-family, detached homes), housing affordability/cost per month (monthly housing costs measured at the neighborhood level), housing affordability/housing cost burden (% of income devoted to monthly housing costs measured at the neighborhood level), and housing affordability/availability of subsidized housing (number of subsidized housing units per 10,000 people measured at the neighborhood level). The policies involved in this dimension are housing accessibility (state and local that make housing accessible for people of all abilities), housing affordability/trust funds (state and local funds that support the development and preservation of affordable housing), housing options (state laws guaranteeing notice and/or first right of purchase to residents of manufactured housing communities to sale), housing affordability/foreclosure prevention and protection (state policies and programs that protect homeowners from losing their homes to foreclosure), and comprehensive livability commitment (communities that have taken comprehensive steps to prepare for the aging of the U.S. population). Neighborhood (access to life, work, and play): The metrics involved in this dimension are proximity to destinations/access to grocery stores and farmers’ markets (number of grocery stores and farmers’ markets within a half-mile measured at the neighborhood level), proximity to destinations/ access to parks (number of parks within a half-mile measured at the neighborhood level), proximity to destinations/access to libraries (number of libraries located within a half-mile measured at the neighborhood level), proximity to destinations/access to jobs by transit (number of jobs accessible within a 45-min transit commute measured at the neighborhood level), proximity to destinations/access to jobs by auto (number of jobs accessible within a 45-min automobile commute measured at the neighborhood level), mixed-use neighborhoods (mix of jobs within a mile measured at the neighborhood level), compact neighborhood (combined number of jobs and people per square mile measured at the neighborhood level), personal safety (combined violent and property crimes per 10,000 people measured at the county level), and neighborhood quality (% of vacant housing units measured at the neighborhood level).The policies involved in this dimension are mixed-use neighborhoods (continued)

Community Indicators

7

Box 1.1 (continued) (state and local programs that support transit-oriented development) and comprehensive livability commitment (communities that have taken comprehensive steps to prepare for the aging of the U.S. populations). Transportation (safe and convenient options): The metrics involved in this dimension are convenient transportation options/frequency of local transit service (total number of buses and trains per hour in both directions for all stops within a quarter-mile measured at the neighborhood level), accessible system design (% of transit stations and vehicles that are ADA-accessible measured at the metro area level), convenient transportation options/walking trips (estimated walk trips per household per day measured at the neighborhood level), convenient transportation options/congestion (estimated total hours that the average commuter spends in traffic each year measured at the metro are level), transportation costs (estimated household transportation costs measured at the neighborhood level), safe streets/speed limits (average speed limit on street and highways measured at the neighborhood level), and safe streets/crash rate (annual average number of fatal crashes per 100,000 people measured at the neighborhood level). The policies involved in this dimension are safe streets (state and local complete streets policies), convenient transportation options/human services transportation coordination (state human services transportation coordination councils), convenient transportation options/volunteer driver policies (state policies that remove barriers to volunteer driver programs), and comprehensive livability commitment (communities that have taken comprehensive steps to prepare for the aging of the U.S. population) Environment (clean air and water): The metrics involved in this dimension are water quality (% of the population getting water from public water systems with at least one health-based violation during the past year measured at the county level), air quality/regional air quality (number of days per year when regional air quality in unhealthy for sensitive populations measured at the county level), air quality near-roadway pollution (% of the population living within 200 meters of a high-traffic road with more than 25,000 vehicles per day measured at the neighborhood level), and air quality/local industrial pollution (toxicity of airborne chemicals released from nearby industrial facilities measured at the neighborhood level). The policies related to this dimension are resilience/state utility disconnection policies (state date-based policies prohibiting disconnection of utility service), resilience/local multihazard mitigation plans (approved local multi-hazard mitigation plans), energy efficiency (state policies that support energy-efficient buildings, facilities, and appliances), and comprehensive livability commitment (communities that have taken comprehensive steps to prepare for the aging of the U.S. population). (continued)

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1 Introduction

Box 1.1 (continued) Health (prevention, access and quality): The metrics involved with this dimension are healthy behaviors/smoking prevalence (estimated smoking rate measured at the county level), healthy behaviors/obesity prevalence (estimated obesity rate measured at the county level), healthy behaviors/access to exercise opportunities (% of people who live within a half-mile of parks and within a mile of recreational facilities measured at the county level), access to health care (severity of clinician shortage measured at the health professional shortage area level from 0 to 25), quality of health care/preventable hospitalization rate (number of hospital admissions for conditions that could be effectively treated through outpatient care per 1000 patients measured at the hospital service area level), and quality of health care/patient satisfaction (% of patients who give are hospitals a rating of 9 or 10 indicating the highest level of satisfaction measured at the hospital area level). The policies involved in this dimension are healthy behaviors (state laws that prohibit smoking in workplaces, restaurants, and bars) and comprehensive livability commitment (communities that have taken comprehensive steps to prepare for the aging of the U.S. population). Engagement (civic and social involvement): The metrics involved in this dimension are internet access (% of residents who have access to 3+ wireline internet service providers and 2+ providers that offer maximum download speeds of 50 megabits per second measured at the neighborhood level), civic engagement/opportunity for civic involvement (number of civic, social, religious, political, and business organizations per 10,000 people measured at the county level), civic engagement/voting rate (% of people ages 18 or older who voted in the last presidential election measured at the county level), social engagement/social involvement index (extent to which residents eat dinner with household members, see or hear from friends and family, talk with neighbors, and do favors for neighbors measured at the metro area scale from 0 to 2), and social engagement/culture, arts, and entertainment institutions (number of performing arts companies, museums, concert venues, sports stadiums, and movie theaters per 10,000 people measured at the neighborhood level). The policies involved in this dimension are internet access (absence of state policies that prevent cities from operating public broadband networks), civic engagement (state laws allowing early, no excuse absentee, or mail-in voting), equal right/local human rights commissions (local human rights commissions), equal rights/local LGBT anti-discrimination laws (total score of 75 or greater from the Human Rights Campaign Municipality Equality Index), and comprehensive livability commitment (communities that have taken comprehensive steps to prepare for the aging of the U.S. population). Opportunity (inclusion and possibilities): The metrics involved in this dimension are equal opportunity (Gini coefficient capturing the gap between (continued)

Community Indicators Projects

9

Box 1.1 (continued) the rich and the poor measured at the county level), economic opportunity (number of jobs per person in the workplace measured at the metro area level), educational opportunity (adjusted 4-year high school cohort graduation rate measured at the school district level), and multi-generational communities (age-group diversity of local population compared to the national population measured at the neighborhood level). The policies involved in this dimension are local fiscal health (local government AAA general obligation bond rating), economic opportunity (state minimum wag is higher than the federal minimum wage and is adjusted for increases in the cost of living), and equal opportunity (state policies that expand upon the federal Family and Medical Leave Act to provide additional leave benefits to workers). Source: Adapted from AARP’s Public Policy Institute’s in relation to the Livability Index (https://livabilityindex.aarp.org/) Community indicators is, of course, a major focus of community indicators projects and a core aspect of this book. In Chap. 1 of the book we will discuss how community indicators in some depth as a function of two approaches: top-down versus bottom-up approaches. The top-down approach reflects an approach to selecting community indicators in which researchers use well-established theoretical models of community well-being to generate a set of community indicators that are best suited to the community in question. These theoretical models of community well-being include personal utility, opulence, just society, human development, and sustainability. The bottom-up approach involves the use of representatives from various community stakeholders to identify important community well-being goals and translate these goals into tangible measures.

Community Indicators Projects Community indicators projects refers to a community-wide effort to organize, plan, collect and analyze community well-being data, and disseminate the results to community stakeholders in ways to influence community leaders to take future action for the purpose of enhancing community well-being (Chambers & Swain, 2006). Community indicators projects are designed to accomplish four major goals: (1) assess the quality of life in the selected geographic unit, (2) educate community residents about the economy, education, health, public safety, natural environment, and social environment, (3) act as a catalyst for social and political change, and (4) help evaluate the impact of social and political programs and policies (cf. Zachary, 2009). See Box 1.2.

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Box 1.2 Objectives of a Community Indicators Project: The Milwaukee’s Menomonee River Valley The objectives of the Milwaukee’s Menomonee River Valley indicators project are multifold. These include: – To raise awareness in the community regarding the current state of the Menomonee Valley and the progress made towards its revitalization; – To create an information clearinghouse on data related to environmental, economic, and social indicators; – To promote the principles of sustainability in an urban context by exploring issues and assembling data in a more holistic manner that considers economic, environmental, and social concerns; – To generate practical synthesis of the raw data for the benefit of a wide variety of users; – To stimulate research interest in the Valley as a complex laboratory for studying urban environments. Source: Adapted from De Sousa, Gramling, and LeMoine (2009, p. 83) However, there are instances in which a situation arises in a community that warrants the development of a monitoring system. An example involves the development of the Clark County Monitoring System (Conway et al., 2009). This monitoring system was prompted by a major event anticipated to create hazardous conditions in Clark County. Specifically, the U.S. Department of Energy made plans to ship 77,000 metric tons of high-level nuclear waste from civilian nuclear reactor sites and weapon facilities from throughout the country through Clark County, Nevada on its way for permanent geological burial at a repository at Yucca Mountain, Nevada. In response to this event Clark County officials developed and indicator-based monitoring program to capture the possible changes to the area’s socio-economic, fiscal, environmental, and public health and safety. A typical community indicators project involves several integral elements: organization, planning, data collection, data analysis and reporting, and dissemination and promotion of the results (see Fig. 1.1). Organization refers to efforts related to organize the community indicators project into an organizational entity with a bureaucratic structure. The organization’s core function is, of course, the planning, data collection, data analysis and reporting, and dissemination and promotion of the results. To accomplish these tasks, a bureaucratic structure has to be in place to oversee the entire operation. Like all functioning organizations, the bureaucratic structure has to reflect the traditional functional units such as operations, finance, accounting, human resources, marketing, information technology, etc. Planning involves identifying stakeholder groups that can be involved in the community indicators project, recruiting these groups, identifying representatives from the stakeholder groups, communicating and meeting with these representatives, raising financial resources, obtaining support from government officials and

Community Indicators Projects

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Fig. 1.1 Elements and processes involved in community indicators projects

Data Collection

Planning

Organization

Data Analysis

Data Dissemination

community leaders, deciding on which community indicators to focus on, among many other planning-related tasks. Data collection, data analysis, and reporting involve highly technical and research-related tasks. They are usually performed by those with research-related expertise such as social scientists, survey researchers, statistical analysts, and data mining specialists. Finally, we have the dissemination and reporting aspects of the project. The project administrator coupled with the marketing and public relations staff take over these tasks to disseminate the projects results to the various stakeholder groups and other community leaders. The goal, of course, is to influence future decision making related to community development and to address quality-of-life issues of community residents. The primary goal of community indicators projects is to provide allow community leaders and government officials develop and evaluate the extent to which community-level programs and policies have been effectiveness in improving community well-being. As such, community indicators projects provide much information about the quality-of-life effectiveness of programs and policies in place. Such information signals the need for reinforcing the programs and policies in place or signals the need to make changes or adjustments in those programs and policies. As such, community indicators projects can be viewed as foundational to evidencebased community interventions and development. There are secondary goals related to community indicators projects too. Here is a list of secondary goals: • Community indicators projects serve to engage and connect varied stakeholder groups within a community. Community cohesion is a secondary outcome.

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• Community indicators projects serve to motivate community leaders and government officials to take action on goals deemed important to the community at large. Motivated social change is usually a significant result. • Community indicators projects help “tell a meaningful story” of the community, its people, its living conditions, as well as the community’s goals and aspirations. A sense of community belonging and history is yet another tangible outcome.

Summary This chapter described three core concepts that are paramount to this book, namely “community,” “community indicators,” and “community indicators projects.” A “community” was defined as a grouping of people based on some geographic, demographic, or other social criterion. are “community indicators”? “Community indicators” were defined as measures of specific aspects of community well-being. Aspects of community wellbeing may include economic well-being of the residents residing in the designated community, their social wellbeing, their environmental wellbeing, their health and safety, etc. An example of health indicators may be percent of adults aged 18 and over who had at least one heavy drinking day (five or more drinks for men and four or more drinks for women) in the past year. The AARP’s Livability Index is a good example of a composite index of community quality of life involving seven sets of indicators: housing, neighborhood, transportation, environment, health, engagement, and opportunity. An example of a community indictor capturing housing wellbeing is percent of housing units in a neighborhood with zero-step entry—can be entered by foot, wheelchair, or walker (capturing housing accessibility). More broadly, “community indicators projects” was discussed in terms of a community-wide effort to organize, plan, collect and analyze community well-being data, and disseminate the results to community stakeholders in ways to influence community leaders to take future action for the purpose of enhancing community well-being.

Progress Check In this chapter the reader should be able to answer the following questions: 1. What is a “community”? 2. What are “community indicators”? 3. What are good examples of community indicators from a well-established community quality-of-life index? 4. What are “community indicators projects”?

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Progress Check Answers 1. What is a “community”? A community, is a grouping of people based on some geographic, demographic, or other social criterion. Most community indicators projects focus on geographic units as explicitly defined through a country’s census. A “community” can also be defined at a more granular level within a specific geographic region such as children in a specific locale, the elderly in a specific region, the poor in a specific city, etc. 2. What are “community indicators”? Community indicators are measures of specific aspects of community well-being. Aspects of community wellbeing may include economic well-being of the residents residing in the designated community, their social wellbeing, their environmental wellbeing, their health and safety, etc. An example of health indicators may be percent of adults aged 18 and over who had at least one heavy drinking day (five or more drinks for men and four or more drinks for women) in the past year. 3. What are good examples of community indicators from a well-established community quality-of-life index? The AARP’s Livability Index is a good example. It is designed to capture community quality of life of local places in the United States. The Livability Index is made up of 60 indicators categorized into seven broad dimensions of community livability: housing, neighborhood, transportation, environment, health, engagement, and opportunity. Metric values and policy points of the indicators within each dimension are combined to create dimension score. Each dimension contains 4–9 metrics and 2–5 policy points. Metrics capture how livable a community currently is; policy points capture steps communities take to increase their future livability. In turn, dimension scores are then averaged to create an overall livability score for the community question in question (selected U.S. neighborhood, city, or county). Good examples of community indicators from the Livability Index related to housing include percent of housing units in a neighborhood with zero-step entry—can be entered by foot, wheelchair, or walker (capturing housing accessibility), percent of housing units in a neighborhood that are not single-family, detached homes (capturing housing options), monthly housing costs measured at the neighborhood level (capturing housing affordability/cost per month), percent of income devoted to monthly housing costs measured at the neighborhood level (capturing housing affordability/housing cost burden), and number of subsidized housing units per 10,000 people measured at the neighborhood level (capturing housing affordability/ availability of subsidized housing). 4. What are “community indicators projects”? Community indicators projects refers to a community-wide effort to organize, plan, collect and analyze community well-being data, and disseminate the results to community stakeholders in ways to influence community leaders to take future action for the purpose of enhancing community well-being.

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References Chambers, M., & Swain, D. (2006). Quality indicators for progress: A guide to community qualityof-life assessments. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 267–322). Kluwer Academic. Conway, S., Aguero, J., & Navis, I. L. (2009). The Clark County monitoring system—An early warning indicator system for Clark County, Nevada. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases III (pp. 41–77). Springer. De Sousa, C., Gramling, B., & LeMoine, K. (2009). Evaluating progress toward sustainable development in Milwaukee’s Menomonee River Valley: Linking Brownfield’s Redevelopment with community quality-of-life. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases III (pp. 79–97). Springer. Prabhakar, D., Qualls-Hampton, R. Y., Jackson, R., & Cardarelli, K. M. (2009). Mental health indicator parity: Integrating national, state, and local data. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 81–109). Springer. Sung, H.-K., & Phillips, R. G. (2018). Indicators and community well-being: Exploring a relational framework. International Journal of Community Well-Being, 1(1), 63–79. US Census Bureau. (2019, January 21). 2010 census tallies of census tracks, block groups & blocks. US Census Bureau. Retrieved March 9, 2021, from 2010 Census Tallies of Census Tracts, Block Groups & Blocks - Geography - U.S. Census Bureau (archive.org) Warner, J. B. (2009). Jacksonville’s race relations progress report: Creating change through community indicators. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-oflife indicators: Best cases IV (pp. 141–161). Springer. Zachary, D. (2009). Connecting outcomes to indicators: The Santa Cruz County California Community Assessment Project (CAP). In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases III (pp. 1–20). Springer.

Chapter 2

Theoretical Foundations

Learning Objectives In this chapter the reader should be able to answer the following questions: 1. What are examples of theoretical concepts that underlie the planning of community indicators projects? 2. How does the concept of personal utility guide the development of community indicator projects? 3. How does the concept of opulence guide the development of community indicator projects? 4. How does the concept of social justice guide the development of community indicator projects? 5. How does the concept of human need satisfaction guide the development of community indicator projects? 6. How does the concept of sustainability guide the development of community indicator projects?

Introduction Community wellbeing (or quality of life) is a construct that focuses on the fulfillment of needs and desires of its residents (Sung & Phillips, 2018, p. 64). As such, the construct embraces a wide range of economic, social, environmental, political, cultural dimensions reflecting how well functions of community are governed and operating (Chanan, 2002; Cox et al., 2010; Haworth & Hart, 2007; Phillips & Wong, 2017). What are these theoretical notions? There are many, but that is not to say we cannot describe popular theoretical notions. We will cover five theoretical concepts that many community indicator researchers have used with a certain degree of success. These are (Sirgy, 2011): © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_2

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The concept of personal utility The concept of opulence The concept of the just society The concept of satisfaction of human needs The concept of sustainability

The Concept of Personal Utility Many community indicators projects have been developed guided by the theoretical notion of personal utility (e.g., Richards & Kamman, 2006; Sirgy & Cornwell, 2001; Sirgy & Rahtz, 2006; Sirgy et al., 2000). The basic premise is that a community rated high on quality-of-life dimensions is a community that has conditions and services that satisfy the needs of community residents. Community conditions are, in essence, outcomes of community action. Examples of community conditions include quality of the environment in the community (air, water, land, etc.), rate of change to the natural landscape (deforestation, housing/commercial development, loss of agricultural land, ridge-line development, etc.), race relations in the community, cost of living in the community, crime in the community, ties with people in the community, and neighborhood and housing conditions. These conditions can be classified in terms three sets of community conditions: (a) economic, (b) social, and (c) physical (see Table 2.1). Note that many community conditions are outcomes of community action. Community action occurs through community organizations or services. Community services are organizations and institutions within the community that serve particular needs of community residents, and ultimately affect community outcomes or conditions. Community services are typically grouped in three major categories: (a) business organizations, (b) non-profit organizations, and (c) government organizations (see Table 2.2). Business organizations that serve the needs of community residents are quite varied. Examples include department stores, grocery stores, discount stores, specialty stores, shopping malls, banks and investment services, realty companies, medical facilities, private schools, movie theatres, restaurants, automobile dealers, Table 2.1 Community conditions and services Community conditions • Economic conditions (e.g., job opportunities, quality of jobs, income and wealth, cost of living) • Social conditions (e.g., crime and public safety, racial/ethnic relations, social cohesion, education, leisure and recreation) • Physical conditions (e.g., incidence of disease, air pollution, noise pollution, land pollution, water pollution, population density, traffic and congestion)

Community services • Government services (e.g., police, fire, refuse, water, transportation, healthcare, education, social services, job training) • Nonprofit services (e.g., religious, healthcare, social services, education) • Business services (e.g., banking, shopping malls, department stores, drug stores, supermarkets, automotive)

The Concept of Personal Utility Table 2.2 Economic indicators

17 Economic indicators of a community • Household income • Rate of unemployment • Type of jobs • Quality of jobs • Cost of living • Housing costs • Poverty • Homelessness • Number of new businesses opened • Number of full-time jobs created • Business closures • Number of building permits issued

Residents’ Evaluations of Community Conditions Residents’ Perception of Community QOL

Community QOL

Residents’ Evaluations of Community Services

Fig. 2.1 Residents’ perception of community quality of life (QOL) ¼ community QOL

telecommunication and media services, etc. Non-profit organizations may include adoption/foster care services, non-profit counseling/support services, non-profit cultural/recreation services, non-profit educational services, non-profit legal services, non-profit senior citizen services, non-profit healthcare facilities, among others. Government organizations include law enforcement, fire protection, transportation, public utilities, public recreation facilities, public schools, among others. It is important to note that the concept of personal utility is grounded in the subjective experience of community residents. That is, it is not the “objective” state of community conditions and services that count. Instead, personal utility refers to the utility extracted by individual community residents as they perceive these community conditions and services. Therefore, community indicators projects guided by the theoretical notion of personal utility tend to take shape through subjective indicators involving community residents’ assessment of quality of community conditions and services—in other words, perceived community quality of life. See Fig. 2.1. Typically, community indicator projects guided by the concept of personal utility employ community surveys to capture residents’ perception and evaluation of

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community conditions and services. Because of the subjective nature of these indicators, emphasis is placed on primary data collection instead of compiling secondary data. We will describe primary and secondary data collection in Chap. 5 in some detail. See Box 2.1 for an example of the use of the concept of personal utility in the development of an indicators project, namely the Gensee County Indicators Project in Michigan, USA. Box 2.1 City of Flint and Genesee County Indicators Project in Michigan, USA This indicators project involves the assessment of satisfaction with various dimensions of community life in the City of Flint and Genesee County in Michigan, USA. Here are selected satisfaction measures: – Satisfaction with the quality of life: satisfaction with the community at large; satisfaction with the quality of neighborhood life – Satisfaction with the economy and jobs: satisfaction with property taxes and cost of living; satisfaction with jobs – Satisfaction with parks and recreation: satisfaction with parks and green space; satisfaction with entertainment facilities and activities; satisfaction with recreational facilities and activities – Satisfaction with neighborhood and home: satisfaction with neighbors; satisfaction with safety in the neighborhood and security from break-ins; satisfaction with racial mix of neighborhood – Satisfaction with social dimensions: satisfaction with family life; satisfaction with friends and acquaintances; satisfaction with church-related activities; satisfaction with race relations in the community – Satisfaction with aesthetic dimensions: satisfaction with the appearance of homes in one’s neighborhood; satisfaction with appearance of other residential areas; satisfaction with appearance of public places; satisfaction with appearance of business areas in the community – Satisfaction with government and leadership: satisfaction with community leaders; satisfaction with leaders in government – Satisfaction with government services: satisfaction with police protection for neighborhoods; satisfaction with fire protection; satisfaction with personal safety in public places; satisfaction with crime prevention efforts; satisfaction with animal control; satisfaction with garbage collection; satisfaction with street conditions – Satisfaction with traffic and transportation: satisfaction with the amount of traffic on own street; satisfaction in traffic conditions in the community; satisfaction with public transportation – Satisfaction with educational systems: satisfaction with public schools (K-12); satisfaction with local colleges and universities; satisfaction with public libraries (continued)

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Box 2.1 (continued) – Satisfaction with health care: satisfaction with medical and dental services; satisfaction with hospitals – Satisfaction with local media: satisfaction with local newspaper; satisfaction with local/regional radio stations; satisfaction with local/regional TV stations – Satisfaction with shopping facilities: satisfaction with shopping activities; satisfaction with local grocery stores Source: Adapted from Widgery (2004, pp. 159–163)

The Concept of Opulence Many community planners believe that their basic mission is essentially economic development. This is because economic development is the foundation for social development. When a community achieves satisfactory levels of economic development, social development follows. There is enough evidence to support the notion that economic development is strongly related to social development (i.e., economic development is highly correlated with community indicators of health, high quality government institutions, environmental pollution, and subjective well-being). Therefore, community indicators projects guided by the opulence concept collect data on economic indicators such as household income, unemployment, type of jobs, quality of jobs, cost of living, poverty, and homelessness (see Table 2.2). Such community planners (and indicator researchers) are essentially economic development specialists or community planners heavily influenced by economic development specialists (see Fig. 2.2). See example of community indicators project focusing on housing costs in Box 2.2. Box 2.2 Housing Costs in Boston, USA To draw a picture of quality of life in the Boston metropolitan area we need to closely examine the high cost of living in this region. In the not so distant past, certain life cycle patterns helped moderate housing cost in Boston. Examples of these cycle patterns include college students graduating and leaving the city (continued)

Economic Development

Fig. 2.2 Economic development ¼ community QOL

Community QOL

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Box 2.2 (continued) for greener pasture; companies tended to locate outside of Boston in the suburbs to decrease commute time into the city; and young professionals moved out of the city once they had kids to give their kids more room to grow and play. Not anymore. Nowadays, young professionals prefer to live in the city. As such, many businesses have responded to the young professionals need to be close to city central by locating back closer to the city core. Young families seem to be more willing to sacrifice space to stay in the city. Empty nesters living in the suburbs are downsizing and moving back to the city. This increased demand to live in Boston has caused a dramatic rise of home value since 2010, with the sharpest increase inside the city of Boston. It is estimated that nationwide home values tend to be only 3% higher than their previous peak, whereas home values in Boston in 2017 have registered 48% higher than their previous peak. The increase in home value of course benefit longstanding homeowners, but they create a significant hurdle to homeownership for renters who make up roughly two-thirds (65%) of residents in Suffolk County. Median monthly rent for new listings in Boston in 2017 was estimated at $2613/month, or $31,356/year. This computes to 51% of median household income in Boston. Housing policy defines households that spend more than 30% of their income on housing to be “housing cost burdened” and those that spend more than 50% to be “extremely housing cost burdened.” This means that a household earning at the median and paying median rent for a current listing is extremely housing cost burdened in Boston. Sharp housing costs increases can significantly restrict economic opportunity, especially when these costs outpace wage gains. This is what’s been happening in the City of Boston: While housing costs have skyrocketed in Boston, wages have stagnated for most people in the labor force. This is a sad picture of quality of life in the City of Boston. Source: Adapted from Schuster and Ciurczak (2018, pp. 23–25)

The Concept of the Just Society Many community planners and indicator researchers develop community indicators projects guided by the implicit notion that community quality of life is a community in which its residents enjoy a high level of social justice (see Fig. 2.3). What is social justice? Perhaps we can quote a famous philosopher, John Rawls (Rawls, 1971, 1975), who devoted much of his philosophical writings to the concept of social justice. Society is considered just if two distinct principles are met. The first principle of a just society holds when there is equality in the assignment of basic rights and duties. The second principle of the just society holds when inequalities are justified to benefit the least advantaged members of the society.

The Concept of the Just Society

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Social Justice

Community QOL

Fig. 2.3 Social justice ¼ community quality of life (QOL) Table 2.3 Community quality-of-life (QOL) indicators guided by social justice Equality in basic rights and duties • Right to meet basic needs (e.g., % of population below poverty line; government entitlement programs directed to the poor and equitable appropriations across all community groups) • Right to safety (e.g., crime rate; government programs and expenditures to combat community crime and equitable appropriations across all community groups) • Right to employment (e.g., unemployment, educational attainment; literacy; job skills; job training programs and equitable appropriations across all community groups) • Right to a healthful environment (e.g., air pollution, water pollution, land pollution, noise pollution; incidence of disease; government programs to combat environmental pollution and equitable appropriations across all community groups) • Duty to pay taxes (e.g., measures of corporate welfare; tax evasion indicators; % of undeclared work; government programs to reduce tax evasion and equitable appropriations across all community groups) • Duty to vote (e.g., % of eligible voters voting; government programs to increase voter turnout and equitable appropriations across all community groups)

Inequality to benefit the least advantaged • Children (e.g., under five mortality rate, 1-year old children fully immunized against tuberculosis and measles; teen pregnancy rate, low-birth weight infants, underweight children under age 5) • Women (e.g., ratio of females graduating high school to males; ratio of females unemployed to males; ratio of median wage of females to males; educational scholarships available to females relative to males; job training and assistance programs available to females relative to males) • Minorities (e.g., ratio of minorities graduating high school to non-minorities; ratio of minorities unemployed to non-minorities; ratio of median wage of minorities to non-minorities; educational scholarships available to minorities relative to non-minorities; job training and assistance programs available to minorities relative to non-minorities) • The Poor (e.g., educational scholarships available to the poor relative to the non-poor; job training and assistance programs available to the poor relative to the non-poor; government expenditures to the poor relative to the non-poor) • The Disabled (e.g., ratio of disabled graduating high school to non-disabled; ratio of disabled unemployed to non-disabled; ratio of median wage of disabled to non-disabled; educational scholarship available to the disabled relative to the non-disabled; job training and assistance programs for the disabled relative to the non-disabled; government expenditures to the disabled relative to the non-disabled)

Table 2.3 shows examples of community indicators that reflect Rawls’ concepts of equality in basic rights and duties and inequalities to benefit the least advantaged members of the community. Equality in basic rights and duties can be viewed in

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terms of at least six dimensions: (a) right to meet basic needs, (b) right to safety, (c) right to employment, (d) right to a healthful environment, (e) duty to pay taxes, and (f) duty to vote. The table also shows examples of community quality-of-life indicators capturing these basic rights and duties of community residents. With respect to Rawls’ second principle of social justice (inequalities are justified to benefit the least advantaged members of the society), Table 2.3 identifies examples of least advantaged members of society: children, women, minorities, the poor, and the disabled. The table also shows examples of community indicators related to each group. See Box 2.3 for examples indicators of “Equity in Justice” as applied in the Sustainable Seattle Indicators Project (Holden, 2006). Box 2.3 Indicators of “Equity in Justice” as Applied in the Sustainable Seattle Indicators Project Although the Sustainable Seattle Indicators Project is guided by mostly sustainability principles, the project included a set of indicators that reflected social justice. These indicators are essentially broken down by demographic groups such as ethnicity, sex, age, among others (and these groups are then compared): – – – – – – –

Adult literacy High school graduation Ethnic diversity of teachers Arts instruction Volunteer involvement in schools Community service by youth Juvenile crime

Source: Adapted from Holden (2006, p. 188) It should be noted that there are many indicators projects that focus on specific disadvantaged populations such as children, specific minority groups, the disabled, the elderly, etc. Focusing on the needs of a particular disadvantaged group and capturing the quality of life of this segment is an exemplar of the social justice concept in indicators projects. See Box 2.4 for quality-of-life indicators developed by the Sacramento County Children’s Indicators Project. Box 2.5 shows indicators of race relations in the City of Jacksonville, Florida, USA (Warner, 2009). Box 2.6 shows results of the EQUALABEL Project on gender equality in nine European cities (Moreno Minguez, 2009).

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Box 2.4 Sacramento County Children’s Indicators Project Demographics – Indicators: population by groups (annual population figures by age, gender, and race/ethnicity); population diversity (population by race/ethnicity); family composition (number of children under 18 years of age living in families with their own parents by married-couple and single-parent); children living in poverty (number and percentage of children living below poverty; number and percentage of publicly enrolled K-12 students participating in the free and reduced-price meals program) Family economics – Indicators: availability of quality child care (total number of licensed child care slots available by child age groups and change over time); employment (annual averages by number and percentage of people employed within selected industry categories in the Sacramento Metropolitan Statistics Area; average unemployment rate for Sacramento County); living wage (two hypothetical family budgets: family of three, mother employed at $15.40/ h. with two children aged 18 months and 7 years; family of four, father and mother work full-time in maintenance and child care, respectively, with annual income of $38,750, and two children aged 18 months and 7 years); housing affordability (average fair market rental rates by number of bedrooms); child care affordability (child care costs compared to familyincome figures); highway congestion (travel time to work by percentage of employed persons driving to work); public transportation ridership (total annual boardings and the 8 average weekday ridership) Education – Indicators: school readiness (measures of school readiness); student and family support services (counselor/student ratio and nurse/student ratio); school enrollment, public and private (number of students enrolled in public and private schools); classroom teachers’ credentials and experience (percentage of students enrolled in public schools by credential type and teachers that are first-year and second-year teachers); test scores (math and reading scores by grade for Sacramento County and California); academic performance index growth (academic performance index refers to a school’s or school district’s performance on statewide student assessments); graduation rates (percentage of students graduating from public schools who entered the ninth grade 4 years earlier); postsecondary enrollment (number of 12th grade graduates completing courses required for admission to the University of California and California State University (continued)

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Box 2.4 (continued) systems); per-pupil expenditures (cost per student is the direct cost of education services divided by student average daily attendance) Health – Indicators: health care access (MediCal enrollment, children without health insurance coverage); sexually transmitted infections (rates of gonorrhea and chlamydia by age); prenatal care (percentage of live births for which prenatal care was received during the first trimester of pregnancy); breastfeeding (number and percentage of mothers by race/ethnicity who initiate exclusive breastfeeding and combination breastfeeding/formula at the time of discharge from the hospital); birth rates among teens (number and percentage of teen births by age and ethnicity); immunization by age 2 (percentage of children fully immunized by second birthday); dental health (percentage of enrolled children with dental cavities or in need of immediate dental care); air quality (number of days air quality exceeded the 1-h state ozone standard; number of days air quality exceeded 1-h and 8-h federal standards); children suffering from asthma (percentage of children diagnosed with asthma); tobacco use and youth (percentage of students by grade level (7, 9, 11) who have smoked at least one cigarette in the last 30 days) Safety – Indicators: homeless children (number of children identified and children estimated as homeless by individual school districts); child deaths (number of child deaths by natural causes and injury-related causes); child abuse and neglect (number of reports and responses to suspected child abuse cases, number of children in out-of-home placement, and types of substantiated maltreatment); domestic violence (number of domestic violence-related calls received by law enforcement agencies); school violence and student crime rates (number of criminal incidences on school campuses per 1000 students by type of crime); juvenile felony arrest rates (number of arrests per 1000 juveniles aged 10–17); driving under the influence or DUI (number of felony and misdemeanor juvenile DUI arrests); runaways (number of juvenile missing persons reports filed in Sacramento County) Social and emotional wellbeing – Indicators: out-of-home placement (number of children in child welfaresupervised foster care by placement type and age); youth substance abuse (alcohol and drug usage by 11th grade youth); mental health services for children (number of youth receiving publicly funded mental health services by age group) Source: Adapted from Findeisen (2006, pp. 207–216)

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Box 2.5 Indicators in the 2007 Race Relations Progress Report: Jacksonville, Florida, USA Perceptions of race relations today: – Survey: Is racism a problem in Jacksonville? – Survey: Have you personally experienced racism? Notes: results are broken down by race/ethnicity. Education: – – – – –

FCAT reading proficiency: Elementary school FCAT reading proficiency: Middle school FCAT reading proficiency: High school High school graduation rates College continuation rates

Notes: FCAT ¼ Florida Comprehensive Achievement Test (the state standardized test used to measure student performance); results are broken down by race/ethnicity. Employment and income – Unemployment rates – Children in low-income households (free and reduced-price school lunch participation rates), by race/ethnicity – Leadership of 50 fast-growing private companies, in Jacksonville, by race/ ethnicity – Jacksonville city contracts, by race/ethnicity of business ownership Neighborhoods and housing – Conventional mortgage denial rates, by race/ethnicity – New owner-occupied home purchase loans, by race/ethnicity – Percent of public elementary school children attending desegregated schools, by race/ethnicity Survey: Perceptions of neighborhood safety, by race/ethnicity. Health – – – –

Heart disease death rate, by race/ethnicity Cancer death rate, by race/ethnicity Infant mortality rate, by race/ethnicity New HIV cases per 100,000 population, by race/ethnicity

Justice and the legal system – Inmate admissions per 1000 population for misdemeanors, by race/ ethnicity (continued)

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Box 2.5 (continued) – Inmate admissions per 1000 population for felonies, by race/ethnicity – Homicide rates, by race/ethnicity – Youths committed as delinquents, by race/ethnicity Politics and civic engagement – Voter registration, by race/ethnicity – Voter turnout, by race/ethnicity – Survey: Perception of influence in local government and decision-making, by race/ethnicity – Survey: Perception of lack of influence in local government and decisionmaking Source: Adapted from Warner (2009, pp. 160–161)

Box 2.6 Recommendations from the EQUALABEL Project: Gender Equality in Nine European Cities – Facilitate the change of values from a traditional model of gender relations to the democratic model of management and provision of local services for citizens – Identify existing shortfalls in gender policies with previous analysis and research. – Develop tools and instruments to promote participation of citizens in activities and actions related to gender equality through the creation of forums with different social agents of the local environment. – Make citizens and local workers aware of the meaning of gender equality through courses, seminars, advertising campaigns, etc. – Facilitate reconciliation of work and family life, managing local public services related to childhood and older people. – Municipalities have to advance dialogue and cooperation between social agents, such as employers and unions, with the objective of creating a model of labor relations (flexibilization of work schedule, home-work, etc.) that facilitates the access of females and males that have to take care of a family to public services managed by municipalities. – Develop activities with children and young people in the field of informal education to create an early socialization in egalitarian roles between males and females. – Promote citizen participation and reinforce associative and community networks to create a participation dynamic in order to involve citizens in programs and activities developed by the municipality. (continued)

The Concept of Human Need Satisfaction

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Box 2.6 (continued) – Develop and distribute guides of good practices regarding gender equality that contain examples of strategies to follow in employment, family, relations, etc. – Introduce systematic assessment programs, the lack of systematic assessment programs of gender policies developed on a local level means that beliefs and perceptions more than facts are what determines policies. – Increase the number and improve the training of experts that work in social policy programs related to gender issues and developed by municipalities. Source: Adapted from Moreno Minguez (2009, pp. 174–175)

The Concept of Human Need Satisfaction The basic premise underlying the concept of human need satisfaction is the notion that a community characterized as high in quality of life is one that plays a significant role in satisfying residents’ developmental needs. Developmental needs refer to a hierarchy of lower and higher-order needs such as health, safety, and economic needs (lower-order needs), as well as social, esteem, actualization, knowledge, and aesthetics needs (higher-order needs). Lower-order needs are basic needs and more pre-potent than higher-order needs. It is difficult to achieve higher-order needs without first and foremost attending to lower-order needs. To achieve a high level of quality of life, community residents must satisfy the full spectrum of their developmental needs—both lower- and higher-order needs. The challenge for community planners is to plan and implement programs and policies designed to enhance need satisfaction (the full spectrum of development needs of the community residents). Community indicators capturing the full spectrum of need satisfaction would allow for the assessment and monitoring of progress towards that end (see Fig. 2.4). Table 2.4 shows examples of community indicators capturing lower- and higherorder need satisfaction. Programs and policies targeting lower-order needs can be viewed in terms of nine dimensions: (a) environmental pollution, (b) disease incidence, (c) crime, (d) housing, (e) unemployment, (f) poverty and homelessness, (g) cost of living, (h) community infrastructure, and (i) illiteracy and lack of job skills. In contrast, programs and policies targeting higher-order needs involve nine

Human Need Satisfaction

Community QOL

Fig. 2.4 Human need satisfaction ¼ community quality of life (QOL)

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Table 2.4 Community quality-of-life (QOL) indicators guided by human need satisfaction Indicators of satisfaction of lower-order (basic) needs • Measures of environmental pollution (air, water, land, and noise) and environmental programs to reduce environmental ill being • Measures of disease incidence and healthcare efforts to reduce health-related ill being • Measures of crime and safety and law enforcement programs to reduce crime and enhance public safety • Measures of housing conditions and community programs to meet housing needs • Measures of unemployment and community programs to reduce work ill being • Measures of poverty/homelessness and community programs to assist the poor and the homeless • Measures of cost of living related to basic goods and services and community programs to reduce the cost of these basic necessities • Measures of community infrastructure (e.g., utilities, roads, transportation, telecommunications) and community programs to maintain a minimum level of infrastructure • Measures of illiteracy and lack of job skills and community efforts to eradicate illiteracy and enhance job skills

Indicators of satisfaction of higher-order (growth) needs • Measures of work productivity and income and community programs to enhance productivity and quality of work life • Measures of consumption of non-basic goods and services and community programs to enhance consumer well being • Measures of quality of leisure and recreation activities and community programs to enhance leisure well being • Measures of educational attainment and community programs to enhance education well being • Measures of the quality of community landscape and community beautification programs • Measures of population density and crowdedness and community programs to reduce crowdedness and congestion • Measure of arts and cultural activities and community programs to enhance cultural well being • Measures of intellectual activities and community programs to enhance knowledge well being • Measures of religious activities and community programs to enhance spiritual well being

different dimensions: (a) work productivity and income, (b) consumption of non-basic goods and services, (c) leisure and recreational activities, (d) educational attainment, (e) community landscape, (f) population density and crowdedness, (g) arts and cultural activities, (h) intellectual activities, and (i) religious activities. For an alternative application of human need satisfaction to urban quality-of-life indicators, see Box 2.7. The application focuses on Max-Neef’s theory of Human Scale Development, a well-recognized theory of human need satisfaction (Max-Neef, 1989). Box 2.7 Applying Max-Neef Model of Human Scale Development to Urban Areas – Subsistence needs: These needs are related to survival. They may include physiology, shelter, and mobility. Urban quality-of-life indicators related to these needs include having air and water quality, a workable energy infrastructure, food availability (and food systems), housing stock (and housing markets and regulations), neighborhood quality, temporary (continued)

The Concept of Human Need Satisfaction

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Box 2.7 (continued) settlement areas, transport infrastructure (and transport affordability and accessibility), multimodality, and proximity to destinations. – Protection needs: These needs include health, security, and safety. Urban quality-of life indicators related to protection needs include life expectancy (and healthcare quality, environmental quality (environmental action), and systems in place to mitigate disease transmission), legibility and comfort of built space, crime perception (and police and security services, social surveillance and neighborhood monitoring), communication systems, safe environment, civil defense and geographic risk features (and preparedness levels and training), and resilience of built environment and natural areas. – Affection needs: These needs include intimacy and aesthetics. Urban quality-of-life indicators related to these needs include meeting spaces and social amenities (and internet access and partner matching), access to counseling, green and blue areas (and accessibility of public spaces), and heritage preservation and biodiversity. – Understanding needs: These needs include knowledge and innovation. Urban quality-of-life indicators related to these needs include higher education and knowledge institutions (and community knowledge and education systems), schools, adult education, training, informal interaction spaces to facilitate entrepreneurship by sharing, matching and learning opportunities, R&D institutions and innovation districts. – Participation needs: These needs include community and society. Urban quality-of-life indicators related to these needs include community centers and meeting places (serve to build social capital and community cohesion), religious spaces and activities (to allow the development of urban subcultures) transitory and symbolic public spaces to facilitate commuting and holding social events. – Leisure needs: These needs include recreation and relaxation. Urban quality-of-life indicators related to these needs include entertainment and tourism amenities, arts and shopping areas, access to nature and resting places. – Creation needs: These needs include creativity and productivity. Urban quality-of-life indicators related to these needs include collective interaction spaces and media outlets (access to media and networks), meeting venues and cultural clusters (peer review and interaction), firms, incubators, and digital infrastructure (active sharing, matching and learning mechanisms), and advanced business service districts (employment markets and regulations). – Identity needs: These needs include belonging and recognition. Urban quality-of-life indicators related to these needs include heritage, monuments, and collective symbols (community cultures, faiths and traditions), (continued)

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Box 2.7 (continued) openness of urban structure; public protest spaces and street grids (sociocultural diversity) and public institutions (gender equality). – Freedom needs: These needs include autonomy and liberty. Urban qualityof-life indicators related to these needs include welfare systems, social emancipation policies, freedom to migrate to city, freedom to move in city, social responsibility, political systems, traditional practices, freedom to gather, and governance institutions. Source: Adapted from Cradoso et al. (2021)

The Concept of Sustainability The World Commission on Environment and Development defined sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (cited in Sung & Phillips, 2018, p. 74). Sustainable communities are those that enhance the economic, environmental, and social characteristics of a community so its residents can lead healthy, productive, enjoyable lives—higher quality of life. The three sets of community characteristics (economic, environmental, and social) are also referred to as “three E’s of Sustainability” (economy, environment, equity) and are employed by many indicators projects (Hardi & Zdan, 1997; United Nations Commission on Sustainable Development, 2001). Community planners, who use terms such as “sustainability,” “sustainable community,” “sustainable development,” and “sustainable growth,” tend to think in terms of the “three E’s” and their linkages. Environmental concerns cannot be isolated from the economy. Likewise, economic matters have ramifications in the social realm. The economy exists entirely within society or “equity,” but society is more than just the economy. Human relationships, the arts, religion, etc. are all part of society. Finally, society itself exists within the natural environment (see Fig. 2.5). Other quality-of-life researchers buy into the notion that the concept of sustainability means equal treatment of people and the ecosystem. In other words, sustainable development is a combination of human well-being and environmental wellbeing. One cannot have a good human condition in a bad environment--indicators of human well-being in a community have to be balanced with indicators of the environmental well-being. Table 2.5 shows dimensions and indicators commonly

Sustainability

Fig. 2.5 Sustainability ¼ community QOL

Community QOL

The Concept of Sustainability

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Table 2.5 Dimensions of indicators of human and ecosystem well-being Indicators of human well-being • Health and population (i.e., physical and mental health, disease, mortality, fertility, population change) • Wealth (i.e., income, poverty, inflation, employment, infrastructure, basic needs for food, water, and shelter) • Knowledge and culture (i.e., education, communication) • Community (i.e., institutions, law, crime, racial and ethnic strife) • Equity (i.e., distribution of benefits and burdens between social groups)

Indicators of ecosystem well-being • Land (i.e., diversity and quality of forests, farmland and other land ecosystems, including their modification, conversion, and degradation) • Water (i.e., diversity and quality of inland water and marine ecosystems, including their modification by dams and other structures, pollution and water withdrawal) • Air (i.e., local and indoor air quality) • Resource use (i.e., energy and materials, waste generation and disposal, recycling)

Source: Adapted from Prescott-Allen (2001)

used by sustainable development researchers. Boxes 2.8 and 2.9 provide examples of indicators project guided by the concept of sustainability. Box 2.8 New Zealand Sustainability Indicators Project The New Zealand Sustainability Indicators Project is led by the country’s bureau of statistics, Statistics New Zealand. The project focuses on seven domains related to sustainable development: – New Zealand’s changing population (how changes in population size, composition, distribution and growth influence the long-term sustainability of the social, economic, and natural environment). Indicators include population size (population growth rate); age structure; ethnic diversity; and aspects of population change (fertility, mortality, external migration, internal migration, etc.). – Environment and ecosystem resilience (whether the New Zealand natural environment is resilient and healthy). Indicators include atmosphere (greenhouse gases, ozone, air quality); land use patterns; oceans, seas and coasts (coastal water quality, fish stocks); fresh water (river water quality, drinking water quality); biodiversity (loss of indigenous vegetation, change in distribution of the Little Spotted Kiwi); biosecurity (total number of seizures by the Ministry of Agriculture and Fisheries Quarantine Service). – Economic growth and innovation (whether the New Zealand economy is innovative and growing; however desired levels of economic growth may not be sustainable or may have costs to society that are unacceptable). Indicators include economic performance (real growth domestic product per capita, real capital investment); trade (balance of trade in goods and services); financial position (current account balance, international (continued)

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Box 2.8 (continued) investment position, savings and debt); regulatory and business climate; industry and regional development (broad industry contributions to real gross domestic product); and science and technology (expenditure on research and development). – People’s skills and knowledge (whether New Zealanders are well educated and have the skills to protect the environment and use resources in a sustainable way). Indicators include current skills and knowledge (educational attainment, qualifications gained, proportion of science graduates, average weekly income); and learning (early childhood education participation rates, literacy improvements, school performance, participation in tertiary education). – Living standards and health (whether New Zealanders are healthy and have a decent standard of living; the standard of living should not erode capital—environmental, economic, human, and social capital). Indicators include income (per capita income, children in low income families, income distribution, unemployment); housing (affordability, crowded households); and health (life expectancy, cigarette smoking, mortality rates for children under five, suicide rates, injuries from motor vehicle accidents). – Consumption and resource use (balance between the economy and the environment). Indicators include household consumption (real household consumption expenditure); solid waste (waste disposal at landfills, % of packaging waste recycled by type); energy (total consumer energy by sector and fuel type); and transportation (total vehicle kilometers traveled). – Social cohesion (whether New Zealanders have a vibrant cultural identities). Indicators include social connectedness (participation in unpaid work outside the home) frequency of interaction with family and friends, household access to telecommunications); human rights (voter turnout at general elections, sex and ethnicity of elected representatives, complaints to the Human Rights Commission and Race Relations Office); culture and identity (fluency in the Maori language, % of Maori and Pacific Islands children receiving education in their own language); safety and security (criminal offense rates, child abuse and neglect). Source: Adapted from Jamieson (2004, pp. 98–100)

Box 2.9 San Diego Sustainability Indicators Project San Diego Sustainability Indicators Project involves three sets of indicators, namely economic, environment, and equity. (continued)

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Box 2.9 (continued) – Economic: Five subsets of indicators capture the economic wellbeing of the region. These are: (1) standard of living (measured in terms of real per capita income); (2) business investment (measured in terms of venture capital share of Gross Metropolitan Product (GMP) and Initial Public Offering (IPO) funds as a share of GMP; (3) capital facilities investment (measured in terms of capital outlays on air transport, capital outlays on sea and inland ports, and capital outlays on highways); (4) innovation (measured in terms of patents per million population); and (5) education (measured in terms of educational attainment for those 25 years of age and older). – Environment: Three subsets of indicators capture the environmental wellbeing of the region. These are: (1) air quality (measured in terms of number of days not meeting the Environmental Protection Agency (EPA) standards); (2) water quality (US EPA index of watershed indicators); and (3) capital facilities investment (measured in terms of capital outlays on sewerage, capital outlays on solid waste, and capital outlays on water utilities). – Equity: Five subsets of indicators capture the equity element. These are (1) income distribution (measured in terms of ratio of average to median household income); (2) housing affordability (measured in terms of housing opportunity index); (3) capital facilities investment (measured in terms of capital outlays on mass transit); (4) transportation (measured in terms of average commute time); and (5) education (measured in terms of % of preschoolers in early childhood education programs). Source: Adapted from Jarosz and Williams (2004, p. 185)

Other Theoretical Concepts To claim that the concepts personal utility, opulence, just society, human need satisfaction, and sustainability are the only philosophical concepts guiding community indicators projects is foolhardy. Of course, there are other concepts but they are “less popular.” These include capability, socio-economic security (or poverty alleviation), slow pace of life, cultural diversity, and innovation ecosystems. Capability is a theoretical concept introduced by Dr. ul Haq (a Pakistani working at the World Bank in the 1970s and later as minister of finance in Pakistan) and Dr. Amartya Sen, the Nobel Laureate. The concept became the foundation for the development of the United Nations Development Programme’s Human Development Index (HDI). Based on capability theory, quality of life is construed in terms of environmental conditions that allow people to become capable of helping themselves and enriching their own lives. In other words, if a country (or community) institutes policies and programs designed to help people exploit their capabilities to

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function, that country (or community) is viewed to have a high level of quality of life. For example, education is viewed as a capability to function because if people are educated, they use this education to help themselves achieve the desired level of quality of life. Thus, the objective of public policy should be the enhancement of the capability of people to undertake valuable and valued “doings and beings” (BurdSharps et al., 2011; Lewis & Burd-Sharps, 2010). Simply put, capabilities determine what people can do to achieve their potential (i.e., to function at their best). People who are rich in capabilities have the resources for making their vision of a “good life” a reality. Conversely, those with few capabilities have fewer options and fewer opportunities. In other words, our own capabilities are constrained by our own efforts, by our family’s circumstances, and by society’s institutions and conditions. Three major dimensions of capabilities are (1) a long and healthy life, (2) access to knowledge, and (3) a decent standard of living. These capabilities are captured through a health index, education index, and an income index, respectively. Specifically, Lewis and Burd-Sharps (2010) developed a dashboard of risk indicators associated with the three major dimensions of health, knowledge, and income captured at the community level. Risks indicators to a long and healthy life include percentage of newborn babies with low birth weight (less than 5.5 pounds), diabetes rates, and trauma-related death rate. Risks indicators to access to knowledge include percentage of 3- and 4-year-olds not enrolled in preschool, percentage of fourth graders not demonstrating reading proficiency, and students who do not graduate from high school of time. Risks indicators to a decent standard of living include children under six living in households with incomes below the poverty line, marginally attached workers, renters with severe housing-cost burdens, and elderly poverty. Poverty alleviation seems to capture the hearts and minds of many community leaders and local public officials. Many community planners are mandated by public policy in many countries to monitor the level of poverty in their locale and take steps to alleviate extreme poverty. There are many community indicators of poverty. Examples of criteria customarily used to guide the measurement of poverty in developing countries involve the following (Chambers, 1995; Glewwe & van der Gaag, 1990): • • • • • • • • • • • •

Disabled (i.e., blind, crippled, mentally impaired, chronically sick) Widowed Lacking land, livestock, farm equipment, a grinding mill Cannot decently bury their dead Cannot send their children to school Having more mouths-to-feed, fewer hands-to-help Lacking able-bodied members who can fend for their families in crisis Poor housing Having vices Lacking social support Having to put children in employment Single parents

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• Having to accept demeaning work or low-status work • Having food security for only a few months each year • Being dependent on common property resources See example of local poverty indicators used in the Philippines in Box 2.10. Box 2.10 Core Local Poverty Indicators Used in Monitoring Poverty in the Philippines Poverty indicators are broken down in terms of three sets of needs: (1) survival needs, (2) security needs, and (3) enabling needs. – Survival needs: Within this broad dimension, indicators of local poverty are further broken down into (1) health (e.g., proportion of children aged 0–5 years of age who died to the total of children aged 0–5 years old); (2) nutrition (e.g., malnutrition prevalence); and (3) access to basic amenities (e.g., proportion of households without access to safe water; proportion of households without access to sanitary toilet facilities). – Security needs: The security dimension is broken down into two subdimensions: (1) shelter (e.g., proportion of households who are squatting; proportion of households living in makeshift housing); and (2) peace and order (e.g., proportion of households with members victimized by crime). – Enabling needs: This dimension involves three major subdimensions with their own indicators: (1) income (e.g., proportion of households with income less than the poverty threshold; proportion of households with income less than the food threshold; proportion of households who eat less than 3 meals a day); (2) employment (e.g., unemployment rate); and (3) education (e.g., elementary participation rate; secondary participation rate). Source: Adapted from Reyes (2003, p. 2) Measuring poverty at the local level has been dominated by the basic needs concept of poverty. Poverty is thus viewed as the deprivation of material requirements for meeting basic human needs. Thus, community researchers attempt to measure community poverty by using indicators related access to material necessities such as food, shelter, schooling, health care, potable water, sanitation facilities, employment opportunities. With respect to the concept of slow pace of life. Mayer and Knox (2006) described the Slow City movement in the context of three case studies of Italian, German, and British towns. The slow city movement was initiated in 1999 when four Italian towns applied the Slow Food tenets in urban planning. Since then, many towns and cities have joined the movement. The slow pace of life concept is captured by a charter involving 54 criteria. The criteria focus on environmental protection, sustainable urban development, urban design and form, the support of local products, and educational awareness. Community leaders and urban planners embracing

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Table 2.6 Criteria of the slow city (slow pace of life) concept Slow city criteria • Environment: system for air quality testing and reporting; programs in support of new composting technologies, support for composting in individual households; support for alternative, renewable energy sources; local ban of genetically modified foods and organism in agriculture; protection of drinking water, sewage treatment, and use of rain water; program eliminating negative influences on urban design such as aesthetically displeasing advertisements; control systems and measures to limit electro smog; prevention of noise and measures to reduce noise; light pollution measurement and prevention; application of governing laws EMAS or ISO 9001, ISO 14000, and SA 8000; participation in the Agenda 21 project • Infrastructure: urban revitalization and historic preservation; programs to minimize traffic and support of a pedestrian environment and alternative mobility such as bike paths, public transportation, and traffic calming; access and availability of public spaces for the handicapped; familyfriendly city in the form of support of social services to all socioeconomic groups; provision of sufficient public green spaces within the city; regulations for delivery traffic; implementation of a schedule of opening and closing hours for the commercial interests of the town that is in keeping with the needs of the citizens; citizen-friendly opening hours for city offices; existence of a Slow City information office • Urban quality through the use of modern technology: promotion of eco-friendly architecture; use of recyclable containers in public structures; effective litter and waste management; use of sympathetically designed litter bins; use of containers for refuse and their removal according to established timetables; support and maintenance of region-specific plants in public and private spaces; programs to catalogue and protect trees and green spaces; development of a city-wide internet-based network for citizens (Webpage, e-government, etc.) and advertisement of these efforts • Encouragement of local produce and products: programs to support organic agriculture and certification of products; ban of genetically modified products; protection of and support for products and production techniques representing local tradition; implementation of concepts for use of local products in local eating eateries; educational programs about food, nutrition and taste in cooperation with Slow Food; Slow Food project for the preservation of unique local foodstuff and traditional production techniques in danger of extinction; implementation of an annual census of local products; creation of market opportunities for natural and local products; initiatives to encourage the protection of local products and handicrafts; implementation of programs that emphasize and conserve local cultural traditions and events • Hospitality: education and training of city staff about Slow City ideas and programs; system for verification that local government and local businesses are honest in their signage and that there is no false advertising; international signage and sustainable urban design concept for signage; existence of well-marked tourist routes with information and description; policies for hospitality such as existence of a policy for facilitating visitor access during events and celebrations; availability of guarded car parks in the areas near the city center; existence of brochures to the “slow” guide to the city; slow tours in the city; existence of a web-based homepage • Awareness: existence of programs to involve citizenry in the implementation of Slow City philosophy and criteria; extensive public relations efforts about Slow City • After slow city certification: presence of the Slow City logo on official documents of the city; web site dedicated to the Slow City programs in the city; regular assessment and evaluation of the city’s conformance with Slow City criteria and up-to-date reports; support of local Slow Food groups Source: Adapted from Mayer and Knox (2006, pp. 27–29); originally from Cittaslow (2006)

the slow pace of life concept pursue local projects that protect local traditions and cultures, contribute to a relaxed pace of life, create hospitality, and promote a unique sense of place and local distinctiveness. The guiding criteria are shown in Table 2.6.

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Another concept that has taken hold in cities and urban places where people from different races and cultures live together in social harmony is cultural diversity. The the Peel Region in Ontario, Canada is such a place and their indicators project highlights cultural diversity indicators as prominent in the overall indicators system (Mohanty, 2009). In addition to indicators capturing socio-economic status, health status, and the environment, this indicators project included cultural diversity in terms of: • • • •

Immigrants as percentage of total population; Visible minority population as a percentage of total population; People speaking non-official languages as percentage of total population; and Religion reported as percentage of total population.

Yet another concept is related to innovation ecosystems. This concept is used to assess innovation intensity in metropolitan regions. Inn ovation is a significant driver of economic growth and prosperity, especially in the suburbs of major cities. The focus here is on emerging industries related to the knowledge economy, namely IT, financial, property, and business services (Martinez-Fernandez & Potts, 2009). See example of innovation indicators as applied in the peripheral suburbs of Sydney, Australia (Box 2.11). Box 2.11 Innovation Indicators: The Case of Peripheral Suburbs of Sydney, Australia – Knowledge intensity: The drivers of knowledge intensity include industry knowledge intensity (captured by indicators such as % of employees in manufacturing; % of employees in knowledge intensive business services; % of employees in cultural and recreational industries; and % of employees in health), connectivity (captured by indicators such as number of business networks, number of industry clusters; and number of development networks), knowledge generation, transfer and integration (captured by indicators such as number of Master and PhD students in the region; proportion of science/engineering/IT based postgraduates; number of students from UWS cooperative programs working in industry; number of universityindustry partnerships), and entrepreneurship (captured by indicators such as the mean number of business startups 1999–2004). – Environment: The driver of this quality-of-life dimension involves environmental dynamics captured by indicators such as waste generation per capita; recycling per capita; commercial water use; residential water use; total water use; greenhouse gas emissions; pollution licenses and breaches; and local government expenditure in environmental management. – Social: The drivers of social wellbeing involve community engagement (captured by indicators such as number of council funded community organizations; and funding attached to these organizations) and livability (continued)

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Box 2.11 (continued) (captured by indicators such as ABS Socio-economic Index; mortgage stress; museums and libraries; recreational expenses per capita; community service expenses per capita; 10-year population growth; crime index; unemployment; and national parks in LGA). Source: Adapted from Martinez-Fernandez and Potts (2009, p. 197) It should be noted that community planners and community indicators researchers select a theoretical concept guiding the planning of an indicators project as a direct function of their perceptions of the community conditions. For example, in a distressed community, poverty, alcoholism, and drug use may lead the indicators ream to select a poverty alleviation concept, and indicators central to this concept would guide their efforts. In contrast, an affluent community may place greater value on the concept of opulence guiding the indicators project.

Summary In this chapter we described five different theoretical concepts guiding the planning of community indicators projects, namely personal utility, opulence, social justice, human need satisfaction, and sustainability. We showed how the concept of personal utility guides the development of a community indicators project. Similarly, we described how the concepts of opulence, social justice, and human need satisfaction guide the development of a community indicators project. With regards to personal utility, the basic premise is that a community rated high on quality-of-life dimensions is a community that has conditions and services that satisfy the needs of community residents. The concept of personal utility is grounded in the subjective experience of community residents. With respect to opulence, many community planners and indicator researchers believe that their basic mission is essentially economic development. This is because economic development is the foundation for social development. When a community achieves satisfactory levels of economic development, social development follows. Therefore, community indicator projects guided by the opulence concept collect data on economic indicators: household income, unemployment, type of jobs, quality of jobs, cost of living, poverty, homelessness, etc. In relation the social justice concept, many community planners and indicator researchers develop community indicators projects guided by the implicit notion that community quality of life is a community in which its residents enjoy a high level of social justice. Society is considered just if two distinct principles are met. The first principle of a just society holds when there is equality in the assignment of basic rights and duties. The second principle of the just society holds when inequalities are

Progress Check

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justified to benefit the least advantaged members of the society. Equality in basic rights and duties can be viewed in terms of at least six dimensions: (a) right to meet basic needs, (b) right to safety, (c) right to employment, (d) right to a healthful environment, (e) duty to pay taxes, and (f) duty to vote. With respect to inequalities being justified to benefit the least advantaged members of the society, community indicators are selected and developed in relation to children, women, minorities, the poor, and the disabled. The basic premise underlying the concept of human need satisfaction is the notion that a community characterized as high in quality of life is one that plays a significant role in satisfying residents’ developmental needs. Developmental needs refer to a hierarchy of lower and higher-order needs such as health, safety, and economic needs (lower-order needs) and social, esteem, actualization, knowledge, and aesthetics needs (higher-order needs). To achieve a high level of quality of life, community residents have to satisfy the full spectrum of their developmental needs—both lower- and higher-order needs. Community indicators capturing the full spectrum of need satisfaction would allow for the assessment and monitoring of progress towards that end. Programs and policies targeting lower-order needs can be viewed in terms of nine dimensions: (a) environmental pollution, (b) disease incidence, (c) crime, (d) housing, (e) unemployment, (f) poverty and homelessness, (g) cost of living, (h) community infrastructure, and (i) illiteracy and lack of job skills. In contrast, programs and policies targeting higher-order needs involve nine different dimensions: (a) work productivity and income, (b) consumption of non-basic goods and services, (c) leisure and recreational activities, (d) educational attainment, (e) community landscape, (f) population density and crowdedness, (g) arts and cultural activities, (h) intellectual activities, and (i) religious activities. Sustainable communities are those that enhance the economic, environmental, and social characteristics of a community so its residents can lead healthy, productive, enjoyable lives—higher quality of life. Sustainable development is a combination of human well-being and environmental well-being. That one cannot have a good human condition in a bad environment--indicators of human well-being in a community have to be balanced with indicators of the environmental well-being of the community. Other theoretical concepts guiding indicators projects that are less popular are taking hold. These include capability, poverty alleviation, slow pace of life, cultural diversity, and innovation ecosystem.

Progress Check 1. How does the concept of personal utility guide the development of community indicator projects? 2. How does the concept of opulence guide the development of indicator projects? 3. How does the concept of social justice guide the development of indicator projects?

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Theoretical Foundations

4. How does the concept of human need satisfaction guide the development of indicator projects? 5. How does the concept of sustainability guide the development of indicator projects?

Progress Check Answers 1. How does the concept of personal utility guide the development of community indicators projects? The basic premise of the concept of personal utility is that a community rated highly on quality-of-life dimensions is a community that has conditions and services that satisfy the needs of community residents. Community conditions are economic, social, and physical outcomes related to community action (e.g., quality of the environment in the community, rate of change to the natural landscape, housing/commercial development, loss of agricultural land, ridge-line development, race relations in the community, cost of living in the community, crime in the community, ties with people in the community, and neighborhood and housing conditions. Community services are organizations and institutions within the community that serve particular needs of community residents, and ultimately affect community outcomes or conditions. Community services (non-profit, business, and government organizations). The concept of personal utility is grounded in the subjective experience of community residents. Personal utility refers to the utility extracted by individual community residents as they perceive these community conditions and services. Therefore, community indicators systems guided by the theoretical notion of personal utility tend to take shape through subjective indicators involving community residents’ assessment of quality of community conditions and services. 2. How does the concept of opulence guide the development of indicator projects? The concept of opulence places much emphasis on the idea that economic development is most important. Economic development is viewed as the foundation for social development. That is, when a community achieves satisfactory levels of economic development, social development follows. Community indicator projects guided by this concept would highlight economic indicators such as household income, unemployment, type of jobs, quality of jobs, cost of living, poverty, and homelessness. 3. How does the concept of social justice guide the development of indicator projects? Social justice typically involves two principles: (a) equality in basic rights and duties, and (b) inequalities to benefit the least advantaged members of the community. Examples of equality in basic rights and duties include right to meet basic needs, right to safety, right to employment, right to a healthful environment, duty to pay taxes, and duty to vote. Community quality-of-life indicators are selected to reflect these characteristics of equality in basic rights and duties of community residents. With respect to the principle of inequalities justified to

References

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benefit the least advantaged members of the society, indicators are selected to reflect the well-being of children, women, minorities, the poor, and the disabled. 4. How does the concept of human need satisfaction guide the development of indicator projects? The concept of human need satisfaction guides the development of community indicators that reflect the full spectrum of residents’ developmental needs. Developmental needs refer to a hierarchy of lower and higher-order needs such as health, safety, and economic needs (lower-order needs) and social, esteem, actualization, knowledge, and aesthetics needs (higher-order needs). To achieve a high level of quality of life, community residents must satisfy the full spectrum of their developmental needs—both lower- and higher-order needs. Examples of programs and policies targeting lower-order needs include environmental pollution, disease incidence, crime, housing, unemployment, poverty/homelessness, cost of living, community infrastructure, and illiteracy and lack of job skills. In contrast, programs and policies targeting higher-order needs include work productivity and income, consumption of non-basic goods and services, leisure and recreational activities, educational attainment, community landscape, population density and crowdedness, arts and cultural activities, intellectual activities, and religious activities. 5. How does the concept of sustainability guide the development of indicator projects? Many community planners and indicator researchers buy into the notion that the concept of sustainability means equal treatment of people and the ecosystem. In other words, sustainable development is a combination of human well-being and environmental well-being. One cannot have a good human condition in a bad environment--indicators of human well-being in a community have to be balanced with indicators of the environmental well-being. Examples of indicators of human well-being include health and population (physical and mental health, disease, mortality, fertility, population change, etc.), wealth (income, poverty, inflation, employment, infrastructure, basic needs for food and shelter, etc.), knowledge and culture (education, communication, etc.), community (institutions, law, crime, racial and ethnic strife, etc.), and equity (distribution of benefits and burdens between social groups). Examples of indicators of eco-system well-being include land (diversity and quality of forests, farmland and other land ecosystems, including their modification, conversion, and degradation), water (diversity and quality of inland water and marine ecosystems, including their modification by dams and other structures, pollution and other water withdrawal), air (local and indoor air quality), and resource use (energy and materials, waste generation and disposal, recycling).

References Burd-Sharps, S., Guyer, P. N., & Lechterman, T. (2011). The American Human Development Index: Results from Mississippi and Louisiana. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 113–136). Springer. Chambers, R. (1995). Poverty and livelihoods: Whose reality counts? IDS Discussion Paper 347.

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Theoretical Foundations

Chanan, G. (2002). Community development foundation measure of community: A study for the active community unit and research. Development and Statistics Unit of the Home Office. Cittaslow. (2006). Cittaslow, Vereinigung der lebenswerten Stadte in Deutschland. Retrieved March 24, 2006 from http://www.slowcity-deutschland.de Cox, D., Frere, M., West, S., & Wiseman, J. (2010). Developing and using local community wellbeing indicators: Learning from the experience of community indicators Victoria. Australian Journal of Social Issues, 45(1), 71–88. Cradoso, R., Sobhani, A., & Meijers, E. (2021). The cities we need: Towards an urbanism guided by human needs satisfaction. Urban Studies. https://doi.org/10.1177/00420980211045571 Findeisen, N. (2006). Using community indicators to improve the quality of life for children: The Sacramento County Children’s Report Card. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 203–228). Kluwer Academic. Glewwe, P., & van der Gaag, J. (1990). Identifying the poor in developing countries: Do different definitions matter? World Development, 18(6), 803–814. Hardi, P., & Zdan, T. (1997). Assessing sustainable development: Principles in practice. International Institute for Sustainable Development. Haworth, J., & Hart, G. (2007). Well-being: Individual, community and social perspectives. Palgrave Macmillan. Holden, M. (2006). Sustainable Seattle: The case of the proptotype sustainability indicators project. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 177–202). Kluwer Academic. Jamieson, K. (2004). A collaborative approach to developing and using quality of life indicators in New Zealand’s largest cities. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community qualityof-life indicators: Best cases (pp. 75–109). Kluwer Academic. Jarosz, B., & Williams, M. D. (2004). Creating an index to evaluate a region’s competitiveness. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases (pp. 183–207). Kluwer Academic. Lewis, K., & Burd-Sharps, S. (2010). The measure of America 2010–2011: Mapping risks and resilience. A Joint Publication of the Social Science Research Council and New York University Press. Martinez-Fernandez, C., & Potts, T. (2009). Quality of life through innovation indicators: The case of peripheral suburbs of Sydney. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 191–207). Springer. Max-Neef, M. (1989). Human scale development: Conception, application and further reflections. Apex. Mayer, H., & Knox, P. (2006). Pace of life and quality of life: The Slow City charter. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases III (pp. 21–39). Springer. Mohanty, S. (2009). Quality of life and cultural diversity in Peel Region (Ontarion, Canada). In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases III (pp. 123–154). Springer. Moreno Minguez, A. (2009). Gender equality and quality of life: Examples of best practices from nine European cities: The EQUALABEL Project. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 163–189). Springer. Phillips, R., & Wong, C. (2017). Introduction. In R. Phillips & C. Wong (Eds.), Handbook of community well-being research (pp. xxiv–xxxviii). Springer. Prescott-Allen, R. (2001). The wellbeing of nations. Island Press. Rawls, J. (1971). A theory of justice. Belknap Press of Harvard University Press. Rawls, J. (1975). Fairness to goodness. Philosophical Review, 84, 530–543. Reyes, C. (2003). Diagnosing poverty at the local level. Micro Impacts of Macroeconomic Adjustment Policies, 10(3), 1–3.

References

43

Richards, R., & Kamman, E. (2006). Living in a post-apartheid city: A baseline survey of quality of life in Buffalo City. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 229–248). Kluwer Academic. Schuster, L., & Ciurczak, P. (2018). A report from Boston Indicators: Boston’s booming . . . but for whom? Building shared prosperity in a time of growth. The Boston Foundation. https:// www.bostonindicators.org/-/media/indicators/boston-indicators-reports/report-files/bostonsbooming-2018.pdf?la¼en&hash¼94DE67E74983CB7DF3EBCB4EFA80F02346719C8B Sirgy, M. J. (2011). Theoretical perspectives guiding QOL indicator projects. Social Indicator Research, 103, 1–22. Sirgy, M. J., & Cornwell, T. L. (2001). Further validation of the Sirgy et al.’s measure of community quality of life. Social Indicators Research, 56, 125–143. Sirgy, M. J., & Rahtz, D. (2006). A measure and method to assess community quality-of-life. In M. J. Sirgy & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases II (pp. 61–74). Springer. Sirgy, M. J., Rahtz, D., Cicic, M., & Underwood, R. (2000). A method for assessing residents’ satisfaction with community-based services: A quality-of-life perspective. Social Indicators Research, 49, 279–316. Sung, H.-K., & Phillips, R. G. (2018). Indicators and community well-being: Exploring a relational framework. International Journal of Community Well-Being, 1(1), 63–79. United Nations Commission on Sustainable Development. (2001). Indicators of sustainable development: Guidelines and methodologies. United Nations Division for Sustainable Development. Warner, J. B. (2009). Jacksonville’s race relations progress report: Creating change through community indicators. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-oflife indicators: Best cases IV (pp. 141–161). Springer. Widgery, R. (2004). A three-decade comparison of residents’ opinions on and beliefs about life in Genesee County, Michigan. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-oflife indicators: Best cases (pp. 153–182). Kluwer Academic.

Chapter 3

An Example

Learning Objectives In this chapter you should be able to answer the following questions: 1. 2. 3. 4. 5. 6. 7.

What motivates a community indicators project by example? How to form an indicators committee? How to develop an initial set of indicators by example? How to refine the set of indicators by example? How to collect and report data based on refined set of indicators by example? How to validate the objective indicators with subjective ones by example? How to translate the system indicators into program and policy indicators by example? 8. How to disseminate the indicators’ findings by example?

Introduction The example we are using in this chapter to illustrate how community indicator projects are developed is based on the Vital Signs Project of the New Century Council (a non-profit organization designed to develop and maintain QUALITYOF-LIFE indicators for 12 counties in western Virginia, USA).1 This chapter provides an overview of the process for choosing initial indicators, the project’s research design, and how the data were analyzed. We also describe how objective data (environmental, social, and economic statistics) were gathered from reliable sources and subjective data (individuals’ perceptions of their quality of life) gathered from a mail survey to 3200 households throughout the region. Both objective (secondary data) and subjective (primary data) community indicators provided

1

This chapter is adapted from Cornwell (2003).

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_3

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46

3 An Example

policymakers in western Virginia information related to community programs and policies to enhance the quality of life of the region.

Initiating an Indicators Project A community indicators projects is typically motivated by a set of adverse conditions that bring community leaders together to address the problem and find solutions. In the context of the Vital Signs Project, the Roanoke regions and its surrounding areas experienced high job losses in the early 1990s when more than 8000 jobs were lost within an 80-mile radius of Roanoke. Roanoke is the largest city in western Virginia. In response to this critical situation, business and community leaders from the New River Valley, Allegheny Highlands, and Roanoke Valley formed an organization to design a strategic plan detailing a brighter economic future. Known as the New Century Region, this area encompasses approximately 500,000 people and originally included 12 counties and five independent cities (an additional county was added in 1998). Led by the New Century Council, an organization of community leaders, more than 1000 volunteers throughout the region participated in a visioning process that identified goals and strategies designed to achieve a desirable, sustainable future. Of primary importance to the process was the agreement that the path to this future involve complex interconnections between various aspects of society—economic, environmental, and social–-and that these aspects must be monitored (see Table 3.1). Concurrently with this process, New Century leaders succeeded in gaining the attention of Virginia’s legislature. In 1997, the Virginia General Assembly passed H. J. Res. No. 467 recognizing the New Century Region of Virginia. The resolution noted that the economic development organizations of the region had collaborated to create a “new platform from which to sell the region nationally to firms that can invest, create jobs, and take advantage of the region’s institutions of higher education.” The resolution also praised the regional collaboration and requested that the Virginia Department of Transportation post and maintain signs reading “Entering Virginia’s Technology Corridor” on various interstate highways entering the region. To ensure that the area would indeed become Virginia’s Technology Corridor, the New Century Council’s various visioning committees designated strategies and recommended specific actions. One of the major recommendations from the New Century Council’s Quality of Life Committee was to establish various community indicators, collect data pertaining to each indicator, compare the data with other areas around the state and nation, and determine where the New Century Region has strengths and weaknesses. This profile could then be used to create programs designed to correct deficiencies and promote strengths in support of a high quality of life for citizens of the region. This was the beginning of the Community Indicators Project of the New Century Region (Virginia’s Technology Corridor), which was later named Vital Signs.

Forming an Indicators Project Committee

47

Table 3.1 Sample data for Roanoke County Population Community Births to teenage girls (age 15–17— per 1000) Rate of child abuse (per 1000) Economy Unemployment rate Manufacturing Tourism Health Infant mortality rate (deaths per 1000 live births) Percent women seeking prenatal care in the first trimester Environment Solid waste disposal (pounds per person/ year) Education Percent of students in grades 9–12 who dropped out of school Financial accessibility to higher ed. (cost as % of Median Household Inc.)

1991 79,900

1992 80,700

1993 81,600

1994 82,700

1995 81,800

1996 81,800

1997 81,600

1998 –

7

10

10

8

7

10

–

–

1.9

2.3

2.3

2.3

2.9

2

–

–

4.1% – –

4.2% 17.2% 3.6%

3.9% – –

2.8% 15.2% 4.2%

2.2% – –

2.2% 13.9% 3.7%

2.4% – –

– – –

4.2

10.2

5.1

4.6

4.8

3.4

–

–

93.9%

91.7%

92.8%

93.3%

91.9%

94.6%

–

–

–

–

2009

1906

1892

1872

1602

–

3%

2%

2%

3%

2%

1%

2%

–

7.29%

7.63%

7.93%

8.16%

8.18%

8.04%

–

–

Forming an Indicators Project Committee It is important to have representatives of significant stakeholders in the indicators project committee. The “voice” of these various stakeholders must be heard and fully represented in that committee. This is what happened regarding the Vital Signs Project Committee. That committee was made up of representatives from a network of public and private institutions. Examples of stakeholders represented in the Vital Signs Project Committee include the U.S. Environmental Protection Agency, the Virginia Environmental Endowment, the Norfolk Southern Corporation, the Robins Foundation and The Cabell Brand Center, foundations interested in sustainability issues, Carilion Community Health Fund, several colleges, and a number of local governments. The

48

3 An Example

committee was charged with the process of selecting indicators, compiling and analyzing data, and publishing a report.

Developing an Initial Set of Indicators Typically, the selection of the initial set of indicators is guided by two factors: (a) the quality of life model and the resulting mission statement of the indicators project committee, and (b) the leading quality-of-life researcher on the same committee. The key factor that guided the development of an initial set of indicators in the Vital Signs Project was the quality-of-life model. The Vital Signs Projects was based on the theoretical notion of sustainability. Specifically, the project was based on the 3 E’s of sustainability: Economy, Environment, and Equity. The theoretical concept of sustainability was reflected in Vital Signs’ mission statement. The overall vision of the New Century Council was embedded in the final phrase of their document: “. . . decision-making based on the principle of [sustainable} development.” Based on that vision, the Vital Signs Project Committee stated its mission as follows: to empower citizens of the New Century Region to understand and develop sustainable communities, which produce strong, healthy families; protect the environment; provide quality education and cultural access; and ensure social justice in a stable and expanding economy.

A second factor that played an important role in the selection of an initial set of indicators in the context of the Vital Signs project was the expertise on steering committee. Customarily university professors or other research consultants who have some expertise in community indicators work are recruited on the committee. This is important because these people do provide an important element of leadership to the committee. They also play an important role in selecting an initial set of indicators that serves as a foundation for the project. Once this foundation is established, the committee invites the representatives of various stakeholders (including the general public) to contribute to the indicator selection process. With respect to the Vital Signs Project Committee, Ferrum College Environmental Science Professor John Leffler provided the initial list of indicators to the committee. Based on this list of indicators the committee began its work by hosting several public meetings to identify possible indicators. Additionally, lists of possible data sets were published in the regional newspapers and citizens were asked to write or call to provide their input about possible indicators (see Table 3.2). Further, community leaders were interviewed to discuss which indicators would be most useful to policymakers in all sectors of the region.

Refining the Initial Set of Indicators and Collecting Data

49

Table 3.2 Indicator selection questionnaire The following was published in the Roanoke Times, Roanoke, Virginia, prior to public meetings to discuss selection of indicators Listed below are possible indicators that have been proposed for inclusion in The New Century Region’s Community Indicators Reports. We are seeking your advice as to which of these, or what other indicators would be most useful to include. All indicators will be reported separately for each of the 16 political jurisdictions as well as for the entire New Century Region. Please consider the goals and strategies identified in other community goals that you may be working toward in other capacities In the space to the left of each indicator, please score that indicator from 1 to 5 based on the following scale 5 ¼ Very important indicator for our community; essential it be included 4 ¼ Important indicator to some people; probably should be included 3 ¼ This indicator probably has some value, but it is not critical 2 ¼ Difficult to see value in this indicator 1 ¼ This indicator has no value; it does not measure anything meaningful If you do not wish to comment on an indicator, feel free to skip over it to the next one. Space is provided at the end of this questionnaire for other indicator suggestions that you feel should be included, but that are not on this list. Also, we appreciate your comments on any of the indicator listed, as well as any other thoughts you may have about this program. Thank you for your assistance with this important program. [List that follows is a sampling of the 67 indicators printed.] _____1. Citizen Participation in Government—voting rate: total number of voters in previous 5 elections (4 years)/total number of citizens over 21 years old _____2. Citizen Participation in Government—percent of uncontested offices in local elections over previous 4 years _____3. Community Prejudice—annual number of “hate” crimes per 100,000 residents _____4. Community Philanthropy—annual contributions to the United Way per $10,000 EBI (effective buying index) _____5. Drugs and Alcohol—annual number of traffic accidents attributable to Driving Under the Influence (DUI) or Driving Under the Influence of Drugs (DUID) per 100,000 residents _____6. Personal Safety—annual number of reported violent person-to-person crimes per 100,000 residents _____7. Property Crime—annual number of reported property crimes per 100,000 residents _____8. Young Adult Immigration/Emigration—number of 25–29-year-old individuals in year “X”/number of 15–19-year-old individuals in year “X-10”; perhaps expressed as percent of gain or loss of young people in a locality _____9. Community Wealth—the Virginia Composite Index; calculated by the Virginia Department of Education; measure the ability of a locality to pay for its school system and other infrastructure ____10. Community Wealth—fiscal Stress Index; measures revenue capacity per capita that local governments can tap to finance their programs

Refining the Initial Set of Indicators and Collecting Data Customarily, indicators project committees refine the initial set of indicators by focusing on those indicators in which secondary data are available from the various government agencies in the region. A case in point is Births to Teenage Girls–Births

50

3 An Example

to girls aged 15–17. What government or non-government agency collects statistics related to this indicator? Does this agency have statistics related to the targeted communities? Does the agency have trend data (i.e., data over time)? The Vital Signs Project Committee re-examined the resultant set of indicators to determine if comparable data could be obtained for each county. Based on this examination, some indicators were dropped, and others were added. Let’s further illustrate by focusing on one example indicators, namely Births to Teenage Girls– Births to girls aged 15–17. The Virginia Department of Health collects and maintains data on this indicator for every city and county in the State of Virginia. As such, this indicator was selected for inclusion in the final set. The final list of indicators was then grouped into six categories: (a) population, (b) community, (c) economy, (c) education, (d) environment, and (e) health (see Table 3.3).

Validating Objective Indicators with Subjective Ones Although this is not typical, many community indicator projects validate the data from the objective indicators with subjective indicators. The goal is to ensure that the objective reality is not divorced from the subjective experience of community residents. Another goal of conducting a community survey is to collect information about residents’ perception and evaluation of community conditions and services. Commonly, surveys are conducted by sampling community residents who are contacted (by mail, telephone, Internet, door-to-door interviews, etc.) and asked questions about their experience with specific aspects of the community—conditions such as crime and traffic congestion, and services such as retailing, banking, telecommunications, transportation, electric power, etc. With respect to the Vital Signs Project, a survey was conducted to capture residents’ perception of various aspects of their community characterizing the quality of life in their local area—an assessment of how people actually felt about living in the New Century Region. The design of the survey questionnaire (subjective indicators) was guided by the personal utility notion described in Chap. 1. The survey was also designed to complement the objective data. A mail survey was conducted during fall, 1999—a survey questionnaire was mailed to a sample of 3200 households randomly selected from the counties comprising the New Century Region. After 3 weeks, a second mailing to non-respondents was conducted. This process resulted in 380 completed questionnaires or a response rate of 13%. The survey captured satisfaction both globally (overall domains in life, e.g., work, family, etc.) and in very specific areas. It asked individuals to rate their satisfaction on a 7-point scale: “terrible,” “unhappy,” “mostly dissatisfied,” “mixed feelings,” “mostly satisfied,” “pleased,” and “delighted.”

Validating Objective Indicators with Subjective Ones

51

Table 3.3 Vital Signs 1998 indicators Population • Number of residents • Age structure Community • Residents in poverty • Children in poverty • Out-of-wedlock births • Births to teenage girls • Children with divorced parents • Child abuse • Elder abuse • DUI arrests • Narcotics arrests • Alcohol and drugs in schools • Person-to-person crime • Property crime • Juvenile crime • Weapons in schools • Acres of recreational land • Library circulation • Voter participation Economy • Employment diversity • Unemployment • Per capita income • Percent of jobs paying a livable wage • Disposable income • Income disparity • Index of community wealth • Adults with H.S. diploma • H.S. graduates in continuing education • Students in 2-year colleges • Employment in manufacturing • Employment in tourism • Airport usage Education • Standardized test scores • Students enrolled in public schools • Expenditures per student • Funds as a percent household income • Drop-out rate • Financial accessibility to higher ed. • Community college enrollment (continued)

52

3

An Example

Table 3.3 (continued) Environment • Violations of EPA water standards • Turbidity of rivers • Air quality • Particulate matter in atmosphere • Toxic chemical release rate • Solid waste disposal per person • Percent of land in agriculture • Percent of land in forests • Percent of protected land • Ownership of protected land Health • Infant mortality rate • Students passing phys. Fitness tests • Financial accessibility to healthcare • Prenatal care • Tobacco usage • Hospital admissions for diabetes • Coronary heart disease • Accidental injury deaths • Suicide deaths

The results of the survey indicated that people in the region were “mostly satisfied” to “delighted” with their family life, overall financial situation, health, education, friends and associates, leisure life, cultural life, social status, spiritual life, home life, and community life. Perhaps the best testament to the high quality of life in the area is the result of the question regarding satisfaction with “your life as a whole”: 93% were “mostly satisfied,” “pleased,” or “delighted.” The one area of “lesser satisfaction” relates to employment. Over half of those currently working had “mixed feelings,” were “mostly dissatisfied” or felt “unhappy” or “terrible” about education/training at the job, and one-third of those currently working felt the same way about health benefits, retirement benefits, and their “future on the job.” Responses to questions regarding community quality of life indicated that, in general, people were satisfied (and many even pleased or delighted) with most aspects of their neighborhoods: cost of living, crime rate, ties with people in the community, home value, housing situation, sense of privacy at home, etc. Two areas, however, where mixed feelings appeared were “race relations” and “rate of change to the natural landscape,” with the latter causing more than one-third of the respondents to indicate they were “unhappy,” “mostly dissatisfied,” or “terrible.” Finally, the survey looked at overall satisfaction with business, government, and nonprofit services in the community. Most of the comments received from all three areas were “mostly satisfied,” “pleased,” or “delighted” with 88%, 84%, and 83% for

Validating Objective Indicators with Subjective Ones Table 3.4 Satisfaction with business services

Services Banking Insurance Realtors Investment Legal Department stores Supermarkets Specialty stores Entertainment Restaurants Day care Sports T.V. Radio Newspapers Automobile services Telephone Electrical services Schools Health care

53 Roanoke County 4.83 4.78 4.09 4.24 4.15 4.72 4.85 4.32 2.53 4.78 2.12 3.86 4.90 4.67 4.37 4.55 4.67 5.13 2.45 4.26

Wythe County 4.72 5.22 4.35 3.88 3.71 3.44 4.67 3.83 2.67 4.61 2.71 4.06 3.50 4.28 4.28 5.28 5.17 5.33 2.83 3.50

business, government, and nonprofit respectively. Furthermore, the survey examined satisfaction with specific services provided by business, government, and nonprofit organizations. Tables 3.4, 3.5, and 3.6 show the results for two of the counties in the survey: Roanoke County (mostly suburban) and Wythe County (mostly rural). The data indicated that satisfaction with traditional business services (e.g., banking, department stores, supermarkets) is lower in Wythe, the rural county, while satisfaction with some services, like day care, may be low, but not as low as the suburban county, where the need for day care is greater, because of the larger number families where both parents work outside the home. As for government services, many rated government services in Wythe County (e.g., water, lights, and transportation) lower than those in Roanoke County. Finally, in the area of nonprofit services, several services were found to be rated much lower in satisfaction in Wythe County: cultural offerings, adult education, and health services. The results of this satisfaction survey were then paired with the objective data to formulate recommendations for policymakers. For example, both objective and subjective data indicate most people living in the region enjoy a high quality of life; however, the objective data and the demographics of the individuals who completed the subjective survey (mostly white, homeowners, and employed at one job) indicate that more programs that help individuals living at the lower end of the income scale (or individuals falling into crisis situations) are needed. Also, the survey revealed that residents are worried about the rate of change of the natural environment. The objective data were consistent with this observation.

54 Table 3.5 Satisfaction with government services

3 Services Alcohol Crisis Adoption Family planning Religious Victim support Mental Senior Food Handicapped Hospital Youth Volunteer Culture Colleges Commerce Adult education Housing Family counseling Childcare Job training Health

Roanoke County 2.72 2.83 2.50 2.69 5.38 3.22 3.64 4.03 3.59 3.45 4.82 3.43 4.08 4.48 5.08 4.05 4.41 3.26 2.98 2.84 2.60 3.40

An Example

Wythe County 2.47 2.82 2.94 2.82 5.22 2.50 3.39 3.71 4.00 3.78 4.17 3.22 3.50 3.11 5.28 4.11 3.56 3.22 2.61 2.61 2.72 2.89

Translating System Indicators into Program and Policy Indicators Traditionally, indicators projects make every attempt possible to translate their research findings in ways that can be used by decision-makers. This is done by developing summary report cards by comparing the community with other communities, the state, the nation, or other geographic entities. Some committees generate charts showing trends and making specific recommendations—a call for action. Further, some committees attempt to translate their indicators into specific actionable policies and programs. Let see how the Vital Signs Project did this. The Vital Signs report provided three overall recommendations to help institutions begin to plan a sustainable future: (a) keep, refine, and use community indicators; (b) all sectors must participate in “education for sustainability”; and (c) use technology for sustainability. These recommendations were approved by the Vital Signs Steering Committee after reviewing the objective and subjective data and working with the region’s major businesses, nonprofit institutions, and governments in policy discussions. Most of these organizations and institutions were just beginning to think about “sustainability,” and Vital Signs recognized the critical need for more education in the concept of sustainability and the use of community indicators. In addition, the

Translating System Indicators into Program and Policy Indicators Table 3.6 Satisfaction with nonprofit services

Services Alcohol Crisis Adoption Family planning Religious Victim support Mental Senior Food Handicapped Hospital Youth Volunteer Culture Colleges Commerce Adult education Housing Family counseling Childcare Job training Health

Roanoke County 2.72 2.83 2.50 2.69 5.38 3.22 3.64 4.03 3.59 3.45 4.82 3.43 4.08 4.48 5.08 4.05 4.41 3.26 2.98 2.84 2.60 3.40

55 Wythe County 2.47 2.82 2.94 2.82 5.22 2.50 3.39 3.71 4.00 3.78 4.17 3.22 3.50 3.11 5.28 4.11 3.56 3.22 2.61 2.61 2.72 2.89

report listed specific recommendations for all levels of government (incorporate sustainability in policymaking, begin to “think regionally,” invest more authority at the state level in regional planning, emphasize “social indicators” as much as “economic indicators” at the federal level); business and the media (begin to lead a “sustainability revolution” and help educate the public about sustainability through use of social indicators); nonprofit institutions (use the indicators to create new programs and track progress); and individuals (make sustainability part of lifelong learning and increase giving/volunteering to charitable organizations). The final phase of the project would involve publishing a new data report and helping the region to respond to the first 3 years of indicator research. Specifically, Vital Signs spent time and effort to help individuals and organizations translate its findings into action. The concept of “sustainable indicators” was described by highlighting three major components of the indicators project—economy, environment, and society—and their linkages. Environmental concerns cannot be isolated from the economy. Likewise, economic matters have ramifications in the social realm. That is, the economy exists entirely within society, but society is more than just the economy. Human relationships, the arts, religion, etc. are all part of society. Finally, society itself exists within the natural environment. The findings were summarized in a regional report card, which compared data for the region with similar data for Virginia and the entire U.S. See Table 3.7 for

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Table 3.7 Regional report card Better than Virginia Indicator Environment Days over 8 h ozone standard Rec. acres per X 1000 population Community Building Children living in poverty Domestic abuse Person-to-Person X Crime Economy Unemployment rate Employment growth Poverty rate

Same as Virginia

We need to improve

X

Better than U.S.

Same as U.S.

We need to improve

*

*

*

X

X X

X

X * *

* *

* *

*

*

X X X

*

X

Notes Selected indicators from Vital Signs 2000 “*” indicates data not analyzed for report Environment: No state or national water indicators were available, so air quality indicators and the amount of recreational land per 1000 residents were used as representative environmental indicators. The U.S. Environmental Protection Agency created specific air quality standards regarding ozone. The “8-Hour 4th Daily Maximum” of ozone was tracked from 1990 to 2000. These figures refer to the 4th highest daily 8-h average ozone measurement for each year. The number of recreational acres in the region compared very favorably with the historically accepted standard of 10 acres per 1000 residents Community Building: The indicator of children living in poverty was based on the poverty level standards used by the U.S. Census Bureau. The domestic abuse figures were developed from data regarding the number of women served in domestic abuse programs from the Virginia Department of Social Services. Person-to-person crime figures were based on the Uniform Crime Report of the Virginia Department of State Police Economy: The unemployment rate was based on information from the Virginia Employment Commission; employment growth was based on a study of total employment growth compared to growth in high tech industries by a local economic analysis firm; and the poverty rate was based on data from the U.S. Census Bureau

examples of indicators used on the report card. This kind of a report card is difficult to construct using all indicators, because comparable state and national data often are not available. Vital Signs chose selected indicators for which comparable data was available. In addition to the Regional Report Card, a chart on Sustainability Trends (see Table 3.8) was also developed. The goal of this trends chart is to help institutions and organizations across the region learn to make decisions based on the principles of

Translating System Indicators into Program and Policy Indicators

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Table 3.8 Sustainability trends Population Environment: water quality Environment: air quality Environment: land use Economy: employment Economy: income concentration Community building: social support Community building: safety Community building: education Community building: health Community building: cultural participation Community building: political participation Community building: understanding sustainability

Moving away from sustainability Static (needs to move toward sustainability) Static (needs to move toward sustainability) Slowly moving away from sustainability Static (needs to move toward sustainability) Static (needs to move toward sustainability) Moving toward sustainability Moving toward sustainability Moving toward sustainability Static: needs to move toward sustainability) Moving toward sustainability Static: needs to move toward sustainability Static: needs to move toward sustainability

Notes: The noted trends are based only on the objective indicators in the 2000 Vital Signs report

sustainability. The indicators used as a background for this chart are shown in Table 3.3. The noted sustainability trends were then translated into “program” and “policy” indicators. Here the emphasis was placed on connecting the indicators to programmatic and budgetary decisions by institutions. In essence, two types of indicators are required for this process to work effectively: system indicators, which provide feedback about the overall health of the region; and program and policy indicators, which provide policymakers with feedback about the success of specific programs and policies. With respect to system indicators, new indicators were added, ones that were not useful were deleted, and others were modified to more clearly show linkages among the environmental, economic, and social aspects of society. As presented in Vital Signs 2000, these indicators were designed to help the region see how the overall “quality-of-life system” is working and anticipate potential system breakdowns or changes in direction. Program or policy indicators reflect activities of subsystems and allow governments and other institutions to assess whether they should adjust their programs or policies when system indicators show movement in an undesirable direction. Examples would be compliance rates for permitted air emissions, decreases in teenage pregnancy rates after institution of a city-wide prevention program, or increases in reading scores by students in a specific district’s reading education program. Following the explanation of system, program (and policy) indicators Vital Signs 2000 presented its list of system indicators arranged according to the categories of environment, economy, and community building. These were followed by suggestions for program and action indicators to be used by businesses, governments, organizations, and individuals. Also included were discussions of the importance of the indicators, linkages with other aspects of society, how the region is performing,

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where differences might be occurring within the region, and suggestions for future indicators more clearly aligned with the concept of sustainability.

Disseminating the Indicators’ Findings It is important to promote the findings as widely as possible. Indicators project committees do this by holding press conferences, press releases, posting the report on their websites and links of their websites with other websites that are used by the targeted stakeholders, printing the full and summary reports and distributing those reports to community leaders, and making paper copy summary reports available to community residents through Chambers of Commerce, the municipal buildings, and other government and non-profit agencies. With respect to the Vital Signs Project, the report was widely distributed through a news conference. Community leaders received their printed copies by mail and other face-to-face encounters. The following year, a formal office for the Vital Signs project was established at Hollins University, additional efforts were made to disseminate the report as widely as possible and to obtain feedback. The project was also featured in local media, and copies of the report were placed in all county and city libraries of the region. Presentation of the report was made to the various county boards of supervisors and city councils throughout the region.

Summary The Vital Signs Project provides a good example of an indicators project. Vital Signs employed a comprehensive, inclusive process in the selection of quality-of-life indicators, data collection and analysis, and publication of reports that have been used in strategic planning by a variety of organizations. The project was grounded in the concept of sustainability, and its reports helped educate the public about this complex issue. It also was one of the few indicators projects nationwide to use both objective and subjective data to examine quality-of-life. This indicators project began with the formation of an indicators project committee. That committee was made up of representatives from a network of public and private institutions. The development of an initial set of indicators was guided by two factors: vision statement and personal expertise. The overall vision of the New Century Council was embedded in the final phrase of their document: “decisionmaking based on the principle of sustainable development.” Thus, the process of indicator selection was guided by a quality-of-life theoretical model, namely the sustainability concept. Ferrum College Environmental Science Professor John Leffler provided the initial list of indicators to the committee. Again, that list was guided by the sustainability model of QOL.

Summary

59

The Vital Signs Project Committee re-examined the resultant set of indicators to determine if comparable data could be obtained for each county. Based on this examination, some indicators were dropped, and others were added. The final list of indicators was then grouped into six categories: (a) population, (b) community, (c) economy, (d) education, (e) environment, and (f) health. Then a survey was conducted to capture residents’ perception of various aspects of their community characterizing the quality of life in their local area—an assessment of how people actually felt about living in the New Century Region. The subjective data were designed to validate and complement the objective data. The survey was guided by the personal utility concept of quality of life. Responses to questions regarding community quality of life indicated that, in general, people were satisfied (and many even pleased or delighted) with most aspects of their neighborhoods: cost of living, crime rate, ties with people in the community, home value, housing situation, sense of privacy at home, etc. Two areas, however, where mixed feelings appeared were “race relations” and “rate of change to the natural landscape,” with the latter causing more than one-third of the respondents to indicate they were “unhappy,” “mostly dissatisfied,” or “terrible.” Finally, the survey looked at overall satisfaction with business, government, and nonprofit services in the community. Most of the comments received from all three areas were “mostly satisfied,” “pleased,” or “delighted” with 88%, 84%, and 83% for business, government, and nonprofit respectively. The results of this satisfaction survey were then paired with the objective data to formulate recommendations for policymakers. The Vital Signs report provided three overall recommendations to help institutions begin to plan a sustainable future: (a) keep, refine, and use community indicators; (b) all sectors must participate in “education for sustainability”; and (c) use technology for sustainability. The final phase of the project involved publishing a new data report and helping the region to respond to the first 3 years of indicator research. The findings were summarized in a regional report card, which compared data for the region with similar data for Virginia and the entire U.S. The report was widely distributed through a news conference. Community leaders received their printed copies by mail and other face-to-face encounters. The following year, a formal office for the Vital Signs project was established at Hollins University, additional efforts were made to disseminate the report as widely as possible and to obtain feedback. The project was also featured in local media, and copies of the report were placed in all county and city libraries of the region. Presentation of the report was made to the various county boards of supervisors and city councils throughout the region. Because Vital Signs is a complex, regional project, fund-raising, as well as steady institutional support, will continue to be challenging. Its past reports are available on the Internet at http://www.newcentury.org.

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Progress Check 1. 2. 3. 4. 5. 6. 7. 8.

What motivates a community indicators project? How to form a community indicators project committee? How to develop an initial set of indicators? How to refine the set of indicators? How to collect and report data based on refined set of indicators? How to validate the objective indicators with subjective ones? How to translate the system indicators into program and policy indicators? How to disseminate the indicators’ findings?

Progress Check Answers 1. What motivates a community indicators project? A community indicators project is typically motivated by a set of adverse conditions that bring community leaders together to address the problem and find solutions. Examples of adverse conditions that bring community leaders together to initiate indicators projects include significant job losses in the area, an epidemic wide spread of disease, significant increase in the crime rate, significant increase in out-migration, significant decrease in household income, among others. 2. How to form a community indicators project committee? It is important to have representatives of significant stakeholders in the community indicators project committee. The “voice” of these various stakeholders has to be heard and fully represented in that committee. Examples of important stakeholders typically would include government agencies representing different domains (departments of health, education, law enforcement, transportation, commerce, environment, etc.), local chambers of commerce, major local banks, healthcare organizations, colleges and universities, utility companies, charitable organizations, and town/ city council, community planners, economic development organizations, transportation centers (e.g., airports, railway and shipping companies), and religious organizations. 3. How to develop an initial set of indicators? Typically, an initial set of indicators is developed guided by the mission statement of the committee and expertise on hand. Remember that the various theoretical concepts that guide the development of indicators projects discussed in Chap. 2 (concepts of personal utility, opulence, social justice, human need satisfaction, and sustainability). The mission statement developed guiding the committee tends to have elements of one or more of these theoretical notions. These are used by the committee to guide the selection of an initial set of indicators. Further, university professors or other research consultants who have some expertise in community indicators work are recruited on the committee. This is important because these people do provide an important element of leadership to the committee. They also play an important role in selecting an initial set of indicators that serves as a foundation for the project.

Progress Check Answers

4.

5.

6.

7.

8.

61

Once this foundation is established, the committee invites the representatives of various stakeholders (including the general public) to contribute to the indicator selection process. Furthermore, community citizens’ views of what indicators are best are solicited through the news media. How to refine the set of indicators? Customarily, indicators project committees refine the initial set of indicators by focusing on those indicators in which secondary data are available from the various government agencies in the region. A case in point is Births to Teenage Girls–Births to girls aged 15–17. What government or non-government agency collects statistics related to this indicator? Does this agency have statistics related to the targeted community(ies)? Does the agency have trend data (i.e., data over time)? How to collect and report data based on refined set of indicators? The next step is for the indicators project committee to contact those agencies that maintain statistics related to the selected indicators and decide to import these data into a standardized platform that would allow the committee to analyze the data and generate descriptive statistics and graphs and charts. Examples of these platforms include Microsoft Excel, Minitab, SPSS, and SAS. How to validate the objective indicators with subjective ones? Many indicator project committees attempt to validate the data from the objective indicators with subjective indicators. The goal is to ensure that the objective reality is not divorced from the subjective experience of community residents. This validation comes about in the form of survey studies. A sample of community residents are contacted (by mail, telephone, Internet, door-to-door interviews, etc.) and asked questions about their experience with specific aspects of the community—conditions such as crime and traffic congestion, and services such as retailing, banking, telecommunications, transportation, electric power, etc. How to translate the system indicators into program and policy indicators? Traditionally, indicators project committees make every attempt possible to translate their research findings in ways that can be used by decision-makers. This is done by developing summary report cards by comparing the community with other communities, the state, the nation, or other geographic entities. Some committees generate charts showing trends and making specific recommendations—a call for action. Further, some committees attempt to translate their indicators into specific actionable policies and programs. How to disseminate the indicators’ findings? It is important to promote the findings as widely as possible. Indicators project committee do this by holding press conferences, press releases, posting the report on their websites and links of their websites with other websites that are used by the targeted stakeholders, printing the full and summary reports and distributing those reports to community leaders, and making paper copy summary reports available to community residents through Chambers of Commerce, the municipal buildings, and other government and non-profit agencies.

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Reference Cornwell, T. L. (2003). Vital signs: Quality-of-life indicators for Virginia’s Technology Corridor. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 1–28). Kluwer Academic.

Part II

Planning

Part II (Planning) contains two chapters. Chapter 4 describes the organizational aspects of indicators projects—holding a feasibility meeting, developing an organizational structure, visioning, finding an organization that will conduct the process, securing funding, and developing a budget. Chapter 5 describes how indicators decisions are made—deciding on geographic boundaries, selecting the quality-of-life dimensions, selecting the quality-of-life indicators, and considering subjective indicators.

Chapter 4

Organizing

Learning Objectives In this chapter you should be able to answer the following questions: 1. 2. 3. 4. 5. 6.

Why hold a feasibility meeting and how? How to develop an organizational structure? How to engage in visioning? What is a suitable organization that will conduct the process effectively? How to secure funding? How to develop and sustain a budget?

Introduction The organizing aspect of planning involves holding a feasibility meeting, developing and organizational structure, engage in a visioning process, finding an organization that will conduct the process, securing funding, and developing a budget. We will address these steps in some detail in the sections below.

Holding a Feasibility Meeting A feasibility meeting with community leaders representing major stakeholders in the area (i.e., public, private, and voluntary sectors) is a good way to initiate an indicators project. The person who holds that meeting should be a prominent community leader with a good reputation (e.g., a town or city mayor, the chair of the town/city council, a politician representing the area at the state level, a president or CEO of a major local bank in the area). This community leader should have enough clout to invite other community leaders to this feasibility meeting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_4

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This person must be convinced that the community should have a community indicators project in place to help community leaders understand the impact of their decisions on the quality of life of community residents. Understanding the impact of their decisions should enable them to modify and adjust their programs and policies to enhance community quality of life. He or she must present the idea of the need for a community indicators project to other community leaders in the most credible and effective way possible. He or she must explain to others why such a project is desirable and how it will benefit policy makers and other decision makers in the long run. The goal of the meeting is drum up support from community leaders about the benefits of such a project. Also, another goal of this meeting is to brainstorm with others about organizations that may be willing to sponsor the project and other funding sources. An important caveat should be noted here. There seems to be a perception that indicator projects are part of a “leftist agenda.” The perception is that much emphasis is placed on environmental well-being at the expense of economic development. Thus, many community leaders with “conservative” leanings are not likely to be receptive to the idea of a community indicators project. It is imperative to counteract this perception by arguing that an indicators project is apolitical (i.e., does not reflect an agenda from either the political right or left.). It is designed to gauge the quality of life in a local area along a comprehensive set of measures that include economic, social, and environmental well-being. Furthermore, the data should help decision makers in both public and private sectors formulate programs and policies designed to benefit the community at large.

Developing an Organizational Structure Here is an example of an organizational structure that was used by the Jacksonville Community Council, Inc. (JCCI) in Jacksonville, Florida, USA, considered to be prototypic (Chambers & Swain, 2006). The JCCI organizational chart is shown in Fig. 4.1. JCCI’s organizational structure involves six different organizational entities: funding source(s), sponsoring organization, a chair, a steering committee, several task forces, and staff support (see Fig. 4.1). We will describe the chair, the steering committee, the task forces, and the staff in some detail. We will address funding sources and supporting organizations in some detail in the following sections.

The Chair The chairperson of the JCCI indicators project was responsible for the entire project. His first task was to recruit members of the steering committee. He chaired the

Developing an Organizational Structure

67

SPONSORING ORGANIZATION

FUNDING SOURCES

(Coordinates entire project; administers funds; and provides staff support)

(Provides funding for the project)

CHAIR

STAFF

(Responsible for entire project; recruits steering committee; serves as project spokesperson)

(Coordinates meetings; prepares meeting summaries; compiles indicators)

STEERING COMMITTEE (Serves as chairs of the task forces; recruits task force members; reviews task force work)

TASK FORCE 1 (Selects indicators related QOL dimension 1)

TASK FORCE 2

TASK FORCE 3

TASK FORCE 4

(Selects indicators related QOL dimension 2)

(Selects indicators related QOL dimension 3)

(Selects indicators related QOL dimension 4)

TASK FORCE N (Selects indicators related QOL dimension n)

Fig. 4.1 Organizational structure of a typical indicators project. Source: Adapted from Chambers and Swain (2006, p. 288)

meetings of the steering committee, attended task force meetings as needed, and chaired meetings of task forces and steering committee combined. He was the major spokesperson for the indicators project. The chairperson was widely respected in the community and possesses good leadership and interpersonal skills.

The Steering Committee The JCCI’s steering committee was responsible for coordinating the entire project and for recruiting task force members. Each steering committee member served as chair or co-chair of a task force. The steering committee debated the overall mission of the project; identified funding sources and organizational sponsors; made planning, development, and implementation decisions; recruited volunteers for the task

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Power • Government • Courts • Unions • Etc.

Recititude • Religious organizations

Respect • Civil-rights groups • Fraternities • Sororities • Honor societies • Etc.

Wellbeing • Hospitals • Medical clinics • Social services • Etc.

Wealth and Poverty • Landowners • Businesses • Insurance companies • Foundations • Poverty programs • Etc.

Organizing

Enlightenment

Skills

Affection

• Higher education • Research organizations • Media, etc.

• Private schools • Public schools • Trade schools • Workplace development • Etc.

• Families • Family support groups • Youth groups • Etc.

Fig. 4.2 Identifying important groups that should be represented on the steering committee guided by Lasswell’s institutional/values categories. Source: Adapted from Grunkemeyer and Moss (2004, p. 32)

forces; received and approved the task force reports; and assures coordination of the work of the various task forces. Members of the JCCI’s steering committee possessed all the following skills and abilities: • Leadership in their own occupation • Enthusiasm for the project and the motivation to see it through • Leadership skills—the ability to facilitate group interaction and to help the group reach consensus • An interest in statistics and general understanding of statistics • Representation from the media • Connections with funding sources What about the size of the steering committee? The steering committee should be large enough to provide chairs or co-chairs for each task force. In the JCCI model, nine quality-of-life dimensions (the economy, education, public safety, health, the natural environment, the social environment, politics/government, mobility, culture/ recreation) were selected. Therefore, ten people were recruited (the 10th member being the chair). It should be noted that the steering committee should include representatives from all sectors of the community. To do this, one has to have some conceptual tool to identify the various sectors of any community. The Ohio Sustainable Communities team (Grunkemeyer & Moss, 2004, p. 32) used Harold Lasswell’s theory of community institutional and value categories (Lasswell, 1958, p. 202) to accomplish this task. This taxonomy based on Lasswell’s theory is shown is Fig. 4.2. As shown in the figure, Lasswell’s taxonomy starts out with a set of universal community values—values that are considered universally positive guiding decision-making and policy formulation. That is, people value power, rectitude (i.e., morally correct behavior), respect, wellbeing, wealth (an abhor poverty), enlightenment, skills, and affection. Underlying each of these values are institutions that have core missions guided by these values. As such, community institutions guided by power include government organizations, the courts, labor unions, etc. Community institutions guided by rectitude involve mostly religious organizations. Institutions guided by respect include civil-rights groups, fraternities, sororities, and honor societies. Institutions guided by the value of wellbeing include hospitals,

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Table 4.1 Identifying possible sponsoring organizations for the Ohio sustainable communities indicators projects Community sector Wealth/poverty (landowners, businesses. Insurance companies, community foundations, and organizations overseeing poverty programs) Wellbeing (health, wellness, safety, and supporting organizations)

Enlightenment (higher education, research organizations, media) Rectitude (religious organizations) Skills (public and private schools, trade schools, workplace development) Power (government, courts, labor unions)

Affection (families, family support groups, youth groups)

Respect (civil rights groups, fraternities, sororities, honor societies, citizens organizations

Sponsoring organization/entity Noble Chamber of Commerce, Retail Merchants Association, Convention and Visitors Bureau, GMN Tri-County Community Action Agency, Whisky Run Golf Course Noble County Health Department, Noble Parks and Recreation Program, MRDD Board, Caldwell Fire Department, Soil and Water Conservation District, Noble County Emergency Management Office Ohio State University Extension County Office, Journal Leader Newspaper, WWKC Radio Station The Noble Ministerial Association Noble Local Shenandoah School District, Caldwell Exempted Village School District, Noble County Jobs and Family Services Caldwell Village Council, Summerfield Village Council, Belle Village Council, Sarahsville Village Council, Noble County Commissioners, Noble Township Trustees Association, Court of Common Pleas, Human Services Department, County Commissioners, Noble County Regional Planning Commission Noble County Senior Citizen’s Organization, Noble Summer Youth Program, Family and Children First Program, Childcare Resource Network, Noble County Service Club Noble County Farm Bureau, Southeastern Ohio Farmer’s Union

Source: Adapted from Grunkemeyer and Moss (2004, p. 34)

medical clinics, and social services. Institutions related to wealth and poverty include landowners, businesses, insurance companies, community foundations, and organizations overseeing poverty programs. Institutions related to enlightenment include higher education, research organizations, and the media. Institutions focusing on skills include private and public schools, trade schools, and workplace development programs. Finally, institutions related to affection include families, family support groups, and youth groups. Applying this taxonomy of community institutions to the Ohio Sustainable Communities resulted in the identification of the following community institutions in the county of Noble, Ohio (Grunkemeyer & Moss, 2004, p. 34). These are shown in Table 4.1.

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Task Forces JCCI’s task force members were charged with the selection of indicators associated with each dimension of the quality-of-life model (the JCCI project employed nine dimensions—see above). Each task force decided initially on the most important indicators of the assigned dimension. The final decision (of which indicators should be included in the final set) was made in a joint meeting between the steering committee and the task forces. How to assemble the task forces? The JCCI issued an open invitation to membership (through mass media) coupled with selective recruitment. The goal of the open invitation was to ensure that no one is excluded. Selective recruitment was designed to supplement the open enrollment to obtain members with needed skills and expertise. The open invitation was issued by a person with great clout and credibility (city mayor). What Is the Ideal Composition of a Task Force? JCCI considered the following criteria in assembling each task force: (a) diversity, (b) familiarity and comfort with statistics, (c) familiarity with the particular quality-of-life dimension (economy, education, public safety, etc.). With respect to the latter criterion, JCCI made a concerted attempt to avoid recruiting people who are directly involved with the sources of data because they may have had a biased point of view or a tendency to push for the use of indicators that may make their agency or department look good. Instead, JCCI used the data source people by inviting them to make presentations at task force meetings as resource people rather than as official members of a task force. Finally, JCCI’s task forces were balanced among competing interests. For example, the economy task force had business- and non-businesspeople. What About the Size of a Task Force? The JCCI model calls for 8–15 people. JCCI avoided task forces with fewer than eight because they were not likely to have breadth of ideas. In contrast, groups larger than 15 entail longer discussions, greater difficulty in making decisions, and higher costs for administration; hence, they were avoided. Further, considering that any particular meeting draws only 75% attendance, meetings averaged ten members.

Visioning Given that the indicators project is determined to be feasible and an organizational structure is established, the next step is “visioning.” Visioning is a process that allows representatives of key stakeholder groups (as members of the steering committee) to meet, brainstorm, and answer the question “where do we want to be.” The goal is to develop a shared vision of their ideal community. This shared vision should be highly instrumental in guiding the entire indicators project.

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71

The visioning process comes about by discussing the current needs and strengths of the community. The discussion should lead the steering committee to develop a vision of a community capitalizing on its strengths and laying out a path to take corrective action to address needs. The product of this visioning process should answer the question: what matters to this community? (Greene et al., 2000; Stevens et al., 2021).

Finding an Organization that Will Conduct the Process Every community indicators project needs an organizational sponsor. A sponsor leads the development of the indicators project and provides staff support (and in some cases provides funding too). This sponsor should have the following characteristics: • The sponsor should be able to commit a project coordinator and support staff to the project, • The project coordinator and some of the professional staff should be skilled in research, • The assigned staff should be skilled in working with volunteers, • The assigned staff have the ability to identify and recruit volunteers from all walks of life, • The organization should be stable and able to commit to work on the project on an annual basis, • The sponsor should be highly regarded in the community, and • The sponsor should have a mission compatible with the project. Organizational sponsors can be divided into two major categories: Government and non-government sponsors (Swain, 2002, pp. 7–8). The key benefit of seeking governmental organizations (e.g., Mayor’s Office, City Council) as sponsor for the indicators project is the reality that they are “plugged” into the formal decisionmaking process of using the results of the indicators project to create or modify policies and programs. The key benefit of seeking non-governmental sponsors is an open and broadly participatory process that may result in community engagement.

Securing Funding Who will fund such a project? Funding sources will vary in different communities. Because this is a community project, a diversity of funding sources is recommended. A partnership of multiple funders may work best in terms of buy-in as well as funding. Examples of funding sources include:

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Organizing

• • • • • •

Local or regional community foundations; Locally based private foundation; City, county, or state government (e.g., town council); Chamber of Commerce; Local media (e.g., local/regional newspaper, television, or radio station); Local large companies with a reputation of corporate social responsibility and community involvement; • Local United Way and other major charity organizations; and • Hospitals, health planning councils, or other health consortia. Additional funding may be in the form of in-kind contributions. It is possible to fund pieces of the project separately, such as a telephone survey, printing, or loaned staff. Specifically, a telephone survey may be funded by a local marketing research company, the local telephone company, or a local university or college. The printing of the survey questionnaire may be funded by a local printer, etc. Loaned staff may come from a local college or university through students seeking internships in related majors (e.g., urban planning, marketing research, survey research, sociology, economics, political science, economic development, and community development). A recent survey of community indicator projects revealed that the majority of the organizations secured funding from foundations/grants, followed by public/government sources, private sources, and other sources, respectively. For example, the Boston Indicators Project has been largely funded by the Boston Foundation with occasional additional funds from by local foundations and contracts. The project’s budget was reported to be between $300,000 and $450,000 annually in the late 1990s and early 2000s (Kahn, 2006). With respect to in-kind support, much of the support came from public/government sources (Alliance for Regional Stewardship, 2006). The reader should note that there are pros and cons in securing funding from specific community sponsors (see Table 4.2). As shown in the table, sponsors of community indicators projects tend to involve a wide range of community organizations: community foundations, nonprofit organizations, local government, and academic institutions. Community foundations tend to be a significant source of funding for community indicators projects.1 Most community foundations provide funding to community indicators projects through grants. They tend to be neutral, well-funded, and well-embedded in the community. They also are action-oriented providing impetus for community action. In contrast, community foundations have their own specific community goals and objectives, which may or may not sync with the goals of community indicators projects—goals that are varied and comprehensive in terms of covering a wide range of conditions and services embedded in the community. As such, community foundations are likely to push for their own

1

Recent years have seen growth in a variety of place-based funders (Mazany & Perry, 2014; Philipp & Traylor, 2014; Ridzi & Prior, 2020)—more than 75% of community foundations that exist today were created in the last four to five decades. As such, community foundations solicit and invest donations by supporting indicators projects.

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Table 4.2 Advantages and disadvantages related to securing funding from different community organizations Community foundations

Nonprofit organizations

Local government

Academic institutions

Advantages Community foundations tend to be neutral, well-funded, and wellembedded in the community; they tend to be action-oriented providing funds to community indicators projects through grants

Nonprofit organizations tend to be wellconnected to the community and other community-based organizations; they are accustomed to work cooperatively with other community organizations. They may provide nonfinancial support in terms of human volunteers, organizational functionality, and perhaps physical space In many instances, local government provides some funding (usually in terms of matching funds with other community-based organizations) Academic institutions tend to be neutral. They may provide some seed money but most importantly they tend to be an important source of experts in both subject matter and research methodology

Disadvantages Community foundations have specific community goals and objectives, which in turn likely to influence selection of community indicators. As such, the selected community indicators may not be representatives of a comprehensive set of community wellbeing indicators Not a good source of funding because they tend to work hard to secure their own funding

Local government officials tend to be political in their views and action; as such, their funding tend to unduly influence the selection of community indicators Academic institutions differ in their orientation to academic research and service to the community. Regional colleges and universities tend to be better connected to the community and are likely to be more engaged. In contrast, research universities are not wellconnected to the community and usually have their own research agenda

agendas—likely to influence selection of community indicators. This may be an over-generalization. That is, there are many community foundations (e.g., United Way) that have goals and aspirations highly consistent with the basic mission of community indicators projects. Consider the United Way of South Hampton Roads (in the State of Virginia, USA: https://www.unitedwayshr.org/community-data). The community indicators project sponsored by this United Way tend to be more neutral compared to other community foundations. This indicators project seems to capture a wide range of community quality-of-life indicators in a non-biased manner—indicators related to health, economy, education, natural and built environment, and social environment. The same can be said for the Vancouver Foundation and its Vital Signs indicators project (Ridzi & Prior, 2020). In contrast, the Annie E. Casey Foundation in the U.S. supports community indicators projects related to children well-being in a community context (through the foundations’ KID COUNT initiative). See example of one of the many KIDS COUNT indicators projects

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supported by the Casey Foundation: the Rhode Island KIDS COUNT Indicators Project (https://www.rikidscount.org/). This observation is reinforced by the case study on the Vancouver Foundation (Ridzi & Prior, 2020). That is, the authors also observed that some community foundations have moved away from a comprehensive examination of community quality of life in favor of specific issues such as social capital and the creative workforce. Nonprofit organizations tend to be another source of funding for community indicators projects. They tend to be well-connected to the community and other community-based organizations. They are adept in working cooperatively with other community organizations. With respect to funding, they may provide nonfinancial support in terms of human volunteers, organizational functionality, and perhaps physical space (i.e., no liquid cash). Nonprofit organizations are not usually considered a good source of funding because they tend to work hard to secure their own funding. Here is an example. In most communities there are women’s resource centers; these are nonprofit organizations that support battered and abused women. The basic mission of these nonprofits is to provide a safe haven for abused and battered women and help them “get back on their feet” to normal lives. These nonprofits do support community indicators projects in many communities in the U.S. Their interest in community indicators projects is narrowly focused on health and well-being indicators of women (more specifically battered and abused women). As such, these organizations are eager and willing to provide nonfinancial support as long as they have a voice in selecting indicators related to women’s health and wellbeing in those areas where they provide safe haven for the battered/abused women. Local governments can be a significant source of funding for indicators projects, and they usually are. A popular way of government funding is the so-called “matching grants”—local government provides some funding as long as these funds are matched with other funding from other community-based organizations. Disadvantages include the distinct possibility that local government officials may be political in their views and action; as such, their funding can unduly influence the selection of community indicators. Finally, academic institutions can also be a source of funding (perhaps some seed money). Academic institutions tend to be neutral. They may provide some seed money but most importantly they tend to be an important source of subject matter experts (SMEs) in the various areas of community well-being (e.g., an economist to capture economic well-being; an ecologist to capture environmental well-being, etc.) as well as experts in research methodology (e.g., social statistician, survey researcher). It should be noted that academic institutions differ in their orientation to academic research and service to the community. Regional colleges and universities tend to be better connected to the community and are likely to be more engaged. In contrast, research universities are not wellconnected to the community and usually have their own research agenda.

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Developing and Sustaining a Budget A typical budget for the first year of operation for an indicators project is US$25,000–$50,000. Income for the first year of operation involves major funding sources and in-kind services (e.g., pro-bono printing, volunteer staff). Expenditures include staff salaries and benefits, overhead (occupancy, telephone, computers, equipment), and copying costs and printing. After the first year, the cost of the project is significantly reduced. At that point, the major costs are that of staff time in collecting, compiling, and presenting the data, as well as printing costs. Staff salaries and benefits are the major cost item. It is difficult to reduce these costs except through the use of loaned staff, student interns, and community volunteers. Conducting a primary data collection (i.e., community survey) adds a substantial amount to costs, even with in-kind services donated. Overhead costs typically vary according to the sponsoring organization. Printing costs also vary, depending on the availability of in-kind contributions. Given the trends toward reduced taxation and cost savings, many indicators projects have sought means to reduced their budget and achieve cost savings. One way to achieve cost savings is to coalesce with other localities and expand the scope of the indicators project (i.e., to include neighboring towns, cities, counties, and other geographic units). For example, the Quality of Life in New Zealand’s Largest Cities Project (Jamieson, 2004) is a joint collaborative initiative where the main goal is advocacy on quality of life issues for large cities across New Zealand rather than individual cities. This collaborative approach achieves cost savings through: • • • •

shared data collection costs, shared data analysis costs, shared report publication costs, and reduced staffing costs.

How about sustaining a budget? Based on their experience with the Twin Cities Compass project, Helmstetter et al. (2011) recommend the following fund-raising strategies: public website, advisory group, in-person presentations, media, newsletters, print pieces, and social media.

Public Website Most indicators projects have their own websites. The website allows visitors to select key measures and provide data and charts about these key measures. As such, users of the key measures are requested to make a donation to support the activities of the indicators project. The public website also serves to draw attention to other services provided by the indicators project (e.g., contractual work).

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Advisory Group Most indicators projects have advisory groups in the form of a steering committee and task forces focusing on specific domains of indicators (e.g., economic indicators, health indicators, environmental indicators, . . .). There are many instances in which members of the advisory groups are called upon to conduct contractual work for specific organizations (e.g., develop an indicators report for a local hospital, assess the economic impact of a local college in the region).

In-Person Presentations There are many occasions in which the staff of the indicators projects are asked to make presentations at events and meetings convened by others. These in-person presentations are an important impetus to engage with sponsors to ensure future funding. Furthermore, in many instances these presentations become a source contractual work for the indicators organization.

Media Recognition in the local media (local newspapers, television news, and radio programming) serves to keep the indicators project on the “radar” of major sponsors and possibly future funders. Local media place high value in publicizing the study findings of indicators project because these findings become a significant source of editorial content—a source of local media stories. In some cases, the media organizations develop programming content with the assistance of the indicators projects staff, and contribute funds in that regard. The media publicity helps with securing future funding and plays a major role in donation from the local public.

Newsletters Most indicators projects have a regular monthly print or electronic newsletter widely disseminated to the projects’ sponsors, the local media, and the general public. The newsletter provides routine updates about newly posted statistics. The newsletter also publicizes upcoming events (i.e., presentations in public forums such as schools and municipalities). Visitors to the indicators projects’ websites are requested to subscribe to the newsletter. As such, indicators projects tend to amass a large subscriber distribution list. Requests for donations are made through these newsletter as well as through the hosted events hosted.

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Print Pieces Most indicators projects tend to “sell” print pieces of two reports: the short-form report that summarizes much of the statistics and the long-form that is more technical, providing greater detail. These print pieces become a significant source of revenue when many of the local organizations buy them for their own promotion goals. Many organizations use the data to recruit employees and local hospitality organizations use the data to highlight attractive community features to tourists and newcomers to the area. Additionally, these print pieces do serve to stimulate interest in contractual work, which in turn becomes another significant source of revenue.

Social Media The increasing popularity of social media has prompted many indicators projects to use this vehicle to raise funds. The most popular forms of social media involve blogs and videos. Blogs disseminating information and statistics about the local conditions are becoming commonplace. Similarly, many of the in-person presentations (discussed above) are videotaped and posted on YouTube with links in the indicators projects’ websites. This type of publicity on social media reinforces fund-raising efforts.

Summary To cap the discussion, this chapter covered organizing aspects of planning: holding a feasibility meeting, developing and organizational structure, engage in a visioning process, finding an organization that will conduct the process, securing funding, and developing a budget. Specifically, we discussed the need to hold a feasibility meeting with community leaders representing major stakeholders in the area (i.e., public, private, and voluntary sectors). The person who holds that meeting should be a prominent community leader with a good reputation. This community leader should have enough clout to invite other community leaders to this feasibility meeting. The goals of the meeting are multifold: drum up support from community leaders and brainstorm with others about organizations that may be willing to sponsor the project and other funding sources. A typical organizational structure for a community indicators project involves six different organizational entities: funding source(s), sponsoring organization, a chair, a steering committee, several task forces, and staff support. The chairperson is responsible for the entire project. This person is customarily tasked with recruiting members of the steering committee, chairing the meetings of the steering committee, attending task force meetings as needed, and chairing meetings of task forces and

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steering committee combined. The steering committee is responsible for coordinating the entire project and for recruiting task force members. The steering committee has to represent various and important stakeholder groups and institutions in the community. Each steering committee member serves as chair or co-chair of a task force. The steering committee develops the overall mission of the project; identifies funding sources and organizational sponsors; makes planning, development, and implementation decisions; recruits volunteers for the task forces; receives and approves the task force reports; and assures coordination of the work of the various task forces. Task force members are charged with the selection of indicators associated with each quality-of-life dimension. Visioning is a process that allows representatives of key stakeholder groups (as members of the steering committee) to meet, brainstorm, and answer the question “where do we want to be.” The goal is to develop a shared vision of their ideal community. The visioning process involves a discussion of the current needs and strengths of the community. The discussion leads the steering committee to develop a vision of a community capitalizing on its strengths and laying out a path to take corrective action to address needs. Community indicators projects should have an organizational sponsor. The sponsor serves to lead the development of the indicators project and provides staff support, and in some cases provides funding too. The sponsor should be able to commit a project coordinator (skilled in research) and support staff to the project. The sponsor should be stable and able to commit to work on the project on an annual basis; should be highly regarded in the community; and should have a mission compatible with the indicators project. Funding sources vary in different communities. Examples of funding sources include local or regional community foundations; locally based private foundation; city/county/state government; Chamber of Commerce; local media; local large companies with a reputation of corporate social responsibility and community involvement; local charity organizations; and hospitals, health planning councils, or other health consortia. It should be noted that there are pros and cons in securing funding from specific community sponsors. Many of the organizations willing to provide funding may have specific political agendas that may bias the scientific basis of the indicators project. Developing a budget is yet another organizing function. Income for the first year of operation involves major funding sources and in-kind services. Expenditures include staff salaries and benefits, overhead, and copying costs and printing. After the first year, the cost of the project is significantly reduced. At that point, the major costs are that of staff time in collecting, compiling, and presenting the data, as well as printing costs. Staff salaries and benefits are the major cost item (which can be reduced through the use of loaned staff, student interns, and community volunteers). Conducting a primary data collection adds a substantial amount to costs. Overhead costs typically vary according to the sponsoring organization. Printing costs also vary, depending on the availability of in-kind contributions. Cost savings can be achieved through coalescing with other localities to expand the scope of the indicators project. Doing so can achieve cost savings through shared data collection costs,

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shared data analysis costs, shared report publication costs, and reduced staffing costs. Sustaining a budget involves sustained efforts at promotion involving requests for donations, fund-raising from major sponsors, offering print pieces of the annual report for sale, and offering contractual services to the major sponsors and other organizations.

Progress Check 1. 2. 3. 4. 5. 6.

Why hold a feasibility meeting and how? How to develop an organizational structure? How to engage in visioning? What is a suitable organization that will conduct the process effectively? How to secure funding? How to develop and sustain a budget?

Progress Check Answers 1. Why hold a feasibility meeting and how? A feasibility meeting is a gathering of community leaders representing major stakeholders in the area (i.e., public, private, and voluntary sectors). The person who holds that meeting typically is a prominent community leader with a good reputation. The goal is to convince community leaders of the need for a community indicators project. The indicators project should help community leaders understand the impact of their decisions on the quality of life of community residents, which in turn should enable leaders to modify and adjust their programs and policies to enhance community quality of life. Another goal of this meeting is to brainstorm with others about organizations that may be willing to sponsor the project and other funding sources. 2. How to develop an organizational structure? An organizational structure may be composed of six different organizational entities: funding source(s), sponsoring organization, a chair, a steering committee, several task forces, and staff support. The chairperson is responsible for the entire project—recruiting members of the steering committee, chairing meetings of the steering committee, attending task force meetings, and chairing meetings of task forces and steering committee combined. The steering committee is responsible for coordinating the entire project and for recruiting task force members. Each steering committee member serves as chair or co-chair of a task force. The steering committee is responsible to debate the overall mission of the project, identifies funding sources and organizational sponsors; makes global decisions, and recruits volunteers for the task. The steering committee has to represent various and important stakeholder groups and institutions in the community. Task force members are charged with the

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selection of indicators associated with each element of the indicators system. Each task force decides initially on the most important indicator of those within the element. The final decision of which indicators should be included in the final set was made in a joint meeting between the steering committee and the task forces. How to engage in visioning? Visioning is a process that allows representatives of key stakeholder groups (as members of the steering committee) to meet, brainstorm, and answer the question “where do we want to be.” The goal is to develop a shared vision of their ideal community. The visioning process involves a discussion of the current needs and strengths of the community. The discussion leads the steering committee to develop a vision of a community capitalizing on its strengths and laying out a path to take corrective action to address needs. What is a suitable organization that can conduct the process effectively? The organization that conducts the indicators project successfully must be able to commit a project coordinator and support staff to the project. The project coordinator and some of the professional staff should also be skilled in research. The assigned staff should be skilled in working with volunteers. The assigned staff should have the ability to identify and recruit diverse volunteers. The organization should be stable and able to commit to work on the project on an annual basis. The sponsor should be highly regarded in the community. And the sponsor should have a mission compatible with the project. How to secure funding? Funders should be varied because funders may exert influence and the resultant indicators may be biased in favor of the funder. Examples of funding sources include local or regional community foundations; a locally-based private foundation; city, county, or state government; Chamber of Commerce, local media; local large companies with a reputation of corporate social responsibility and community involvement; major charity organizations; and hospitals, health planning councils, or other health consortia. Additional funding may be in the form of in-kind contributions. How to develop and sustain a budget? Income for the first year of operation involves major funding sources and in-kind services. Expenditures include staff salaries and benefits, overhead, and copying costs and printing. After the first year, the cost of the project is significantly reduced. At that point, the major costs are that of staff time in collecting, compiling, and presenting the data, as well as printing costs. Staff salaries and benefits are the major cost item. Cost savings can be achieved through coalescing with other localities to expand the scope of the indicators project. Doing so can achieve cost savings through shared data collection costs, shared data analysis costs, shared report publication costs, and reduced staffing costs. Sustaining a budget involves sustained efforts at promotion involving requests for donations, fund-raising from major sponsors, offering print pieces of the annual report for sale, and offering contractual services to the major sponsors and other organizations.

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References Alliance for Regional Stewardship. (2006). Regional indicators: Telling stories, measuring trends, inspiring action. Alliance for Regional Stewardship. Chambers, M., & Swain, D. (2006). Quality indicators for progress: A guide to community qualityof-life assessments. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 267–322). Springer. Greene, G., Haines, A., & Halebsky, S. (2000). Building our future—A guide to community visioning. Cooperative Extension of the University of Wisconsin. Retrieved from https:// learningstore.uwex.edu/Assets/pdfs/G3708.pdf Grunkemeyer, W. T., & Moss, M. L. (2004). The sustainable community model approach to the development and use of multi-dimensional quality of life indicators. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 29–52). Kluwer Academic. Helmstetter, C., Mattessich, P., Egbert, A., Brower, S., Hartzler, N., Franklin, J., & Lloyd, B. (2011). Sustaining the operations of the community indicators projects: The case of Twin Cities Compass. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 47–66). Springer. Jamieson, K. (2004). A collaborative approach to developing and using quality of life indicators in New Zealand’s largest cities. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community qualityof-life indicators: Best cases (pp. 75–109). Kluwer Academic. Kahn, C. (2006). A framework for social change. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 23–42). Kluwer Academic. Lasswell, H. (1958). Politics: Who gets what, when, how. The World. Mazany, T., & Perry, D. C. (2014). The second century: Community foundations as foundations of community. In T. Mazany & D. C. Perry (Eds.), Here for good: Community foundations and the challenges of the 21st century (pp. 17–40). Routledge. Philipp, A., & Traylor, T. (2014). Ensuring there is “community” in the community foundation. In T. Mazany & D. C. Perry (Eds.), Here for good: Community foundations and the challenges of the 21st century (pp. 137–144). Routledge. Ridzi, F., & Prior, T. (2020). Community leadership through conversations and coordination: The role of local surveys in community foundation run community indicators projects. International Journal of Community Well-Being. https://doi.org/10.1007/s42413-020-00098-z Stevens, C., de Blois, M., Hemberg, R., Baldwin, J., & The Community Indicators Consortium. (2021). Community indicators project development guide. Community Indicators Consortium. Swain, D. (2002). Measuring progress: Community indicators and quality of life. Jackson Community Council.

Chapter 5

Making Decisions About Indicators

Learning Objectives In this chapter you should be able to answer the following questions: 1. 2. 3. 4.

How to decide on geographic boundaries and units within? How to select the quality-of-life dimensions? How to select the quality-of-life indicators? Should subjective indicators be used?

Introduction This chapter focuses on decisions related to indicators selection. Specifically, we will discuss issues related to how to decide on geographic boundaries and units within, how to select the quality-of-life dimensions, how to select the quality-of-life indicators, and the pros and cons of using subjective indicators.

Deciding on the Geographic Boundaries and Units Within In this section, we will try to help the reader understand how to answer two important questions: (1) what are the geographic boundaries of the indicators project, and (2) will the selected geographic entity be divided into smaller units, and if so what are they? However, to answer those questions, the reader must have an appreciation of the concept of unit of analysis and data aggregation and disaggregation. What is the unit of analysis and data aggregation/disaggregation? In community indicators research, much of the data reflect five different levels of analysis: (a) the individual level, (b) the household level, (c) the neighborhood level, (d) the entire town or city, or (e) the county level or a larger geographic entity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_5

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Data at the individual level reflect observations about a quality-of-life element (e.g., economic well-being) that reflect community residents (i.e., individuals). For example, a survey may be conducted with a sample of community residents. Survey respondents are asked questions about their economic, social, health, and environmental well-being. Data at the household level, in contrast, focus on groups of people living in homes such as single-family dwelling or apartments located within the community. The focus here is counting some characteristic about economic, social, health, and environmental well-being of families (biological families as in a family that includes father, mother, and children or non-biological such as several people living together as roommates) residing in the community in question. For example, much of the census data is at the household level because data are gathered by asking an adult member of a household to complete the census questionnaire on behalf of the entire household. Household data come from two sources: household-level data (as described above) and individual-level data aggregated at the household level. What does individual-level data aggregated at the household level mean? This means that if community indicator researchers collect individual-level data, they can aggregate the data by household. This can be done only if the researchers know which individuals belong to which household. Individuals within a particular household are aggregated to characterize a quality-of-life dimension (e.g., economic well-being) of that household. Although this is theoretically doable, in reality most of the household data reflect household-level data, not individual-level data aggregated to the household level. Data at the neighborhood level considers entire neighborhoods. For example, some indicators data come in the form of neighborhood quality. Neighborhood quality is typically broken down in terms of three dimensions: physical, economic, and social. Indicators of physical quality of a neighborhood may include home upkeep, graffiti, beauty of the landscape, land and noise pollution. Indicators of economic quality of a neighborhood include the average market price of homes in the neighborhood and the rate of appreciation or depreciation of these homes. Indicators of the social quality of a neighborhood include number of reported violent crimes in the neighborhood, the neighborhood reputation of street crime, and perception of racial or ethnic strife in the neighborhood. As discussed in reference to household-level data, neighborhood-level data can also be derived by aggregating individual-level data aggregated to the neighborhood level to characterize particular neighborhoods along a quality-of-life dimension. Similarly, household-level data can be aggregated to the neighborhood level. Data at the town, city, or county level focus on a larger geographic unit. For example, air pollution of a particular town, city, or county is estimated at that level of analysis. That is, air pollution is typically not measured at the household or neighborhood level but a larger geographic entity such as a town, city, or county. In contrast, other measures of community quality of life are derived by aggregating data from individual level (e.g., perceived quality of life in the community measured by a survey of community residents who rate the quality of their community on a 5-point scale varying from “very bad” to “very good”). Similarly, data from household and

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neighborhood levels can be aggregated to reflect quality-of-life characteristics of towns, cities, and counties. Now let’s revisit the two key questions: (1) what are the geographic boundaries of the indicators project, and (2) will the selected geographic entity be divided into smaller units, and if so, what are they? The two questions can be answered by considering several factors: the availability of secondary data, the cost of primary data, and meaningful impact. Suppose you find that most of the secondary data that are available and pertinent being compiled at the city level. If you extend the geographic boundaries to the county, this means that primary data must be collected (i.e., new data that has to be collected by your organization by conducting a survey). Of course, the cost of this extra data must be considered. Do you have the resources to collect primary data? If little resources are available to collect primary data, then the answer is clear: the geographic boundary is the city because secondary data are available at the city level and we cannot afford to collect primary data to capture remaining localities (non-cities) within the county. Answering this question should help in deciding on the geographic boundaries of the indicators project. The same consideration should help in answering the question about dividing the geographic entity into smaller units. Now bring into account the meaningful impact factor and re-weigh that decision. Residents tend to identify most strongly with the smallest unit of government. They feel that their best chance for programs and policies to make a difference is to understand how those programs and policies work at the smallest unit possible (i.e., individuals, households, and neighborhoods). It is difficult to appreciate the effectiveness of particular programs and policies if the assessment is made at a larger unit of analysis, namely the city or county level (or perhaps a larger geographic unit). Therefore, the meaningful impact is an important factor to consider in defining the scope of the indicators project. If the answer is although we have secondary data available at the city level, we can afford to collect primary data that would allow us to capture community quality of life at the individual level (or household or neighborhood level). Doing so would allow us to include the surrounding localities (those other localities within the county not accounted for by the city within the county). And doing so would allow us to achieve meaningful impact. In considering the meaningful impact factor in making geographic boundary decisions, the reader should further consider the “average” problem. If the unit of analysis is too large you end up with an average problem. That is, gathering data at a large community or regional level tends to mask neighborhood-level differences, many of which maybe high significant. For example, air pollution may be at an acceptable level at the monitoring stations but may be unacceptable in a low-income part of the community. Averages, by definition, mask differences, some of these differences may be important to certain constituencies. So, as you move in size from town to city to county to region, this “average” problem gets worse. Having discussed the ideal case scenario, we have to admit that selecting the geographic unit is a political process. It involves political coalitions and a great deal of politicking. See the politics involved in in the San Diego Indicators Project as an example in Box 5.1 (Jarosz & Williams, 2004).

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Box 5.1 San Diego Indicators Project: Selecting the Regions The San Diego Indicators Project team selected 21 regions. The selection process involved quantitative data and input from the Advisory committee, which was mostly a political matter. The regions were initially selected based on population size—19 regions were selected that are comparable in population size to the San Diego region, approximately one million people. These regions were then evaluated based on three sets of sustainability indicators: economic, environment, and equity. At this stage, a political discussion ensued and four regions were added and three dropped. The final list involved 20 regions in addition to San Diego. Source: Adapted from Jarosz and Williams (2004, p. 188)

Selecting the Quality-of-Life Dimensions Community quality-of-life dimensions are those conceptual categories that make up the conception of a high quality-of-life community. How is a community high in quality of life defined? For example, should one define a high quality-of-life community as one that is rated highly in terms of economic well-being, consumer well-being, health well-being, social well-being, environmental well-being? What is the conceptual makeup of a high quality-of-life community, and how does this community differ from other communities low in quality of life? There are two approaches to selecting the quality-of-life dimensions: (a) top-down and (b) bottom-up. We’ll discuss these in some detail.

Top-Down Approach to Selecting Quality-of-Life Dimensions The top-down approach advocates that we start out with a theoretical concept. In Chap. 2 five different theoretical concepts guiding community indicators research were presented: (a) personal utility, (b) opulence, (c) social justice, (d) human need satisfaction, and (e) sustainability. Suppose one buys into the concept of sustainability. In that chapter we described this concept to involve two key dimensions: human well-being and ecosystem well-being. The human well-being dimension is further broken into five sub-dimensions: (a) health and population, (b) wealth, (c) knowledge and culture, (d) community, and (e) equity. Each of these sub-dimensions is further broken into sub-sub-dimensions. Specifically, health and population involve physical health, mental health, disease, mortality, fertility, and population change. Wealth involves income; poverty; inflation; employment; infrastructure; and basic needs for food, water, and shelter. Knowledge and culture involve education and communication. Community involves institutions, law,

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crime, and racial and ethnic strife. Finally, equity involves distribution of benefits and burdens between social groups. Similarly, the ecosystem well-being dimension has at least four sub-dimensions: (a) land, (b) water, (c) air, and (d) resource use, and each of these sub-dimensions has its own sub-sub-dimensions. Land involves diversity and quality of forests, farmland and other land ecosystems, including their modification, conversion, and degradation. Water involves diversity and quality of inland water and marine ecosystems, including their modification by dams and other structures; pollution; and water withdrawal. Air involves local and indoor air quality. Finally, resource use involves energy and materials, waste generation and disposal, and recycling. How to select a community quality-of-life theoretical concept that guides the formulation of the dimensions? The answer to this question is community concerns. Each community has its own set of concerns. For example, if a community is besieged with problems of business failures and unemployment, then perhaps the opulence concept should be selected. Opulence motivates indicator researchers to focus on economic development and many dimensions of economic development (e.g., unemployment, business failures, quality of jobs, number of jobs lost, and recruitment of new businesses). If a major concern in the community is pollution from industrial development, then perhaps the concept of sustainability should be selected. Sustainability should guide the development of quality-of-life indicators that focus on the well-being ecosystem in addition to human well-being. If the community has major concerns with class strife, income disparity, gender discrimination, and racial tension, then perhaps the social justice concept may be most opportune. Finally, if the community is equally concerned about basic issues such as food and shelter for the poor as well as culture and arts, then perhaps the human need satisfaction concept is most suitable.

Bottom-Up Approach to Selecting Quality-of-Life Dimensions The second approach to selecting quality-of-life dimensions is the bottom-up approach. This approach is sometimes referred to in the indicators project literature as “community visioning.” It is based on the idea that the concept of community quality of life must be defined by the community citizens. One should ask the community residents what a good community is, what quality of life in a community means to them. For example, in 1992, 150 residents of the City of Pasadena (California, USA) representing the various city neighborhoods were invited to a meeting by the Pasadena Public Health department to determine Pasadena’s public health objectives. This meeting resulted in a selecting a set of indicators perceived as important to those community residents. These indicators were combined in a composite called the Quality of Life Index that addresses public health through education, housing, recreation, arts and culture, and open spaces (Smolko et al., 2006). Note that in this case, the process of selecting the quality-of-life dimensions was not guided by a theoretical notion (i.e., top-down) but through a process in

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which community residents express their vision of what quality of life in their community should be (i.e., bottom-up). There are many methods used guided by the bottom-up approach such as focus groups, using key informants, conducting a social survey, arranging town-hall meetings, envisioning through committee meetings, delegating this task to a technical team, among others. See Boxes 5.2–5.5 for examples. Box 5.2 Engaging Community Residents Through a Social Survey Social surveys conducted in the context of a community can provide a clear voice and add substance to an indicators project. Conducting a social survey allows community residents to be more involved in community development. The survey serves to incorporate their perspectives, concerns and ideas. In the case of Vancouver Vital Signs indicators project, engaging residents was evident from the survey data and community conversations—a bottom-up approach to indicator selection. A draft of the survey was created with input from an advisory committee of community stakeholders, which was then shared with all representatives of the sponsoring groups for their suggestions and revisions. A half-day session was held with 50 participants. As a result, the survey became more reflective of the province, and it served to enhance the meaningfulness of the findings. When the report was released in 2016, the overseer of the Vital Signs project used the survey data and the community conversations to shed more light of the study findings that were based on the objective data. To reiterate, the survey data served as a community resource. Residents were able to download the survey data from the website and continue the community conversation. As such, the Vital Signs survey results have helped better understand the Vancouver community and through this understanding new priority areas emerged. Source: Adapted from Ridzi and Prior (2020)

Box 5.3 Engaging the Community Through Meetings The Ohio Sustainable Communities Program has used the bottom-up approach to indicator selection by engaging community residents through community meetings. Community meetings took two forms. Sessions were held where residents normally gather. Participants at these sessions were asked to answer two questions: (1) What they value most about the community today and want future generations to have available; and (2) What they hope their grandchildren and great-grandchildren will have that is not here today. Answers to the first question were considered “community treasures” and “community rainbow” to the second question. Examples of “treasures” include “We have reasonable property and rental rates”; “We have tow good school (continued)

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Box 5.3 (continued) systems”; “There is plenty of opportunities for hunting and fishing.” Example of “rainbows” include “Development is on a modest scale; not overwhelmed by commercialism”; “University housing is less invasive into neighborhoods”; and “There are opportunities for life-long learning.” What emerged from these sessions was a clear consensus of what makes quality of life in their community. These sessions took place in the context of three locations: Noble County, Kent City, and Carroll County. In all three communities, a consensus emerged capturing their community “ethos.” Specifically, in Noble County quality of life meant open spaces, wildlife and natural surroundings, small towns with small locally owned retail businesses, close knit communities, locally controlled schools, and small family farms. In Carroll County quality of life meant agriculture of key to the local economy, small manufacturing firms, and managed residential growth. For the City of Kent, quality of life meant diversity of residents, new ideas, connection with the university (Kent State), a vibrant downtown with locally owned business centering around entertainment and arts, well-maintained neighborhoods, a pedestrianoriented community, and preservation of environmentally sensitive areas. Source: Adapted from Grunkemeyer and Moss (2004)

Box 5.4 Envisioning Through Committee Meetings The Truckee Meadows Tomorrow (TMT) indicators project gathered leaders from a diverse group of stakeholders: business and economic organizations, labor, ethnic, environmental, human services, religious, youth, health, arts, government, disabled and progressive organizations. The TMT members formed nine committees to brainstorm potential indicators to monitor quality of life in the following areas: arts, economy, education, environment, government, health, human services, land use, housing, transportation, and public safety. Members of each committee were asked “What is important for your quality of life at the community level?” And “How can the community monitor its progress on the things that matter most?” Through this process, the nine TMT committees developed over 300 potential indicators to track community progress. Source: Adapted from Barsell and Maser (2004)

Box 5.5 The Technical Team: The Quality of Life in New Zealand’s Largest Cities Indicators Project A project technical team, comprised of research and policy representatives from the eight largest cities in New Zealand (Auckland, Waitakere, North (continued)

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Box 5.5 (continued) Shore, Manukau, Hamilton, Wellington, Christchurch, and Dunedin) was delegated the responsibility for the indicators project indicator selection. The technical team members were allocated a domain or sector category (e.g., health, housing) and undertook analysis of the indicators in that domain across all eight cities. The following 11 domains were selected by the technical team to provide a picture of quality of life in New Zealand cities: People: Dimensions in this domain highlight key demographics about New Zealand city residents (e.g., population growth, ethnicity, age structure, and households and families). Knowledge and skills: Dimensions in this domain provide an overview of the state of educational participation and achievement in New Zealand’s largest cities (e.g., participation in early childhood education, school decile ratings, school suspensions and stand downs qualification levels, participation in community education). Economic standard of living: Dimensions in this domain examine aspects of the economy that impact at the personal and household level (e.g., income, cost of living, household expenditure, social and material deprivation). Economic development: Dimensions in this domain explore aspects of the broader economy and its impact on New Zealand cities (e.g., economic growth, employment, business growth, retail sales, building permits, tourism). Housing: Dimensions in this domain identify issues related to general housing trends and crises in New Zealand cities and how these issues impact communities in terms of health, education, and community cohesion (e.g., housing tenure, housing costs and affordability, household crowding, government housing provision, urban housing intensification). Health: Dimensions in this domain focus on a holistic view of health and address aspects of physical and mental wellbeing (e.g., life expectancy, low birth weights, infant mortality, teen parenthood, diseases, access to general practitioners, mental health and emotional wellbeing, disability, self-reported health status, modifiable health risk factors). Safety: Dimensions in this domain cover perceptions of safety, aspects of physical safety for key population groups and general law and order issues (e.g., child safety, perceptions of safety, road casualties, crime levels). Natural environment: Dimensions in this domain examine aspects of the natural environment that are significant to city living (e.g., solid waste management and recycling, biodiversity, air quality, beach and stream/lake water quality, drinking water quality). Social connectedness: Dimensions highlight how people develop and maintain positive relationships with others (e.g., overall quality of life, diversity, local community strength and spirit, electronic communication). (continued)

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Box 5.5 (continued) Civil and political rights: Dimensions in this domain examine the effectiveness of civil and political systems that are important to policy making in cities (e.g., Treaty of Waitangi and local authority relationships with Maori, community involvement in council decision making, voter turnout, representation on local decision-making bodies). Source: Adapted from Jamieson (2004)

Selecting the Quality-of-Life Indicators Indicators (i.e., operational measures such % teen pregnancies for each community quality-of-life dimension (e.g., economic well-being, consumer well-being, health well-being, social well-being, environmental well-being) must be identified. Remember the discussion we had in the previous section concerning the top-down versus bottom-up approach in selecting quality-of-life dimensions. Let us stick with this distinction to further discuss selection of quality-of-life indicators. Quality-oflife dimensions are the abstract form of indicators. For example, safe and clean water is a quality-of-life dimension whereas indicators related to this dimension may include indicators such as (a) percent of individual households served by public water and sewer, (b) number of inter- and intra-county connections between water systems, (c) percentage of individual household systems meeting current clean water standards, (d) the quality of local streams measured by e-coli and other pathogens (Grunkemeyer & Moss, 2004, p. 46).

Top-Down Approach to Selecting Quality-of-Life Indicators To reiterate, the essence of the top-down process is the method of selecting the dimensions guided by a theoretical concept of community quality of life. In Chap. 2 we described five theoretical concepts of community quality of life: personal utility, opulence, just society, human need satisfaction, and sustainability. We also described how these theoretical concepts guide community development researchers articulate a set of dimensions that reflect the core meaning of the selected theoretical concept. For example, the sustainability concept would guide the indicators team to focus on two sets of indicators: one set capturing ecosystem wellbeing, the other human wellbeing. The quality of life dimensions that capture eco-system wellbeing include land (i.e., diversity and quality of forests, farmland and other land ecosystems, including their modification, conversion, and degradation), water (i.e., diversity and quality of inland water and marine ecosystems, including their modification by dams and other structures, pollution and water withdrawal), air (i.e., local and

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indoor air quality), and resource use (i.e., energy and materials, waste generation and disposal, recycling). In contrast, the quality-of-life dimensions capturing human wellbeing include health and population (i.e., physical and mental health, disease, mortality, fertility, population change), wealth (i.e., income, poverty, inflation, employment, infrastructure, basic needs for food, water, and shelter), knowledge and culture (i.e., education, communication), community (i.e., institutions, law, crime, racial and ethnic strife), and equity (i.e., distribution of benefits and burdens between social groups). As such, the challenge in using the top-down approach is to brainstorm with members of the steering committee to identify a theoretical concept that best capture the core values and beliefs of the majority of the community residents and leaders. Which of the theoretical concepts best capture community values and beliefs? If a consensus emerges from the brainstorming session about a specific theoretical concept (e.g., sustainability) then the quality-of-life dimensions can be easily deduced. The reader should refer back to the discussion of the theoretical concepts of community quality of life in Chap. 2. This discussion should aid in the development of a list of quality-of-life dimensions and indicators. If the brainstorming session does not produce a consensus pinning down a specific theoretical concept, then the bottom-up approach should be adopted.

Bottom-Up Approach to Selecting Quality-of-Life Indicators As previously described this approach involves a process in which specific qualityof-life dimensions are developed as a function of eliciting the views and opinions of community residents and/or representative of important stakeholder groups in the community. We discussed this process in some detail in the previous section of this chapter. The bottom-up approach also compels indicators researchers to rely on members of the steering committee to develop a list of indicators once the dimensions are clearly envisioned. This process involves several steps: (1) identifying performance indicators of sponsoring organizations, (2) eliminating indicators lacking available data and statistics, and (3) further eliminating indicators that do not meet standard criteria.

Identifying Performance Indicators of Sponsoring Organizations The goal here is to use available performance indicators and statistics of sponsoring organizations. Let us suppose that one of the sponsoring organizations that have a representative on the steering committee is the county bureau of tourism. The performance of such an organization is evaluated periodically (perhaps annually) using indicators such as number of visitors to the bureau’s website, number of business listed on the bureau’s website, number of website visitors, number of local businesses and agencies who provide financial support to the bureau, dollar

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amount of that support, dollar amount of outside support (through grants), number of people requesting visitor’s guide, number of jobs related to hospitality and tourism, and amount of state and local taxes generated from hospitality and tourism organizations. See Box 5.6 for an example of how this was implemented by the Noble County Sustainable Communities Program. Box 5.6 An Initial Process of Indicators Selection Noble County Sustainable Communities Program, developed a list of indicators guided by a bottom-up approach. The research staff identified specific organizations represented by members of the steering committee (e.g., Child Resource Center, County Commissioners, Soil and Water Conservation District, Local School Districts, Noble Economic Development Office), and each member was asked to develop a list of indicators capturing their own performance indicators. Specifically, the Child Resource Center revealed that the performance of their organization had been evaluated using the following indicators: number of quality caregivers, accreditation training, after school programs, on-site technical assistance, and early care participation. The County Commissioners’ performance indicators included change in general fund revenues, public service expansion, restoration of covered bridges, park development, funding for low income, receiving grant funds, and passing levies). Performance indicators of the Soil and Water Conservation District included levy support, increased soil fertility, youth attendance at education sessions, classroom presentations, number of persons using services, number of hits on PRMS website, funds obtained for landowners (cost share program), and miles of streams cleaned. The performance indicators of Local School Districts included proficiency rates, graduation rates, attendance rates, continuous improvement numbers, support services, ratio of students, number of volunteers, student grades, retention rates, class offerings, post-secondary success, scholarships received, staff retention, levy support, and discipline reports. The performance indicators of the Noble Economic Development Office included number of jobs, wages generated, available work force, use of incentive programs, graduation rates, test scores, and graduates returning to community. Source: Grunkemeyer and Moss (2004, pp. 40–43) At this point we have to recognize that a long list of potential indicators (mostly objective indicators with secondary data) may have been generated. See the long list of community indicators generated by the Quality of Life Task Force of the Truckee Meadows Tomorrow indicators project. These are mostly objective indicators that involve secondary data (see Box 5.7). As such it becomes imperative to prioritize

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these indicators and force a selection of a manageable set. How can we do this? We’ll address this issue in the next several sections. Box 5.7 List of Potential Objective Indicators (Secondary Data) Developed by Truckee Meadows Tomorrow Indicators Project Arts and culture: – – – – – – – –

Number of arts events offered Arts programs in the schools Venues for arts events Attendance at arts events Economic contribution of the arts Quality of the arts Quantity of arts organizations Incubation of new arts organizations especially ethnic ones

Economy and economic vitality: – – – – – – – – – – – – – – – – – – – –

Cost of living Livable wage Employment by industry Percent of full time versus part time jobs Percent of jobs that offer benefits Unemployment rate/employment rate Percent of workforce with permanent work disability Tourism visits Tourism diversification: heritage/cultural tourism, recreational tourism Room nights Room taxes Average length of stay Average amount spent during stay Litter index Number of historic, cultural, architectural and archeological sites identified and protected Airport passengers Airport flights Value of cargo shipped Average weekly wage by industry Poverty level

Education and life-long learning: – Dropout rate (middle, high schools) – Attrition rate (college) – Completion rate (high school, college) (continued)

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Box 5.7 (continued) – Teacher starting and average salaries and benefits – Per pupil spending – Students participating in vocational and occupational education – Percent of employees getting employment training each year Percent of students who go to college after high school – Number of people participating in apprenticeship program – Percent of student completing formal training after high school: vocational, community college, university – Number of arts programs provided in grade school – Arts curriculum—age appropriate and integrated into regular curriculum – Students participating in elective course in art and music – Library performance compared to national measures: per capita support for libraries – Library materials budget as percent of the total budget – Library material expenditures per capita – Library holdings – Library visits per capita – Internet access at the library – Percent of curriculum devoted to health education, PE, and recreation K-12 – Parental involvement measured by attendance at teacher conferences – Number of hours volunteered at child’s school—for parents and community volunteers – Number of schools with Partners-in-Education – Parental involvement as measured by survey of involvement as defined by parents – Safety in schools (violence to students) – Safety in schools (violence to faculty) – Student-teacher ratios – Percent of teachers teaching in their field of study – School enrollment in full-time equivalents – School capacity compared to enrollment: how many schools are over capacity? – Terra Nova test score studies – Longitudinal test score studies – High school proficiency exam; percent of students who pass – SAT’s – ACT’s and other standardized test Environment: – Pollution Standard Index for ozone, carbon monoxide, and particulate matter (fine dust) (continued)

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Box 5.7 (continued) – Hazardous or toxic spills – Toxic Releases Inventory – Visibility – Tons of waste recycled/landfilled – Acres of open space – Significant hydrologic resources protected or lost (wetlands) – Homes, businesses, and structures located in the 100-year flood plain – Acres of land available for public use in the Truckee River Corridor – Audubon Christmas bird count – Wildlife habitat acres preserved and protected – Acres of hillside areas and miles of ridge lines protected from development – Acres of community, regional, neighborhood, and linear parks per 1000 population – Maintenance person months per acre park – Miles of regional trails, bike paths—distinguish type of trail designation: pedestrian, bike, equestrian, motorized, off-road – Park and trail use – Facilities used as community centers per 1000 population – Recreation facilities per capita including golf courses, tennis courts, pools, soccer fields, baseball diamonds – Water quality in the Truckee River including nutrients, dissolved solids, dissolved oxygen, temperature, nitrogen and phosphorus standards – Water quality in community water wells – In-stream flows in the Truckee River – Tons of solid waste recycled, put in landfills – Litter index – Number of boil orders issued last year – Water use per capita – Overall regional wastewater gallons per capita per day – Frequency of once weekly watering restrictions Government: – – – –

Percent of population over 18 who are registered to vote Percent of population over 18 who actually vote Hours people spent volunteering Membership in social, fraternal and charitable organizations

Health and welfare: – Suicide rate – Homicide rate – Death per 1000 from heart disease (continued)

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Box 5.7 (continued) – Industrial accidents – Years lost due to premature death – Infant mortality – Low birth weight babies – Cost of health care – Access to health care as measured by percent of people with/without health insurance – Emergency Room patients without primary care doctors (or who could be seen elsewhere) – Persons on a waiting list to be seen for treatment at public health care facilities – Primary care doctors (family and general practice) – Pediatricians – Children’s hospitals – Health care public assistance – Immunization for 2-year olds – Immunization for seniors – Access to quality medical care (number of doctors taking new patients) – Number of specialists per capita – Access to quality mental health care – Access to quality care for the handicapped – Physical access in the community – Prenatal care in the first trimester – Prenatal care for high-risk pregnancies – Prevention and early treatment: mammogram – Prevention and early treatment: pap smear – Prevention and early treatment: prostate cancer screening – Prevention and early treatment: HIV-AIDS – At risk behavior: smoking – At risk behavior: drinking – At risk behavior: seat belt use – At risk behavior: sedentary lifestyle/overweight – At risk behavior: youth drinking, smoking, substance abuse – At risk behavior: teen pregnancy – Cause of death by age – Teen access to health care Human services: – Child abuse reported and substantiated – Children in poverty – Juvenile violent crime (continued)

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Box 5.7 (continued) – Gang participation, crimes – Domestic violence reports – Child abuse – Senior abuse – Number of deaths caused by abuse – Senior abuse reported and substantiated – Senior housing – Senior health specialists – Long-term health care facilities – Senior immunizations – Length of average stay on welfare – Vacant child care slots, number of children on waiting list for child care – Families living in poverty Land Use, Housing and Transportation: – – – – – – – – – – – – – – – – –

Average home price Average rent Housing affordability index Ratio of cost of a home a median income family can afford and median sales price Number of households below the median income spending 30% or more of their income on rent and utilities Percent of homeowners Citifare and Citilift ridership Transportation mode split Public transit coverage and service hours, by service area Miles of road not attaining level of service standards/congestion Pavement condition index Average community time Average speed on the freeways Vehicles Miles Traveled Estimated per capita energy consumption by source: electric, gasoline Estimated per unit energy consumption in commercial, industrial, and transportation sectors Percent of energy from renewable sources being used in the region

Public safety: – – – –

Neighborhood Watch programs Uniform Crime Index Report Hate crimes Homicide rate (continued)

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Box 5.7 (continued) – Police response time – Fire response time – Ambulance response time Source: Adapted from Barsell and Maser (2004, pp. 56–60)

Eliminating Indicators Lacking Available Data and Statistics An important method of narrowing the pool of indicators to a manageable set is to check the availability of data and statistics related to the performance indicators of those organizational entities that focus on specific dimensions of quality of life in the community at large. As previously mentioned, task forces (or committees) are charged to oversee indicators related to a specific quality of life dimension. As such, each task force should examine the selected indicators related to their own dimension and investigate the availability of pertinent statistics from a variety of sources, especially government sources. For example, if the task force is in charge of health well-being, then the starting point is to identify all the pertinent statistical sources (e.g., health departments at national, regional, state, county, city, and town) and see what indicators they have that can be borrowed. This should be the first step in narrowing the indicators to a manageable set.

Further Eliminating Indicators that Do Not Meet Standard Criteria At this point, the steering committee is likely to be exposed to a bunch of indicators brought to their attention by the various task forces. Remember each task force concentrates on one major quality-of-life dimension (or sub-dimension). The task force assembles many indicators and submits them to the steering committee for further consideration. The question arises concerning how the steering committee judges whether the submitted indicators are good or bad. The steering committee must establish criteria for the selection of indicators. This is the next step. There are two major criteria commonly used in the selection of indicators. These are face validity and policy relevance. With respect to the first criterion, we typically ask: Does the indicator measure a factor that is directly related to the selected quality-of-life dimension (or sub-dimension)? Research methodologists talk about face validity--does the indicator reflect the conceptual domain of the construct? This translates into the idea that the indicator must reflect the spirit of the conceptual dimension it represents. For example, the conceptual dimension is economic well-being, which in turn is broken down into several sub-dimensions such as income, cost of living, unemployment, and job quality. Let’s focus on income. How is income measured based on the available statistics? Is it measured by median household income as reported by census data? If so, then the indicator has

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face validity. But then let’s look at another measure of income such as income reputation. This measure involves a self-report of a sample of community residents who respond to a survey question: “What do you believe is the annual income of the average household in this community?” Some social science researchers would take issue with the face validity of such a measure. They would criticize the notion that residents are familiar with what others in the community make in terms of income, and even so whether such familiarity is accurate of reality. The second key criterion speaks to policy relevance. Indicators should have policy relevance. In other words, one needs to ask whether the indicator has relevance for public policy decisions. If not, it’s not a good indicator. For example, it may be interesting to know what the average climate temperature is during the summer months, but is this information useful? Can community leaders make policy decisions to affect the weather? The answer is clearly no. Therefore, the average climate temperature during the summer should not be considered as a good indicator. Remember the primary objective of any indicators project is to help community leaders make policy and program decisions to enhance the quality of life of community residents. Indicators should be selected with this goal in mind. There are other criteria commonly used in selecting indicators besides face validity and policy relevance, but they are secondary in importance compared to face validity and policy relevance (Grunkemeyer & Moss, 2004). These include: • Construct validity: Does the indicator have sufficient scientific evidence of construct validity as established in the scientific literature? (see Box 5.8) • Consistency and reliability: Are the data collected consistently using the same indicators over time? • Source credibility: Are the data collected by reputable sources? • Geographic comparability: Can the data be compared to other comparable communities or geographic regions, and are there similar data for these comparable geographic units? • Comprehension and excitement: Are these indicators easy to understand by lay people and the media, and do they generate excitement especially with the media? • Accessibility and affordability: Are the data easily accessible year after year and can we obtain the data for no or little cost? • Reflective of Social Issues: Do these indicators reflect underlying social issues in the community? (see Box 5.9) • Leading indicators: Are these indicators lagging? If so, leading indicators are better than lagging indicators? (see Box 5.10). Box 5.8 Community Indicators Supported by Academic Research The Thriving Cities Indicator Explorer (https://explore.thrivingcities.com/ indicators) has categorized community indicators into five categories reflecting the degree of construct validity: (1) “Very Strong Academic (continued)

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Box 5.8 (continued) Support,” (2) “Strong Academic Support,” (3) “Promising Academic Support,” (4) “Slight Academic Support,” and (5) “Inconclusive Academic Support.” Examples of indicators with “Very Strong Academic Support” include educational attainment, high school graduation rate, commute by public transport, greenhouse gas, water quality index, waste disposal, preschool enrollment, air quality index, food desert, ridership, adult obesity, kindergarten readiness, SAT/ACT Scores, dropout rate, and child poverty. Examples of indicators with “Strong Academic Support” include property crime rate, violent crime rate, child abuse reported, child abuse substantiated, crime rate, electricity use, food insecurity in children, and renewable power generation. Examples of indicators with “Promising Academic Support” include auto thefts, volunteering, burglary rate, commute time, domestic violence, juvenile arrest, neighborhood safety, education spending, 10th grade proficiencies, college readiness, and student-teacher ratio. Examples of indicators with “Slight Academic Support” include nonviolent crime rate, pedestrian infrastructure, teen birth rate, attend college, police officers, rape rate, safe walking at night, teachers, and distance to park. Examples of indicators with “Inconclusive Academic Support” include housing units, residential building permits, road conditions, traffic accident fatalities/injuries, higher education enrollment, public schools, and workforce training. Source: Adapted from Thriving Cities Indicator Explorer (https://explore. thrivingcities.com/indicators)

Box 5.9 Identifying Community Indicators that Signal Social Issues Consider the case of the Baltimore Neighborhood Indicators Alliance (BNIA) project (https://bniajfi.org/). Through a community survey the BNIA found that community residents desire on-street events. On-street events require street permits to close a street for a block party. As such, permit data were used to identify areas that are popular in organizing on-street events. Another indicator that reflect an important social issue is growth/decline of population in specific neighborhoods. Population growth in a neighborhood signals issues related to parking, rising housing prices, crime, and gentrification. In contrast, population decline signals vacant and abandoned properties, poverty, and violent crime. Neighborhood with declining populations are also likely to lose retail stores, banks, and schools. Source: Adapted from Steven et al. (2021, p. 32)

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Box 5.10 Lagging Versus Leading Indicators in Indicators Selection The CNYVitals indicators project of the Central New York Community Foundation ran into a problem of lagging indicators prompting the community indicators researchers to take corrective action. The indicator in question was “adult literacy.” It was deemed “lagging” given the fact that they relied on census data which are updated every 10 years. Thus, the census data were not particularly sensitive nor timely. The corrective action was to use “meso-level indicators” considered as “leading indicators.” These include: “families that read to kids” and “Kindergarten readiness.” These data were available in a timely fashion and considered to be predictive of adult literacy. Source: Adapted from Steven et al. (2021, p. 30) Please note that these criteria for indicators selection are what we believe to be optimal. That is not to say that there are other criteria that can be used that may or may not be optimal. Such as criterion is to solicit community residents to rate the importance of a pool of indicators. For example, the steering committee of the Truckee Meadows Tomorrow indicators project conducted a “demographically representative” survey by mailing a survey questionnaire to a sample of 5000 households (randomly selected from the county assessor’s files). Respondents were asked to rate the importance of each indicator (Barsell & Maser, 2004). Such a method is not recommended because in this instance we’re assuming that respondents’ importance ratings are based on a good understanding of the social issues of the community. Only a small segment of the community may be in that position (i.e., to rate those indicators based a good understanding of the various issues. Other methods may include an advertisement in the local newspaper soliciting residents’ opinions about the importance of a selected set of indicators (Barsell & Maser, 2004). Again. The effectiveness of these methods are questionable.

Putting Things Together (Secondary Data) The indicators we have discussed so far are essentially indicators based on secondary data. Secondary data involve hard data collected by various agencies, mostly government agencies. For example, an example of an objective indicator is % of people who have been vaccinated (e.g., COVID-19 vaccine). This statistic may be based on the public health officials counting people who have actually been

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documented to have received the vaccine based on their visiting a clinic with medical personnel having documented the vaccination event. Thus, vaccination data are statistics collected by the local health department. The next step is to consult with local professionals. Professionals that specialize in the indicators in question should be invited to speak at a meeting of the task force. Alternatively, a member of the task force may agree to contact professionals for their ideas and make a report back to the task force. Some members of the task force may have the expertise. Remember, we talked about recruiting members of a particular task force that have expertise in the quality-of-life dimension and indicators in question. Caveat: Many professionals specializing in certain indicators are likely to make a strong attempt to persuade the task force to adopt their set of indicators. The task force as a whole has to remain impartial and select those indicators based on merit alone. At this stage, each task force that have focused on specific dimensions of quality of life will have to present their selected indicators to the steering committee. However, before the task force formally presents its findings to the steering committee, it reviews all the indicators to make sure that the selected indicators are covering the entire domain of the quality-of-life dimension (or sub-dimension) in question. For example, suppose the task force is focusing on economic well-being. The task force identified several indicators pertaining to income, unemployment, cost of living, but failed to identify suitable indicators for job quality. At this stage, the task force should note this gap and may recommend primary data based on survey, a topic which we will turn to in the next section. That is, because secondary data are not available for job quality, then the task force recommends to the steering committee to collect data about job quality using a survey of local residents. This entails primary data collection, a topic we will address in some detail in a subsequent section. See Boxes 5.6 and 5.10 for a long list of community indicators (objective and subjective) developed by the Truckee Meadows Tomorrow indicators project. In preparing the list of indicators for presentation to the steering committee, each task force should answer the following question: how many indicators should we have? The answer to this question entails considering the tradeoff between comprehensiveness and cost. There is a high cost to selecting many indicators— cost of data collection, tracking, reporting, etc. Yet too few indicators may fail to provide a representative picture of the quality-of-life dimension (or sub-dimension). In many cases, we find ourselves motivated to expand the number of indicators, hoping that the various biases and caveats that are inherent in these indicators will balance one another. As an example, the steering committee of the JCCI indicators project agreed to limit indicators to a maximum of 10 for each quality-of-life dimension (Chambers & Swain, 2006).

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Considering Subjective Indicators (Primary Data) Primary data collection typically is done through community surveys. The data collection technique could be a telephone survey, a mail survey, e-mail/Internet survey, or a door-to-door survey. A telephone survey is typically conducted by randomly calling residents in the community until some a quota is achieved (e.g., sample of 500 in a city of approximately a million) within the designated geographic boundary. If the geographic boundary is further divided by neighborhoods, then a stratified sample is used to ensure a quota of certain number of respondents in each neighborhood. In other words, people are identified in certain neighborhoods and a sample of each neighborhood population is selected based on the size the neighborhood (i.e., larger neighborhoods have proportionally larger sample sizes). A mail survey is another commonly used data collection technique. Again, a sampling frame (list of people) of the community population within the designated geographic boundary is developed (e.g., through tax or voter registration records), and a sample of these residents are mailed the survey questionnaire. Typically, the cover letter of the mail questionnaire is written by a prominent community leader (e.g., city mayor) urging prospective respondents to complete the survey. Similarly, if the community is divided into neighborhoods, then the sample is stratified by neighborhood to achieve a minimum number of respondents within each neighborhood. E-mail/ Internet-based surveys involve sending out e-mail messages to a large sample of community residents randomly selected. The e-mail message informs them about the community survey and attempts to recruit them as respondents for the study. Embedded in the e-mail message is a link to a web-based survey. A web-based survey is a survey taken on the web and scored automatically and the data is downloaded into a data file for further processing. Door-to-door data collection entails cluster sampling. That is, the community is divided into neighborhoods, and within each neighborhood an interviewer selects certain homes randomly, knocks on each door and requests that an adult member of the household complete the survey questionnaire (either on the spot or makes arrangement to pick up the completed questionnaire after a few days). This is done until the specified quota for each neighborhood is met. Typically, the telephone interview survey generates a high response rate and is least costly. One can further reduce the cost of the telephone survey by recruiting community volunteers to conduct the telephone interviews. The door-to-door survey method also generates the highest response rate but is the costliest. The cost of this method can significantly be reduced if the people handing out (and collecting) the survey questionnaires are volunteers (non-paid staff). The mail survey method generates the lowest response rate and is somewhat costly. One can decrease the cost of the mail survey by request local printers to print the questionnaire pro-bono (as a service to the community). A volunteer staff can be assembled to stuff the envelopes and print and paste mailing labels, etc. This can significantly reduce the cost of the mail survey. We will discuss issues dealing with data collection (including data collection through social surveys) in some detail in the next chapter.

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We consider subjective indicators for several reasons: identifying social issues, complementarity, validation, and other factors such as timeliness, and granularity to local geographies.

Identifying Important Social Issues and Complementarity Social surveys of community residents are commonly used to identify social issues. This exercise helps the steering committee focus on objective indicators highlighted by community residents as important to the quality of life of the community at large. These surveys are usually administered locally focusing on asking residents to rate each issue area as well as identify priorities that should be addressed (Ridzi & Prior, 2020). That is, a major reason for considering subjective indicators is to provide primary data for indicators that we consider important and for which no other secondary data are available. This lack of secondary data (as well as missing data) is ubiquitous for many indicators projects (Patten & Lyons, 2009). Consider the Vancouver’s Vital Signs indicators project. Initially, the 2006 project relied heavily on secondary data; however, recent data were not available (Ridzi & Prior, 2020). See Box 5.11. Box 5.11 Secondary Data? Not Good Enough Many community indicators researchers feel much frustration concerning publicly available data sources (i.e., data related to objective indicators). This was particularly evident in the context of Syracuse indicators project. The Syracuse researchers were able to identify where they were doing worse than other communities but failed to understand the specific issues in the community at a more granular level. As a result, those who were overseeing the indicators project realized that they had to invest in a more sophisticated community data infrastructure through subjective indicators. Otherwise, they felt that much of the information derived from objective indicators were constrained by a “black box.” That is, they couldn’t understand the objective trends without having the Syracuse residents evaluate the trends through a social survey. Source: Adapted from Ridzi and Prior (2020) Many community indicators projects have capitalized on the availability of national data sets, many indicators projects have also elected to complement existing objective data with survey data capturing residents’ perception of community quality of life. For example, a majority of residents may view as rampant development as not sustainable. However, the project team may discover that they do not have objective indicators and statistics from secondary sources that capture “rampant development.” Box 5.12 shows the rationale that the New Zealand indicators project team

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used to develop and justify certain measures in the 2002 New Zealand Quality-ofLife Survey (Jamieson, 2004). Box 5.12 Rationale for Selected Measures in the 2002 New Zealand Quality-of-Life Survey Access to general practitioners (measured by % of people who do not visit a doctor despite wanting to and reasons). General practitioners (GPs) are part of the front line of health care provision. Understanding the barriers to accessing GPs is an important factor in the treatment and prevention of poor health. Mental health and emotional wellbeing (measured by % of people who report feeling calm and peaceful; % of people who report feeling happy; % of people who report feeling negative effects of stress; % of people who say they have someone to turn to for support in times of stress). Mental health is more than the absence of diagnosed mental illness. In the New Zealand context, it has been broadened to include psychological health and emotional wellbeing. People who report that they are generally calm, peaceful, and happy are more likely to be emotionally healthy. Modern urban living can create stress points in people’s lives that impact on their general wellbeing. Availability of support structures in times of emotional need are more likely to lesson the negative impacts of emotional problems. Self-reported health status (measured by rating of self-reported health status; rating of degree of self-reported lifestyle healthiness). Self-reported health is identified as an international measure of health status in communities. People who report that they lead healthy lifestyles are more likely to be physically and mentally healthy. These subjective measures complement findings from objective health outcome measures. Modifiable health risk factors (measured by % of people who take part in physical activities that increase their heart rate or breathing for 30 min or more by frequency; % of people who participate in sports clubs, in teams and organized groups; level of perceived barriers to participation in leisure and recreation activities). Physical inactivity has been identified by the World Health Organization as one of the biggest contributors to the global burden of disease. People’s ability to participate in sports, leisure and recreation activities are influenced by factors such as state of health, income, competing demands on time, availability of recreation opportunities and facilities, etc. Child safety (measured by % of people who perceive their local neighborhood as a safe or unsafe place for children to play unsupervised). Protecting the physical and psychological health of children is critical for improving quality of life. The protection and nurture of children is a shared responsibility of families and the communities they live in. Perceptions of neighborhoods as (continued)

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Box 5.12 (continued) safe places for children to play provide insight into aspects of local community safety. Perceptions of safety (measured by % of people who rate their home, neighborhood and city center as safe or unsafe during the day and after dark). Perceptions of safety have an important bearing on people’s ability and willingness to participate in community life. If people feel unsafe, they are likely to be less trusting and less willing to engage with others, particularly those they perceive as different to themselves. City issues such as vandalism, car theft and displays of anti-social behavior contribute to fear of crime. These subjective measures complement findings of the objective facts about law and order in cities. Look and feel of the city (measured by % of people who have a sense of pride in the way their city looks and feels and reasons). Ideally, residents of a city would feel a sense of pride and enjoyment about the area in which they live. This measure acts as a barometer of how people feel about various aspects that comprise the built environment and their city’s livability. Graffiti (measured by % of people who perceive graffiti as a problem in their neighborhood). The presence of graffiti has a strong impact on the way people feel about where they live. It is perceived as an act of vandalism and can contribute to people feeling unsafe in their neighborhood. Noise pollution (measured by % of people who rate noise pollution as a problem in their neighborhood). While the emission of noise is an intrinsic part of everyday activities in cities, noise can affect the way people feel about the area in which they live. Excessive noise is recognized as a potential health and environmental hazard. Public transportation (measured by % of people who rate public transportation as affordable, safe, and convenient). Public transportation systems provide links between different locations in the built environment, connecting residents with the city’s services and activities. For many people, reliance on public transportation is critical for effective participation in community life. An affordable, safe, and convenient public transportation system can stimulate a reduction in the use of motor vehicles and reduce traffic congestion. Air quality (measured by % of people who rate air pollution as a problem in their neighborhood). Good air quality is essential for human health and the health of the natural environment. Perception of sir pollution is a subjective measure that complements objective measures of air quality. Beach and stream/lake water quality (measured by % or people who rate water pollution as a problem in their neighborhood). Clean beach and stream/ lake water is essential for safe waterways. Water quality also shows the impact of human activity on beaches and natural waterways. Perception of water (continued)

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Box 5.12 (continued) pollution is a subjective measure that complements objective measures of water quality. Overall quality of life (measured by % of people who rate their quality of life as extremely good through extremely poor). This measure provides a useful summary of people’s subjective perception of their overall quality of life, which can be compared to more objective quality of life outcome indicators. Diversity (measured by % of people who perceive increased cultural diversity as having a positive or a negative impact on their city and reasons). Cities are becoming home to an increasing number of people with diverse lifestyles and cultural backgrounds. This diversity impacts on how people communicate with each other and how people are made to feel a part of their city. This measure looks at the advantages and disadvantages of growing diversity in cities from the point of view of residents. Local community strength and spirit (measured by % of people who say that the social network or group that matters most to them is made up of people who live in the same local neighborhood or is made up of people who share the same interests but don’t live in their local neighborhood; % of people who agree or disagree that their community works together and people support each other; % of people who agree or disagree that there is a sense of community in their neighborhood; % of people who feel lonely or isolated all the time through to never). Being a part of a social group or network brings positive outcomes for individuals and for society. The presence of formal and informal relationships between people facilitates participation in society, encourages a sense of belonging and enables the development and maintenance of stable communities. Conversely, social isolation is often seen as both a cause and effect of modern urban disadvantage. These measures test residents’ perceptions of social networks and support structures in their cities. Electronic communication (measured by frequency of use of email and the Internet; % of people who use email and the Internet at home, work, training institution, library, internet café, or friend/relative’s place; % of people who rate email and the Internet as being important in their decision making). Communicating with others Is a fundamental requisite in healthy societies. Electronic communication media can facilitate social interaction, overcome mobility barriers to interaction, stimulate learning and aid decision making. These measures identify resident use of electronic communications and their importance in providing information for decision making. Community involvement in council decision making (measured by % of people who agree or disagree that they understand how councils make decisions; % of people who agree or disagree that decisions made by councils are in the best interests of their city; % of people who are satisfied or dissatisfied (continued)

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Box 5.12 (continued) with the level of public involvement in council decision making and reasons; % of people who feel that the public has a large through to no influence on council decision making). The purpose of local government as described in the New Zealand Local Government Act (2002) is to enable democratic local decision making and action by and on behalf of communities. Identification of community outcomes is an important planning function under the Act and the definition of those outcomes must be developed with the community rather than being imposed by local authorities. Local communities must be actively engaged in planning. These measures look at the nature of local authority decision making and consultation processes and whether or not residents feel they are actively engaged in these processes. Voter turnout (measured by main barriers cited by people who did not vote in the 2001 local authority elections). Voting in democratic societies is one of the main means through which citizens can express their political will. It is key to effective governance. This measure looks at the factors which influence people’s voting behavior, particularly those that inhibit voter turnout. Source: Adapted from Jamieson (2004, pp. 89–94)

Validating the Objective Indicators With respect to validation, ordinarily we conduct surveys to validate the objective indicators (compiled from secondary data sources) with subjective indicators (data reflecting residents’ perceptions and evaluations of community conditions and services). The goal is to ensure that the objective reality as revealed by the objective indicators (secondary data) is consistent with the subjective reality of the community residents.

Timeliness There are many instances in which the timing of government release of statistics is not optimal. That is, the indicators project team need to incorporate specific indicators and statistics in their report but are prevented from doing so because those desired indicators and statistics are issued at times that may be out of sync with other indicators.

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Granularity to Local Geographies The reader should note that a major advantage for collecting subjective data using subjective indicators is the fact that most of the objective data available through secondary sources do not drill down to the neighborhood level (Ridzi & Prior, 2020). In other words, if the available indicators and data are available at the county level but the objective of the indicators project is to monitor community wellbeing at the neighborhood level, then conducting surveys with sample respondents of targeted neighborhoods could fill in that gap.

Unavailability of Data in Useable Format There are many instances in which secondary data involving objective indicators are available only in formats that are difficult to use. In those instances, the team may not have a choice but to conduct a survey to capture the missing data in subjective terms. This may be a good way to use subjective indicators as proxy to the objective indicators.

Issues of Public Accessibility Although most of the objective data are gathered by government bodies, some indicators are not accessible to the public. If the indicators project team does not have personnel representing those government agencies, then the team is out of luck (i.e., the data become inaccessible).

Incorporating Local Perceptions in Telling a Story In reporting the study findings, the indicators project team is motivated to illustrate certain findings (e.g., a social trend involving increase in substance abuse) with comments made by local residents. Such comments can be elicited through social surveys (Ridzi & Prior, 2020).

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Community QOL

Domains Subdomains Subjective (primary) and objective (secondary) measures

Fig. 5.1 Community wellbeing and indicator relational framework. Source: Adapted from Sung and Phillips (2016, p. 7)

A Relational Approach to Selecting Quality-of-Life Indicators Sung and Phillips (2018) developed an approach that allows community planners and researchers to bring together a host of objective and subjective indicators into one framework. They coined this framework “Relational.” The framework involves organizing a set of constructs in a pyramid (see Fig. 5.1). The pinnacle of the pyramid is community quality of life. The bases supporting the pinnacle are the domains, sub-domains, and indicators. That is, community quality of life is determined mostly by community domains (economic, human, environmental, social, etc.). These domains are composed of sub-domains, and in turn, are mostly determined by specific experiences within. Objective and subjective indicators are at the bottom of the pyramid. These are the measures of multi-faceted human needs. That is, the indicators project can be guided by individual concerns with each domain as well as community circumstances. As such, the selected indicators can vary on community needs and circumstances (e.g., a distressed community versus a flourishing community).

Summary This chapter focused on decisions related to indicators selection. Specifically, we discussed issues related to how to decide on geographic boundaries and units within, how to select the quality-of-life dimensions, how to select the quality-of-life indicators, and the pros and cons of using subjective indicators. We described the process related to deciding on geographic boundaries and units within. Specifically, the major factors to consider in making these decisions are the

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availability of secondary data, the cost of primary data, and meaningful impact. It is best to collect data at the neighborhood level to allow policy makers to monitor the effectiveness of their programs at that level. Data at larger units may not be meaningful. However, data at the neighborhood level often are not available through secondary sources and entail primary data collection. If so, the cost of primary data collection must be considered in the scope of the indicators project (geographic boundaries) and the units within. The next step in indicators selection involves selecting the quality-of-life dimensions. Two approaches were described: top-down and bottom-up. The top-down approach to selecting the dimensions is essentially theoretical or deductive (concept driven). In contrast the bottom-up approach is practical or inductive (data driven). Regarding the top-down approach, the selection of the quality-of-life dimensions (and possibly sub-dimensions) is based on one or combination of the following theoretical concepts: personal utility, opulence, social justice, human need satisfaction, and sustainability. For example, if a community is besieged with problems of business failures and unemployment, then perhaps the opulence concept should be selected. Opulence motivates indicator researchers to focus on the economic dimension of community quality of life and its sub-dimensions (e.g., unemployment, business failures, quality of jobs, number of jobs lost, and recruitment of new businesses). If a major concern in the community is pollution from industrial development, then the concept of sustainability may be judged as most suitable. Sustainability should guide the development of quality-of-life indicators that focus on the well-being ecosystem in addition to human well-being. If the community has major concerns with class strife, income disparity, gender discrimination, and racial tension, then the social justice concept may be most opportune. Finally, if the community is equally concerned about basic issues such as food and shelter for the poor as well as culture and arts, then the human need satisfaction concept is most suitable. In contrast, the bottom-up approach to selecting the quality-of-life dimensions is based on a vision articulated by the citizens of the community in question. For example, a sample of community residents are surveyed and asked to define their own quality of life in their community. That vision is then used to select the qualityof-life dimensions. After selecting the quality-of-life dimensions, the focus is directed to selecting the quality-of-life indicators. First, there are criteria that are used in selecting the various indicators or measures under each quality-of-life dimension and sub-dimension. Primary criteria are face validity and policy relevance. Face validity refers to the question of whether the indicator reflects the conceptual domain of the construct. Does the indicator have relevance for public policy decisions? If not, it’s not a good indicator. Secondary criteria commonly used in selecting indicators include construct validity, consistency and reliability, source credibility, geographic comparability, comprehension and excitement, accessibility and affordability, reflective of social issues, and leading indicators. The process for selecting indicators involves several steps: Start out with a list of indicators available from statistical sources. Select indicators based on standard criteria. Identify alternative statistical sources and reconcile differences. Consult

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with local professionals. And review and present to the steering committee. Finally, another decision must be made: How many indicators should we have? The answer to this question entails considering the tradeoff between comprehensiveness and cost. There is a high cost to selecting many indicators—cost of data collection, tracking, reporting, etc. Yet too few indicators may fail to give you a representative picture of the quality-of-life dimension (or sub-dimension). Primary data collection typically is done through community surveys. The data collection technique of that survey could be a telephone survey, a mail survey, or a door-to-door survey. Typically, the telephone interview survey generates a high response rate and is least costly. The door-to-door survey method also generates the highest response rate but is the costliest. The mail survey method generates the lowest response rate and is somewhat costly. There are several reasons for considering subjective indicators. These include identifying important social issues, complementing missing objective indicators with subjective ones, validating the objective indicators through perceptions of local residents, when the release of objective indicators and statistics is ill-timed, when the objective indicators and data are not focused enough on smaller geographic units such as neighborhoods, when the data pertaining to the objective indicators are not coded in a user-friendly way, when the use of the secondary indicators and statistics is restricted to government personnel who are not represented on the steering committee of the indicators project, and when the need arises to illustrate certain study findings through perceptions and commentary of local residents.

Progress Check 1. 2. 3. 4.

How to decide on geographic boundaries and units within? How to select the quality-of-life dimensions? How to select the quality-of-life indicators? Should subjective indicators be used?

Progress Check Answers 1. How to decide on geographic boundaries and units within? The major factors to consider in making these decisions are the availability of secondary data, the cost of primary data, and meaningful impact. It is best to collect data at the neighborhood level to allow policy makers to monitor the effectiveness of their programs at that level. Data at larger units may not be meaningful. However, data at the neighborhood level often are not available through secondary sources and entail primary data collection. If so, the cost of primary data collection has to be considered in the scope of the indicators project (geographic boundaries) and the units within.

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2. How to select the quality-of-life dimensions? There are two approaches to the selection of the quality-of-life dimensions (and possibly sub-dimensions): top-down and bottom-up. Top-down is based on the theoretical concept selected to guide the entire indicators project and community concerns. There are at least five theoretical concepts: personal utility, opulence, social justice, human need satisfaction, and sustainability. For example, if a community is besieged with problems of business failures and unemployment, then the opulence concept is judged to be most suitable. Opulence gets indicator researchers to focus on economic development and many dimensions of economic development (e.g., unemployment, business failures, quality of jobs, number of jobs lost, and recruitment of new businesses). If a major concern in the community is pollution from industrial development, then the concept of sustainability is most suitable. Sustainability should guide the development of quality-of-life indicators that focus on the well-being ecosystem in addition to human well-being. If the community has major concerns with class strife, income disparity, gender discrimination, and racial tension, then the social justice concept may be most opportune. If the community is equally concerned about basic issues such as food and shelter for the poor as well as culture and arts, then perhaps the human need satisfaction concept is most suitable. In contrast, the bottom-up approach to selecting the quality of life dimensions is based on a vision articulated by the citizens of the community. For example, a sample of community residents are surveyed and asked to define their own quality of life in their community. That vision is then used to select the quality-of-life dimensions. 3. How to select the quality-of-life indicators? First, there are criteria that are used in selecting the various indicators or measures under each quality-of-life dimension and sub-dimension. These are face validity and policy relevance. Face validity refers to the question of whether the indicator reflects the conceptual domain of the construct. Does the indicator have relevance for public policy decisions? If not, it’s not a good indicator. Secondary criteria commonly used in selecting indicators include construct validity, consistency and reliability, source credibility, geographic comparability, comprehension and excitement, accessibility and affordability, reflective of social issues, and leading indicators. Second, the process for selecting indicators involves several steps: (a) Start out with a list of indicators available from statistical sources. (b) Select indicators based on standard criteria. (c) Identify alternative statistical sources and reconcile differences. (d) Consult with local professionals. And (e) review and present to the steering committee. Finally, another decision must be made: how many indicators should we have? The answer to this question entails considering the tradeoff between comprehensiveness and cost. There is a high cost to selecting many indicators— cost of data collection, tracking, reporting, etc. Yet too few indicators may fail to give you a representative picture of the quality-of-life dimension (or subdimension). 4. Should subjective indicators be used? There are several reasons for considering subjective indicators. These include (a) identifying important social issues, complementing missing objective indicators with subjective ones, (b) validating

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the objective indicators through perceptions of local residents, (c) when the release of objective indicators and statistics is ill-timed, (d) when the objective indicators and data are not focused enough on smaller geographic units such as neighborhoods, (e) when the data pertaining to the objective indicators are not coded in a user-friendly way, (f) when the use of the secondary indicators and statistics is restricted to government personnel who are not represented on the steering committee of the indicators project, and (g) when the need arises to illustrate certain study findings through perceptions and commentary of local residents.

References Barsell, K., & Maser, E. (2004). Taking indicators to the next level: Truckee Meadows Tomorrow launches quality of life compacts. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 53–74). Kluwer Academic. Chambers, M., & Swain, D. (2006). Quality indicators for progress: A guide to community qualityof-life assessments. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 267–322). Springer. Grunkemeyer, W. T., & Moss, M. L. (2004). The sustainable community model approach to the development and use of multi-dimensional quality of life indicators. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 29–52). Kluwer Academic. Jamieson, K. (2004). A collaborative approach to developing and using quality of life indicators in New Zealand’s largest cities. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community qualityof-life indicators: Best cases (pp. 75–109). Kluwer Academic. Jarosz, B., & Williams, M. D. (2004). Creating an index to evaluate a region’s competitiveness. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 183–207). Kluwer Academic. Patten, M., & Lyons, S. (2009). Vital signs: Connecting community needs to community philanthropy in Canada. The Philanthropist, 22, 56–61. Ridzi, F., & Prior, T. (2020). Community leadership through conversations and coordination: The role of local surveys in community foundation run community indicators projects. International Journal of Community Well-Being. https://doi.org/10.1007/s42413-020-00098-z Smolko, R., Strange, C. J., & Venetoulis, J. (2006). The community indicators handbook: Measuring progress toward healthy and sustainable communities (2nd ed., p. 5). Redefining Progress. Stevens, C., de Blois, M., Hemberg, R., Baldwin, J., & The Community Indicators Consortium. (2021). Community indicators project development guide. Community Indicators Consortium. Sung, H., & Phillips, R. (2016). Conceptualizing a community well-being and theory construct. In Y. Kee, S. J. Lee, & R. Phillips (Eds.), Social factors and community well-being (pp. 1–13). Springer. Sung, H.-K., & Phillips, R. G. (2018). Indicators and community well-being: Exploring a relational framework. International Journal of Community Well-Being, 1(1), 63–79.

Part III

Implementation

Part III (Implementation) contains five chapters. Chapter 6 describes the data collection process of indicators projects. The chapter makes a key distinction between primary and secondary data and how these data can be collected effectively. Chapter 7 describe the data analysis process. The chapter covers basic statistical concepts related to data variables and measurement scales. These basic concepts are foundational in performing descriptive and inferential statistics. The chapter also describes data mining, a trend that is increasing in popularity and use. Chapter 8 describes the process related to data reporting. In doing so, we discuss a key difference between reports written for the general public versus report that are more technical that can pass the muster of technical scrutiny. Chapter 9 focuses on promotion issues. That is, we discuss issues related to distributing the reports (both public and research reports). We describe how the public report can be effectively promoted through public relations and e-marketing techniques. We also describe strategies to stimulate community action and translate outcome indicators into action indicators. Finally, Chap. 10 focuses on follow-up issues. Specifically, the chapter the five W’s related to annual reviews: What? Why? When? Where? And Who? We also discuss how to measure the impact of indicators reports, an important aspect of the follow-up process.

Chapter 6

Data Collection

Learning Objectives In this chapter the reader will learn how to answer the following questions: 1. What is the distinction between secondary and primary data in community indicators research? 2. How to deal with the time element of secondary data? 3. How to manipulate the data using formulations such as ratio, proportion (and percent), rate, per capita, constant dollar, weighted averages, and composite index? 4. What are the traditional goals of a survey study in community indicators research? 5. What questions should be asked in a survey and why? 6. What are the approaches commonly used in the development of survey items? 7. Should indicator researchers develop their own measures or borrow measures from published studies? 8. How do indicator researchers go about developing their own measures of community well-being? 9. How do indicator researchers figure out the survey’s sample size? 10. What are common sampling techniques used in indicators projects? 11. What are some common data collection techniques used by indicators projects? 12. How do indicator researchers validate their subjective measures? 13. How do indicator researchers weight the sample at the data analysis stage?

Secondary Data Once indicators have been selected, the project completes the planning process and begins the implementation process. The implementation process involves two different sets of tasks: (1) compiling secondary data, and (2) collecting primary data. As © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_6

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previously described, secondary data involve statistical information about the selected indicators available from statistical agencies and other sources. Primary data, in contrast, are data obtained from a community survey conducted by the indicators project team. In compiling secondary data, the staff assigned to perform this task goes through two major several steps: (1) dealing with the time element of the data, and (2) manipulating the data. These tasks are repeated annually. This assumes that the indicators project is here to stay, and part of its ongoing charge is to report data trends every year to monitor community progress toward higher levels of quality of life.

Dealing with the Time Element of the Data Remember what we talked about in the context of the Planning Process discussion: we usually start out with a list of indicators available from known statistical agencies and make several decisions to select the final set of indicators. In other words, we start out knowing what the statistical agencies are and what data are available. The next step is contact these agencies to make arrangements to use their data related to the selected indicators. At that stage, it is very likely that different statistical agencies will have data along some time interval. Different statistical agencies collect their data in different cycles (e.g., monthly, quarterly, semi-annually, annually, biennially, etc.). Typically, indicators project data are reported annually. Therefore, some degree of data manipulation is warranted. Monthly, quarterly, and semi-annual data are averaged into annual figures. Biennial data are reported for the 2 years in question—the same figure is used twice under the 2 years in question. And so forth. Furthermore, the precise calendar differs from one institution to the next. For example, educational data are gathered using the school year (starting August). Financial data reflect the fiscal year (starting from June). So, what to do? Typically, indicator researchers deal with this problem is two ways. First, they report data annually and using the calendar year. This means they use whatever information that approximates the time interval in question and report the data closest to the year in question. They do this by documenting the discrepancies in the body of the report and in the notes underneath each table (i.e., the table’s legend). For example, if we are reporting educational data (e.g., % graduating from high school) for the year 2002, 2003, and 2004 but the department of education at the state level has these data for 2001–2002 (August 2001 to June 2002), 2002–2003 (August 2002 to June 2003) and 2003–2004 (August 2003 to June 2004). Therefore, the data for 2001–2002 are used to report performance for 2002. And similarly, the data for 2002–2003 and 2003–2004 are used to report performance for 2003 and 2004, respectively. Also, what is typically done is to document these discrepancies in the legend (notes) embedded under the table in question. These discrepancies are also noted in the discussion and interpretation of the data in the body of the report. Second, some indicator researchers attempt to manipulate the data to arrive at an approximation or an estimate for the year in question. For example, if the focus is to

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report data concerning 1 year in particular, let’s say 2003, the average of 2002–2003 and 2003–2004 is computed and reported. Such data manipulation is permissible only when there is no need to report trend data, but the focus is exclusively on a particular year (or other time interval).

Manipulating the Data In the previous section, we discussed the need for some data manipulation to deal with the element of time in the way the data are made available and reported. In this section, we will describe other circumstances necessitating additional data manipulation. We will describe the following calculations: ratio, proportion (and percent), rate, per capita, constant dollar, weighted averages, and composite index. Indicators researchers use raw data to compute constructs involving ratios. An example of a ratio is population density. This construct is calculated by dividing total population by land area. Total population ¼ Population density Land area Here is an example, if a neighborhood has a total population of 30,000 residents and the land area of the neighborhood is 10 square miles, then the population density of that neighborhood is calculated to be 3000 residents per square mile. 30, 000 ¼ 3000 10 Proportion and percent are also commonly used in indicators research. Proportion is a ratio involving a denominator is the total and the numerator represents part of the total. For example, one can compute the proportion of children under the age of 18 in a neighborhood. Example: actual count of children in a particular neighborhood is 5000 and the total population of residents in the same neighborhood is 30,000. Hence, the proportion of children in that neighborhood is 0.167. Multiply this figure by 100 and we obtain a percent (16.7%). 5000 ¼ 1:67ðor 1:67%Þ 30, 000 Rate refers to an occurrence of a specific event over time, usually expressed in relation to a constant such as 100, 10,000 or 100,000. For example, we can compute disease incidence (e.g., cancer incidence in a community) as a rate. For example, the community has 200,000 residents and 5000 of these residents are reported to have some form of cancer. As such, the cancer rate in that community is 2.5%; it is computed as 5000 divided by 200,000 and multiplied by 100.

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5000  100 ¼ 2:5% 20, 000 With respect to per capita calculations, we do this to eliminate the effects of population growth on that indicator. Here is an example. Suppose we want to report statistics related to cigarette consumption. The data reported from the Department of Health are number of packs of cigarettes sold on annual basis. Suppose that the numbers look like they are increasing significantly. Does this increase signal a valid increase in cigarette consumption? The answer is: not necessarily. Perhaps the increase is due to the increase of the population. Therefore, we need to rule out the effects of population growth. This is done by dividing the number of packs sold for a particular year with the size of the population in that year. Crime rate is another example. Instead using number of crimes reported, we calculate a rate by obtaining the raw figures (e.g. the number of crimes reported) and we do the per-capita calculation ourselves using the population estimate. Regarding the constant dollar, there are many instances in which we report revenue and expenditure figures (e.g., the cost of 1000 kwh of electricity). But then the value of a dollar today is not the same as the value of a dollar last year, the year before, 10 years ago, or 50 years ago. We need to adjust currency calculations with the rate of inflation (price deflator) of the country in question. More specifically, dollar trend lines that have not been adjusted for inflation give the distorted appearance of moving upward more than is accurate in terms of actual buying power. Therefore, dollar figures must be deflated. That is, they must be adjusted so that the figure for each year is presented in terms of the buying power for a single, selected, base year. The most logical choice for a base year is the most recent year. Thus, all dollar figures are presented at nearly current dollar value, making them most meaningful for those seeking to understand the indicator. The downside of this choice is that all dollar figures must be recalculated each year using new deflator factors. The most authoritative and readily available source of the rate of inflation is the Consumer Price Index. However, we do a little manipulation of the inflation rate to obtain price deflators to all the years we are trying to adjust. We typically start with the most current year (or previous year) as the base year and report the dollar figures for that year. Then using price deflators, we adjust the dollar figures of all past years. We do the adjustment as follows: Actual dollar figure for a particular year ¼X Price deflator figure for that year For example, if we are tracking an indicator of average public-school teacher salaries and the average salary in school year 1997–1998 was $37,000, you would compute the deflated salary for base year 2006 as follows, using the deflator calculated in the example above:

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$37, 000 ¼ X ¼ $42, 334 874 Also, we typically report both actual and deflated dollar figures in the final analysis. With respect to the weighted average, there are many instances in which we calculate a weighted average. Consider the situation in which statistics pertaining to certain indicators are reported from two or three different sources. Suppose these sources vary in degrees of credibility. In other words, we trust the data from some sources more than others. Suppose cigarette smoking among teens is estimated from two different sources: survey conducted by the census bureau and another survey conducted by the local school board. The estimate is 40% from the census bureau and the local school board produced an estimate of 50%. Suppose we trust the data from the census bureau more so than the local school board data. We can assign % weights based on our perception of confidence to the data from the census bureau and the local school board data, let’s say 70% and 30% respectively. Instead of computing a simple average between 40 and 50%, we obtain a weighted average. We do this by multiplying 40 by 0.7 (¼ 28) and 50 by 0.3 (¼ 15) and the summing up the results (28 + 15). The weighted average is 43%. Weighted average ¼ ðestimate 1  %weight 1Þ þ ðestimate 2  %weight 2Þ þ . . . þ ðestimate n  %weight nÞ The last data manipulation technique is the composite index. Many community indicators projects report an overall index, a number that represents the entire quality of life of the community by year. The overall index is useful because one can say, “Last year our community quality-of-life index was 85, and this year it has risen to 89.” Many economic national indicators are reported using overall composite indices. Examples include the Gross National Product and the Dow Jones Index. Of course, overall indices do not mean much because they aggregate all the indicators’ statistics into one factor score. The meaningful information resides with the individual indicators. However, for the purpose of tracking the overall performance of the community, an index may be useful despite its obvious limitations. How do we combine indicators to form composite indices? Some say, “you cannot mix apples with oranges.” The result is neither “apples” nor “oranges.” The challenge is to transform all the raw numbers of the indicators into performance scores. And this is indeed a big challenge because it requires understanding whether a given raw figure of a certain indicator is “good,” “fair,” “medium,” “poor,” or “bad.” How do we assign % scores to these performance categories? We do this by first obtaining a score distribution dividing the entire distribution into five categories—the top percentile of each category is used to assign a score to that category. For example, we may end up with something like assigning “good” a performance score of 100, “fair” a score of 80, “medium” a score of 60, “poor” a score of 40, and “bad” a score of 20. Let us illustrate how we develop composite indices using an example, namely an index of knowledge well-being. This construct has two key dimensions: education

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and communication Prescott-Allen (2001). There are two indicators for education: net primary school enrollment (which is the % of children of primary school-age who are enrolled in primary school) and net secondary school enrollment (which is the % of children of secondary school-age who are enrolled in secondary school). When we obtain the score distribution of these two variables we end up with the following performance scores: with respect to primary school enrollment we assign 100 ¼ good, 95 ¼ fair, 90 ¼ medium, 80 ¼ poor, 60 bad; and with respect to secondary school enrollment, we assign 100 ¼ good, 90 ¼ fair, 80 ¼ medium, 60 ¼ poor, 30 bad. Then we compute the average of these two variables to produce a resultant score for the education dimension. With respect to communication, this dimension is also captured using two indicators: number of telephones and cellular phones per 100 persons and Internet users per 10,000 persons. By obtaining the score distribution of these two variables and dividing these distributions by five categories we end up with the following assignment of performance scores: with respect to the number of telephone/cellular phones per 100 persons, we end up with 100 ¼ good, 50 ¼ fair, 25 ¼ medium, 12 ¼ poor, 6 and bad; and with respect to the Internet uses per 10,000 persons, we end up with 1200 ¼ good, 600 ¼ fair, 300 ¼ medium, 150 ¼ poor, and 75 bad. We now average the scores of these two variables to derive an overall score for the communication dimension. Our final task is to combine the overall score of the education dimension with that of the communication dimension. Should we get the simple average of these scores? By doing so, we assume equal weights. In other words, education should play an equal role to communication in capturing the knowledge construct. Perhaps not! Weighted average may be a better solution here. One can argue that the education dimension should be weighted more than the communication dimension, perhaps 2 to 1. In other words, we should weigh the education dimension by 2 and the communication dimension by 1 and then obtain the average of these two scores. The weighted average score represents the composite index of knowledge well-being.

Primary Data Primary data usually involves the administration of a community survey through a telephone, mail, Internet, or door-to-door survey. This is what we call “quantitative research.” In contrast, we use other “qualitative” techniques. Qualitative research usually involves focus groups, in-depth interviews, and open-ended (i.e., write-n) questions in surveys. This type of research allows the community indicators researcher to gain special insights into particular issues. In many cases, qualitative research precedes quantitative research in that the use of qualitative research helps the community indicators research develop the survey questionnaire. As such in the following sections we will concentrate on survey mechanics. A recent survey of community indicator projects revealed that 62% of those surveyed indicated that they use primary data (public opinion surveys) in addition to

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secondary data (Alliance for Regional Stewardship, 2006). But what is(are) the goal (s) of primary data collection?

Goals Associated with Survey Conducting a survey of community residents serves two key goals: complimenting the secondary data and validating the objective indicators with subjective ones. A community survey serves to fill in the holes left from the secondary data collection. For example, if a key sub-dimension of the economy is quality of wages, and no statistics were found about this important sub-dimension, then the survey can fill in the gaping hole by including one or more survey items capturing residents’ perception and evaluation of their wages (and/or the wage of the head of the household). Also see Box 6.1. With respect to the second goal (validation), surveys can be used to validate the objective indicators with subjective ones. If the secondary data show that the community is enjoying a high well of economic, social, health, and environmental well-being, does this reality translate into the subjective reality of the community residents? Do community residents indeed perceive their community to enjoy a high level of economic, social, health, and environmental well-being? If such a discrepancy exists, then perhaps community planners and other community leaders should educate and inform their residents that their community does indeed fare well in terms of its quality of life. It is important for the community residents to be informed because this information may help change their negative opinion of the well-being of their community, which in turn may contribute to an increase in their life satisfaction or overall happiness. This occurs because perception of community well-being (or satisfaction with one’s community) is a significant contributor to overall life satisfaction. What happens when community residents perceive their community in a favorable light (their favorable opinion matching the positive secondary data)? Again, this is a very positive situation in which both secondary and primary data indicate that community well-being is favorable. Such a circumstance warrants promotion. Promoting this fact to community residents should enhance community cohesion and pride, which in turn should further enhance residents’ satisfaction with their community and generate an overall sense of well-being. Furthermore, promoting this fact to the outside world should attract new people and businesses to the community. More people and businesses in the area means greater economic development, which coupled with good community planning, could further enhance the quality of life of the community at large. What happens when the secondary data show that the community scores low on well-being dimensions such as economic, social, health, and environmental wellbeing? Should this be validated against perceptions of community residents? Again, the answer is yes. If community residents perceive their community unfavorably (matching the objective reality), then such a situation warrants a concerted effort to turn things around. This situation should motivate community planners and leaders

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to take action to improve the quality of life in the community. The oddball here is what happens when objective indicators indicate a low level of community wellbeing, but community residents express high level of satisfaction with their community. In other words, the negative factual situation does not match with the positive subjective situation. What to do? The answer is: do better. Community residents are satisfied, but if community conditions and services improve, then community residents are likely to feel better about their community. In sum, the key goal of collecting primary data in the form of a community survey about community conditions and services is to validate the objective reality with the subjective one, and to note any discrepancies between the objective and subjective reality. The ultimate goal is to create community programs and policies that can lead to a high level of objective plus subjective community wellbeing. Box 6.1: Providing Insights into Community Conditions Through their indicators project (https://cnyvitals.org/), the Central New York Community Foundation has used a local survey to deal with poverty in Syracuse by providing insights into community conditions that fester poverty. The community had been ranked as among the worst in the United States for concentrated poverty. There was insufficient data about this problem. To address the causes and conditions of concentrated poverty in Syracuse, the survey broke down the experience of poverty into concrete issues related to housing, transportation, healthcare, clothing etc. Source: Adapted from Ridzi and Prior (2020)

Survey Questionnaire The most important part of the primary data collection is the design of the survey questionnaire? What goes in the survey questionnaire? In other words, what questions should be asked and why? Keep in mind that the key goal of any primary data collection is a validation of the objective indicators derived from secondary information sources. With this in mind, the survey questions should reflect the conceptual dimensions and sub-dimensions underlying the objective indicators scheme. Consider the following example. The major quality-of-life dimensions selected for the objective indicators scheme involves economic, consumer, social, health, and environmental well-being. Each of these major quality-of-life dimensions is composed of sub-dimensions. Economic well-being involves three sub-dimensions, namely standard of living and cost of living. The consumer well-being dimension is composed of availability of a variety of retail establishments in the local area and the availability of shopping centers and malls. The social well-being dimension is composed of education, communication, recreation, and crime. The health well-being dimension involves two

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sub-dimensions: physical health and mental health. Environmental well-being involves pollution (air, water, land, and noise), congestion, and beauty of landscape. Suppose that objective indicators of these quality-of-life dimensions are already in place. The goal now is to design a survey questionnaire that can capture the subjective indicators of the same quality-of-life dimensions and sub-dimensions. There are two approaches to the development of survey items to capture the sub-dimensions in question: (a) developing the survey items from scratch, and (b) borrowing and adapting valid measures from published studies. The rule of thumb is to borrow and adapt valid measures from published sources. This is a sure way to use good measures. Published studies containing survey measures of the construct in questions are likely to provide measures that are accepted by the scientific community as “valid.” The concept of validity is very important from a psychometrics point-of-view. Psychometricians (researchers specialized in the measurement of subjective constructs) recommend the use of measures that have demonstrated construct validity. Psychometricians define construct validity in terms of face validity (the extent to which the wording or the phrasing of the item reflects the conceptual sub-dimension in question), convergent validity (the extent to which a measurement of the sub-dimension in question correlates highly with other items of the same sub-dimension), discriminant validity (the extent to which a measurement item of a sub-dimension does not correlate significantly with another measurement items designed to capture a different sub-dimension), and predictive (or nomological) validity (the extent to which the measures of a given sub-dimension successfully predict other constructs well-established by theory). Therefore, the experts suggest that we should not develop our own measures. We develop our own measures only in the absence of published measures. Even in selecting published measures, we ought to select those that have demonstrated face, convergent, discriminant, and predictive validity. We develop our own measures only as a last resort. And when we do, we do so with an eye on face validity—that is, we make sure that the items are phrased in a very clear manner capturing the conceptual quality-of-life sub-dimensions in question. Even so, we do not rest until we validate our own measures (see the subsequent section titled “Validating the Measures”). This means that an indicator researcher has to have the means to conduct a thorough search of the literature on quality-of-life studies, social indicators research, community development, as well as the published literature in psychology, social psychology, sociology, community psychology, political science, public administration, and economics. Most universities have search facilities in their libraries, and indicator researchers should use these resources to identify published studies involving the constructs in question. Assuming that such a search is not feasible, thus the researcher does not have much of a choice but to develop his or her own survey items to capture the selected quality-of-life sub-dimensions. Let’s go through that exercise. As previously stated, we used a conceptual scheme involving five major dimensions: economic, consumer, social, health, and environmental well-being. Each of these dimensions is composed of sub-dimensions. Economic well-being involves two sub-dimensions, namely standard of living and cost of living. Thus, we need to develop survey items

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capturing standard and cost of living. Here is a good attempt: “Standard of living refers to how financially comfortable people are with the money they make from work or other sources of investments. Rate your own standard of living using the following scale: very poor, less than adequate, adequate, more than adequate, very good.” Note that we start out by defining standard of living to the respondents to make sure that they have a clear idea of what we are asking them to do. Then we ask them to rate their standard of living. We get the respondents to rate the standard of living using a scale with semantic categories that they can easily comprehend and relate to. Also, we try to use balanced scales—scales that have a number of semantic categories on one side of the scale that have comparable and opposite meaning on the other side of the scale. The cost of living sub-dimension can be measured similarly: “Cost of living refers to whether the cost of buying goods and services (including homes and automobiles) in the local area is financially burdensome. Rate the cost of living in your community using the following scale: very high, somewhat high, neither high nor low, somewhat low, and very low.” Similar measures can be constructed for the sub-dimensions of consumer, social, health, and environmental well-being. Other considerations in the design of the community survey questionnaire: • Whether the survey is a telephone, mail, door-to-door, or Internet-based survey, the introduction part of the questionnaire has to make a credible impression on the prospective respondent. The appeal has to come from a prominent organization or leader within the community. • The plea is to complete this very important survey because community planners and other leaders will use the data to help them develop programs and policies that can enhance the quality of life of the community at large. • Prospective respondents have to be assured that their individual responses will remain anonymous and confidential. Individual data will be used for statistical purposes only (i.e., will be aggregated with other individual respondents to develop community profiles, NOT individual profiles). • If data collection is done by mail, Internet, or door-to-door, then a specific deadline for completing the questionnaire has to be specified. • The questionnaire should be divided in sections and clearly labeled (e.g., economic well-being, consumer well-being, social well-being). • The particular scales used in the questionnaire may vary as a function of the data collection method. For example, a 10-point rating scale is permissible in a mail and door-to-door surveys but not in telephone surveys. Scales used in telephone surveys are much shorter. (e.g., 3-point scales). • The aforementioned measures pertain to what we typically call outcome (or “system”) indicators. Outcome indicators refer to end goals such as individual end states that reflect economic well-being (e.g., standard of living) compared to process indicators (these indicators are sometimes referred to as “program indicators,” “policy indicators,” “performance indicators,” and “input/process/output indicators”). The latter point to conditions, programs or policies that produce the outcomes (e.g., quality jobs in the local area). Many surveys of indicators projects

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insert measurement items capturing residents’ evaluation of those process indicators. These are particularly useful in guiding community planners and policy makers in linking specific programs and policies with specific well-being outcomes. • Measures of community satisfaction, perception of the quality of the community, or overall attitude towards the community have to be introduced into the questionnaire toward the end part of the questionnaire (more on this in the forthcoming “Validating the Measures” section). • Insert demographic questions (e.g., age, education, sex, marital status, ethnicity, income) in the last section of the questionnaire.

Sampling and Data Collection Method Let’s talk about sampling and we will start the discussion by making a distinction between sample and population. Typically, community surveys rely on extracting a sample from the entire population (i.e., the entire population of the target community). However, ideally speaking, surveying the entire population could mitigate the most significant problem associated with sampling, namely sample representativeness (i.e., whether the sample truly captures information about the entire population of the community residents. There are instances in which a survey of the entire population is feasible. For example, if the survey is focused on primary education in schools in the area and the community indicators researchers decide to survey members of the local PTAs (Parents-Teachers Association), there is the possibility of surveying the entire population. It is very likely that the local PTAs may have full contact information of their membership. As such, a survey can be directed to the entire population of PTA members. However, these are exceptions, not the rule. Typically, the vast majority of community indicators projects that employ surveys use samples of the entire population of community residents because it is very costly difficult to survey the entire population especially with certain data collection techniques such as personal interviews. So, we will proceed by discussing sampling techniques but then when we start addressing issues related to data collection the reader should keep in mind that surveying the entire population is possible for some target groups (e.g., members of local PTAs) using certain data collection techniques (e.g., web-based surveys). We will proceed by discussing decisions related to sampling techniques, data collection methods, and last but not least sampling frames. As the reader will note throughout the discussion, these decisions are highly interrelated. With respect to sample size, many indicator projects employ a sample of approximately 500 (completed surveys). The 500 number is customarily referred to as the final sample. To achieve 500 completed surveys, we start out with a much larger sample (e.g., 3000). We refer to this as the initial sample. But then the size of the initial sample is highly dependent on several factors: type of data collection, geographic boundary and units within, accuracy, confidence level, and cost.

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The response rate (% of sampled residents contacted completing the survey successfully) varies considerably as a direct function of the data collection method. Internet-based surveys produce the lowest response rate while the door-to-door approach the highest. It is most difficult to refuse completing a survey questionnaire that “should benefit the community” if a mature person comes to your door, explains the purpose of the survey, and kindly requests that you take a few minutes to complete the questionnaire at your convenience. That person also requests the questionnaire to be completed by a certain date (in a few days or a week) and that he or she will come by to pick up the completed questionnaire personally. This data collection method produces the highest response rate; the second best is the telephone method, followed by the mail method, with the e-mail/Internet-based survey producing the lowest response rate. Therefore, if we are planning to use the door-todoor method, we start out with an initial sample that is close to the final sample (e.g., initial sample of 600 is likely to produce a final sample of 500 completed surveys). In contrast, if we are shooting for 500 completed questionnaires through an e-mail/ Internet based survey, we start out with a much larger sample (e.g., 5–7000). The second factor considered in determining sample size is the geographic boundary and units within. The sample size varies considerably as a function of the largess of the surveyed community (geographic boundary: region involving several counties and cities, county, city, town, neighborhood) and the geographic units within (e.g., city divided into neighborhood). For example, if the survey involves several counties that are further subdivided into cities, towns, and neighborhoods, and each locality should be represented (at least proportionally) in the sample, that sample will have to be much larger than 500 (completed surveys). However, if there is no need to ensure that each neighborhood or locality is significantly represented in the survey data, then a smaller sample size would suffice. The third and fourth factors are accuracy and confidence level. These two factors are related to the following two questions, respectively: (a) how small do we want the margin of error to be, and (b) how much confidence do we want to place on the interval between the estimate and reality? Typically, indicator researchers set the desired margin of error at plus or minus five percentage points with a confidence level of 95%. One might say, for example, that “Fifty-five % plus or minus 5% of households think that the standard of living in community XYZ is “good” with 95% confidence. That is, if the survey were conducted 100 times, the result would be the same 95 times—the more accurate our need for the study findings (plus or minus 4%, 3%, 2%, or 1%), the larger the sample size; similarly, the higher the confidence interval (99% instead of the customary 95%), the larger the sample size. The last factor considered in sample size is cost. The rule of thumb is the larger the budget the more we can afford more data—that is, larger samples. The cost factor associated with sample size is most pronounced with the data collection technique. Consider the door-to-door data collection—the more completed surveys we want, the more doors we have to knock on and the more time we have to spend interacting with potential respondents explaining to them what the survey is about and eliciting their participation in the survey. Time, effort, and money get steeper and steeper with larger samples. Similarly, the cost of contacting more potential respondents through

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a mail survey is high (but probably not as high as door-to-door). Sending out more mail questionnaires requires more money to print additional questionnaires, envelopes, and return envelopes. More money is needed for postage to mail the questionnaires and postage for the return ones. The cost of manpower in stuffing and mailing those envelopes has to be considered too. Now let’s talk about sampling techniques. Indicator researchers typically use a probabilistic sampling technique involving either a stratified sampling or clusterbased sampling. First, let’s define probabilistic versus non-probabilistic sampling. Probabilistic sampling is the method of selecting prospective respondents in a way that each and every community resident has an equal chance of being selected. Thus, if we have a sampling frame (list with person name and contact information) that identifies every community resident in the designated geographic boundary and select a random sample from that frame, the sample would be characterized as probabilistic. Of course, this is the ideal sampling technique, and we strive to employ a sampling technique that comes close to this ideal. It is ideal because it is extremely difficult to obtain a sampling frame that captures every person in the targeted area. To appreciate probabilistic sampling, we need to contrast this sampling technique with its converse, namely non-probabilistic sampling. A good example of non-probabilistic sampling is intercepting people at some event such as a mall intercept or asking people who attend church on Sunday to complete the questionnaire. This is what we call convenience sampling, and it is certainly non-probabilistic. Non-probabilistic sampling produces the kind of findings that cannot be generalized to the entire community. In contrast, we can generalize the findings to the entire community if and when we use probabilistic sampling. See Box 6.2. Box 6.2: The Sampling Method Used by the Santa Cruz County California Community Assessment Project The Santa Cruz County California Community Assessment Project uses a telephone survey of a sample of Santa Cruz County residents, in both English and Spanish with over 700 randomly selected county residents. The goal of the survey is to measure the opinions, attitudes, and needs of a demographically representative sample of the county’s residents. The survey is conducted every year. Potential respondents are selected based on phone number prefixes, and a quota sampling is employed to obtain the desired geographic distribution of respondents across the three regions of the county. Source: Adapted from Zachary (2009, p. 5) In the context of probabilistic sampling, indicator researchers employ stratified and cluster-based sampling. Stratified sampling is a technique that ensures proportional representation of groups of people. In many instances, we use stratified sampling to ensure that different ethnic groups are proportionately represented in the survey (proportionate to the size of their own groups within the community). For

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example, if there are 30% of the community residents are African Americans, then we strive to ensure that our final sample will reflect this proportion. Similarly, many indicators projects employ income stratification. Stratified sampling is used with telephone, mail, e-mail/Internet surveys, but customarily not with door-to-door surveys. Door-to-door surveys employ cluster-based sampling. Here is an example of cluster-based sampling done in the context of door-to-door surveys. Selected neighborhoods are divided in equal larger units. A sample of these larger units is selected. In the context of a selected neighborhood, the door-to-door interviewer knocks on the door of a home. If this generates a successful contact, the interviewer skips x number of homes and then knocks on the door of the next target home. If the knock does not generate a successful response, the interviewer moves on to the home next door, and repeats the process. This process is repeated until a quota number of distributed questionnaires is achieved. Now let’s turn to data collection techniques. As previously stated, there are at least four common data collection techniques used by indicator projects. These are telephone, mail, Internet, and door-to-door. A telephone survey is typically conducted by randomly calling residents in the community until some a quota is achieved (e.g., sample of 500 in a city of approximately a million) within the designated geographic boundary. If the geographic boundary is further divided by neighborhoods, then a stratified sample is used to ensure a quota of certain number of respondents in each neighborhood. In other words, people are identified in certain neighborhoods and a sample of each neighborhood population is selected based on the size the neighborhood (i.e., larger neighborhoods have proportionally larger sample sizes). A mail survey is another commonly used data collection technique. Again, a sampling frame (list of people) of the community population within the designated geographic boundary is developed (e.g., through tax or voter registration records), and a sample of these residents are mailed the survey questionnaire. Similarly, if the community is divided into neighborhoods, then the sample is stratified by neighborhood to achieve a minimum number of respondents within each neighborhood. E-mail/Internet-based surveys involve sending out e-mail messages to a large sample of community residents randomly selected. The e-mail message informs them about the community survey and attempts to recruit them as respondents for the study. Embedded in the e-mail message is a link to a web-based survey. A web-based survey is a survey taken on the web and scored automatically and the data is downloaded into a data file for further processing. Door-to-door data collection entails cluster sampling. That is, the community is divided into neighborhoods, and within each neighborhood an interviewer randomly selects certain homes, knocks on each door and requests that an adult member of the household complete the survey questionnaire (either on the spot or makes arrangement to pick up the completed questionnaire after a few days). This is done until a specified quota for each neighborhood is met. Typically, the telephone interview survey generates a high response rate and is least costly. One can further reduce the cost of the telephone survey by recruiting community volunteers to conduct the telephone interviews. The door-to-door survey method also generates the highest response rate but is the costliest. The cost of this

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method can significantly be reduced if the people handing out (and collecting) the survey questionnaires are volunteers (non-paid staff). The mail survey method generates the lowest response rate and is somewhat costly. One can decrease the cost of the mail survey by request local printers to print the questionnaire pro-bono (as a service to the community). A volunteer staff can be assembled to stuff the envelopes, print and paste mailing labels, etc. This can significantly reduce the cost of the mail survey. The e-mail/Internet-based survey is least costly but the response rate of usually dismal. Also, many criticize the use of e-mail/Internet-based surveys as not based on probability sample—Internet-users are not likely to come from the lower classes. That is, a significant socio-economic segment of the community is not likely to be represented by the study findings.

Validating the Measures In the section dealing with measures and questionnaire design, we addressed the psychometric issue of selecting valid measures in terms of face, convergent, discriminant, and predictive (nomological) validity. We made the point that it is better to select valid measures from previously published studies than to develop one’s own measures from scratch. We talked about developing measures from scratch as a last resort. We hinted at the need to validate these newly designed measures. A common way to validate the measures capturing the quality-of-life dimensions and sub-dimensions is to use them in a regression equation (as predictor variables) to predict overall community satisfaction or perception of overall quality of the community as a place to live and work. An example of such as measure that can be included in the survey questionnaire is as follows: “Rate the overall quality of your community as a place to live and work: “very poor,” “poor,” “so/so,” “good,” and “very good.” There are many valid measures of overall community satisfaction or perceived quality of the community published in quality-of-life journals such as Social Indicators Research, the Journal of Happiness Studies, Quality of Life Research, and Applied Research in Quality of Life. The theoretical assumption (supported by many studies in the quality-of-life research literature) is that residents’ satisfaction with major dimensions of community well-being (economic, social, consumer, health, environmental, etc.) are likely to be good predictors of residents’ overall feelings of satisfaction with the community at large. Therefore, to validate the newly constructed measures, indicator researchers regresses measures of global community satisfaction with the measures capturing the dimensions of community well-being (or indices of their sub-dimensions). If the regression results show successful prediction (significant beta weights for the community well-being dimensions and a decent multiple r-square), then the newly constructed measures can be deemed valid.

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Weighting the Sample and Data Analysis We addressed the issue of stratified sampling in a previous section. As a reminder, stratified sampling is a sampling technique designed to ensure that certain segments of the community population are represented in a manner proportionate to their size in the community. For example, we want to make sure that the poor residents (who make up 20% of the population in the target community) are represented in the survey (i.e., approximately 20% of the respondents should be classified as poor). Although sample stratification in data collection is ideal, in reality we fail to reach the target number of respondents in certain segments. So, what to do? Weight the sample after the fact—at the data analysis stage. How is weighting done? We start out by obtaining census data to get a true picture of the segments’ representation in the community (e.g., census data show that 20% of the community residents fall below the poverty line). Suppose that the survey shows that only 5% of the respondents are poor (95% are above the poverty line). The proportion of the population divided by the proportion of the quality-of-life survey respondents in each cell is then calculated to derive the appropriate weight to use as a multiplier for individual responses to the survey. In our example, the weight for the poor is 4.0 (or 20/0.05 ¼ 4.0). This is the multiplier for all responses of respondents classified as falling below the poverty line. Conversely, the multiplier for respondents fall above the poverty line is 0.84 (or 0.80/0.95 ¼ 0.84).

Summary This chapter covered much material related to data collection of primary and secondary data. Secondary data involve statistical information about the selected indicators available from statistical agencies and other sources. Primary data, in contrast, are data obtained from a community survey conducted by the indicators project team. Two major tasks are involved in compiling secondary data: dealing with the time element of the data and manipulating the data. In dealing with the time element, it is very likely that different statistical agencies will have data along some time interval. Different statistical agencies collect their data in different cycles (e.g., monthly, quarterly, semi-annually, annually, biennially, etc.). Typically, community indicators’ data are reported annually. Therefore, some degree of data manipulation is warranted. Monthly, quarterly, and semi-annual data are averaged into annual figures. Biennial data are reported for the 2 years in question—the same figure is used twice under the 2 years in question. Furthermore, the precise calendar differs from one institutional sector to the next. For example, educational data are gathered using the school year (starting August). Financial data reflect the fiscal year (starting June). So, what to do? Typically, indicator researchers deal with this problem is two ways. First, they report data annually and using the calendar year (starting January).

Summary

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This means they use whatever information that approximates the time interval in question and report the data closest to the year in question. We do this by documenting the discrepancies in the body of the report and in the notes underneath each table (i.e., the table’s legend). Second, some indicator researchers attempt to manipulate the data to arrive at an approximation or an estimate for the year in question. Several types of data manipulation were described: ratio, proportion (and percent), rate, per capita, constant dollar, weighted averages, composite index. Indicators researchers use raw data to compute constructs involving ratios (e.g., population density ¼ total population / land area). Proportion is a ratio involving a denominator is the total and the numerator represents part of the total (e.g., proportion of children under the age of 18 in a neighborhood [0.167 or 16.7%] ¼ actual count of children in a particular neighborhood [5000] divided by the total population of residents in the same neighborhood [30,000]). Rate refers to an occurrence of a specific event over time, usually expressed in relation to a constant such as 100, 10,000 or 100,000 (e.g., cancer incidence in a community [2.5%] is computed by dividing these residents reported to have some form of cancer [5000] by the total residents in the community [20,000]). Per capita calculations are designed to eliminate the effects of population growth on that indicator. This is done by dividing the indicator statistic for a particular year with the size of the population in that year. The constant dollar calculation is designed to adjust currency figures with the rate of inflation (price deflator) of the country in question. Therefore, dollar figures must be deflated. We obtain price deflators to all the years we are trying to adjust. We typically start with the most current year (or previous year) as the base year and report the dollar figures for that year. Then using price deflators, we adjust the dollar figures of all past years (i.e., actual dollar figure for a particular year divided by the proper deflator for that year). The weighted average is used when statistics pertaining to certain indicators are reported from two or three different sources. Instead of calculating a simple average, we obtain a weighted average—weighted by the perceived credibility of the sources. The composite index is a calculation that combines indicators into an overall score. We first transform all the raw numbers of the indicators into performance scores (“good,” “fair,” “medium,” “poor,” or “bad”). This is done by obtaining a score distribution and dividing the entire distribution into five categories—the top percentile of each category is used to assign a score to that category. The remaining parts of the chapter dealt with data collection of primary data. There are two reasons for conducting surveys: complimenting the secondary data collection and validating the objective indicators with subjective ones. What goes in the survey questionnaire? The survey questions should reflect the conceptual dimensions and sub-dimensions underlying the objective indicators scheme. There are two approaches to the development of survey items to capture the sub-dimensions in question: (a) developing the survey items from scratch, and (b) borrowing and adapting valid measures from published studies. The rule of thumb is to borrow and adapt valid measures from published sources. This is a sure way to use valid measures. The concept of validity is very important from a psychometrics point-ofview.

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In developing our own measures, we are guided by the selected theoretical quality-of-life model, and we focus on a particular dimension or sub-dimension and attempt to create survey items accordingly. We start out by defining the concept we like to measure. Then we ask respondents to rate their perception of that community condition on a 5-point rating scale. We then addressed issues relating to sample size, sampling techniques, data collection methods, and sampling frames. Sample size is influenced by factors such as type of data collection, geographic boundary and units within, accuracy, confidence level, and cost. Regarding sampling technique, indicator researchers typically use a probabilistic sampling technique involving either a stratified sampling or cluster-based sampling. There are at least four common data collection techniques used by indicator projects: telephone, mail, Internet, and door-to-door. A common way to validate the measures capturing the quality-of-life dimensions and sub-dimensions is to use them in a regression equation (as predictor variables) to predict global measure of community satisfaction or perceived quality of the community as a place to live and work. The theoretical assumption (supported by many studies in the quality-of-life research literature) is that residents’ satisfaction with major dimensions of community well-being (economic, social, consumer, health, environmental, etc.) are likely to be good predictors of residents’ global satisfaction with the community or their overall perception of the quality of life in that community. Finally, many indicators projects weight the sample. This is done to ensure that certain segments of the community population are represented in a manner proportionate to their size in the community. Weighting is done by obtaining census data to get a true picture of the segments’ representation in the community. The proportion of the population divided by the proportion of the quality-of-life survey respondents in each cell is then calculated to derive the appropriate weight to use as a multiplier for individual responses to the survey.

Progress Check 1. What is the distinction between secondary and primary data in community indicators research? 2. How to deal with the time element of secondary data? 3. How to manipulate the data using formulations such as ratio, proportion (and percent), rate, per capita, constant dollar, weighted averages, and composite index? 4. What are the traditional goals of a survey study in community indicators research? 5. What questions should be asked in a survey and why? 6. What are the approaches commonly used in the development of survey items? 7. Should indicator researchers develop their own measures or borrow measures from published studies?

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8. How do indicator researchers go about developing their own measures of community well-being? 9. How do indicator researchers figure out the survey’s sample size? 10. What are common sampling techniques used in indicators projects? 11. What are some common data collection techniques used by indicators projects? 12. How do indicator researchers validate their subjective measures? 13. How do indicator researchers weight the sample at the data analysis stage?

Progress Check Answers 1. What is the distinction between secondary and primary data in community indicator research? Secondary data involve statistical information about the selected indicators available from statistical agencies and other sources. Primary data, in contrast, are data obtained from a community survey conducted by the indicators project team. 2. How to deal with the time element of secondary data? In dealing with the time element, it is very likely that different statistical agencies will have data along some time interval. Different statistical agencies collect their data in different cycles (e.g., monthly, quarterly, semi-annually, annually, biennially, etc.). Typically, community indicators’ data are reported annually. Therefore, some degree of data manipulation is warranted. Monthly, quarterly, and semi-annual data are averaged into annual figures. Biennial data are reported for the 2 years in question—the same figure is used twice under the 2 years in question. Furthermore, the precise calendar differs from one institution to the next. For example, educational data are gathered using the school year (starting August). Financial data reflect the fiscal year (starting June). So, what to do? Typically, indicator researchers deal with this problem is two ways. First, they report data annually and using the calendar year (starting January). This means we use whatever information that approximates the time interval in question and report the data closest to the year in question. We do this by documenting the discrepancies in the body of the report and in the notes underneath each table (i.e., the table’s legend). Second, some indicator researchers attempt to manipulate the data to arrive at an approximation or an estimate for the year in question 3. How to manipulate the data using formulations such as ratio, proportion (and percent), rate, per capita, constant dollar, weighted averages, and composite index? Indicators researchers use raw data to compute constructs involving ratios (e.g., population density ¼ total population / land area). Proportion is a ratio involving a denominator is the total and the numerator represents part of the total (e.g., proportion of children under the age of 18 in a neighborhood [0.167 or 16.7%] ¼ actual count of children in a particular neighborhood [5000] divided by the total population of residents in the same neighborhood [30,000]). Rate refers to an occurrence of a specific event over time, usually expressed in relation to a constant such as 100, 10,000 or 100,000 (e.g., cancer incidence in a

138

4.

5.

6.

7.

8.

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Data Collection

community [2.5%] is computed by dividing these residents reported to have some form of cancer [5000] by the total residents in the community [20,000]). Per capita calculations are designed to eliminate the effects of population growth on that indicator. This is done by dividing the indicator statistic for a particular year with the size of the population in that year. The constant dollar calculation is designed to adjust currency figures with the rate of inflation (price deflator) of the country in question. Therefore, dollar figures must be deflated. We obtain price deflators to all the years we are trying to adjust. We typically start with the most current year (or previous year) as the base year and report the dollar figures for that year. Then using price deflators, we adjust the dollar figures of all past years. We do the adjustment as follows: Actual dollar figure for a particular year divided by the proper deflator figure for that year. The weighted average is used when statistics pertaining to certain indicators are reported from two or three different sources. Instead of calculating a simple average with get a weighted average—weighted by the perceived credibility of the sources. The composite index is a calculation that combines indicators into an overall score. We first transform all the raw numbers of the indicators into performance scores (“good,” “fair,” “medium,” “poor,” or “bad”). This is done by obtaining a score distribution dividing the entire distribution into five categories—the top percentile of each category is used to assign a score to that category. What are the traditional goals of a survey study in community indicators research? There are two key goals to conducting a survey of community residents. One is to compliment the secondary data collection by providing data on key indicators that with unavailable statistics. The other goal is to validate the objective reality (secondary data) with subjective reality (primary data). Doing so would help create community programs and policies that can lead to a high level of objective plus subjective community well-being. What questions should be asked in a survey and why? What goes in the survey questionnaire are questions (or survey items) that reflect the conceptual dimensions and sub-dimensions underlying the objective indicators scheme. What are the approaches commonly used in the development of survey items? There are two approaches to the development of survey items to capture the sub-dimensions in question: (a) developing the survey items from scratch, and (b) borrowing and adapting valid measures from published studies. Should community indicator researchers develop their own measures or borrow measures from published studies? The rule of thumb is to borrow and adapt valid measures from published sources. This is a sure way to ensure use validity of the measures. How do community indicator researchers go about developing their own measures of community well-being? In developing our own measures, we are guided by the selected theoretical quality-of-life model, and we focus on a particular dimension or sub-dimension and attempt to create a survey item. We start out by defining the concept we like to measure. Then we ask them to rate their perception of that community condition on a 5-point rating scale.

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9. How do community indicator researchers figure out the survey’s sample size? Sample size is influenced by factors such as type of data collection, geographic boundary and units within, accuracy, confidence level, and cost. 10. What are common sampling techniques used in community indicators projects? Community indicator researchers typically use a probabilistic sampling technique involving either a stratified sampling or cluster-based sampling. 11. What are some common data collection techniques used by community indicators projects? There are at least four common data collection techniques used by indicators projects. These are telephone, mail, Internet, and door-to-door. 12. How do community indicator researchers validate their subjective measures? A common way to validate the measures capturing the quality-of-life dimensions and sub-dimensions is to use them in a regression equation (as predictor variables) to predict global community satisfaction or perceived community quality of life. The theoretical assumption (supported by many studies in the quality-oflife research literature) is that residents’ satisfaction with major dimensions of community well-being (economic, social, consumer, health, environmental, etc.) are likely to be good predictors of residents’ global satisfaction with the community or their overall perception of the quality of life in that community. 13. How do community indicator researchers weight the sample at the data analysis stage? Weighting is done to ensure that certain segments of the community population are represented in a manner proportionate to their size in the community. Specifically, the researcher starts out with census data to get a true picture of the segments’ representation in the community. The proportion of the population divided by the proportion of the quality-of-life survey respondents in each cell is then calculated to derive the appropriate weight to use as a multiplier for individual responses to the survey.

References Alliance for Regional Stewardship. (2006). Regional indicators: Telling stories, measuring trends, inspiring action. Alliance for Regional Stewardship. Prescott-Allen, R. (2001). The wellbeing of nations (pp. 36–40). Island Press. Ridzi, F., & Prior, T. (2020). Community leadership through conversations and coordination: The role of local surveys in community foundation run community indicators projects. International Journal of Community Well-Being. https://doi.org/10.1007/s42413-020-00098-z Zachary, D. (2009). Connecting outcomes to indicators: The Santa Cruz County California community assessment project (CAP). In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases (pp. 1–20). Springer.

Chapter 7

Data Analysis

Learning Objectives In this chapter the reader will learn how to answer the following questions: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

What are variables in statistical terms What are measurement scales in statistical terms? What is the distinction between descriptive and inferential statistics What are commonly used descriptive statistics in indicators projects? What are frequency and percent descriptive statistics? What are commonly used descriptive statistics capturing central tendency? What are some advantages and disadvantages related to central tendency descriptive statistics? What are commonly used descriptive statistics capturing data dispersion? What are commonly used descriptive statistics capturing position? What are common objectives of inferential statistics? What are inferential statistical methods Indicators commonly used by indicators researchers to test hypotheses? How do indicators researchers go about selecting the proper inferential statistical technique to test hypotheses? What is the distinction between independent and dependent variables? Predictor and criterion variables? How is this distinction helpful in hypothesis testing? How are composite indices constructed? What is data mining and how is data mining used in indicators projects?

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_7

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Data Variables and Measurement Scales Before commencing the data analysis exercise, indicators researchers take note of the variables at hand and the type of data inherent in these variables. Let’s take a few moments to describe “variables” and “measurement scales” that reflect the data inherent in the variables.

Variables Let’s start off with an example of a variable after which we will introduce the reader to a formal definition. Let’s say in the context of a community indicators survey we have a question in the survey questionnaire that captures residents’ evaluation of an aspect of community conditions such as neighborhood safety, policing, neighborhood watch, etc. The survey question related to neighborhood safety is as follows: “How safe is your neighborhood? Please rate your neighborhood in terms of safety using the following scale: 1¼very unsafe, 2¼somewhat unsafe, 3¼so-so, 4¼somewhat safe, and 5¼very safe.” The survey question related to policing is as follows: “How effective is law enforcement in policing your neighborhood? Please rate your neighborhood in terms of policing using the following scale: 1¼law enforcement has been completely ineffective in policing my neighborhood, 2¼law enforcement has been somewhat ineffective in policing my neighborhood, 3¼so-so, 4¼law enforcement has been somewhat effective in policing my neighborhood, and 5¼law enforcement has been very effective in policing my neighborhood.” The survey question related to neighborhood watch is as follows: “How effective is the organized effort of your neighborhood in mitigating crime? Please rate your neighborhood in terms of neighborhood watch using the following scale: 1¼the neighborhood watch has been completely ineffective in mitigating crime in my neighborhood, 2¼the neighborhood watch has been somewhat ineffective in mitigating crime in my neighborhood, 3¼so-so, 4¼the neighborhood watch has been somewhat effective in mitigating crime in my neighborhood, and 5¼the neighborhood watch has been very effective in mitigating crime in my neighborhood.” The survey is administered and survey respondents completed this survey and the aforementioned survey items in particular. The data for these survey items are shown in a tabular form in a data sheet (usually and Excel spread sheet) as shown in Table 7.1. See an exception to tabular data sheet in Box 7.1. The spread sheet displays the data pertaining to the three survey items in columns 1, 2, 3, The last column (“Variable n”) underscores the possibility that the remaining survey items (whatever they are) can be displayed in the same manner in the spread sheet. The rows in the spread sheet capture respondents’ responses to each of the survey items. The spread sheet shows data from respondents 1–6; and the last row (“Respondent n”) underscores the possibility that the remaining respondents (from Respondent 7 to the last respondent) can be displayed in the same manner. Note that each survey item

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Table 7.1 Examples of variables in an excel spread sheet

Respondent 1 Respondent 2 Respondent 3 Respondent 4 Respondent 5 Respondent 6 Respondent n

Variable 1 (neighborhood safety) 1

Variable 2 (effectiveness of law enforcement) 2

Variable 3 (effectiveness of the neighborhood watch) 4

Variable n ...

5

4

5

...

3

3

5

...

4

4

5

...

4

4

5

...

3

5

4

...

5

4

3

represents a “variable.” That is, once the data are tabulated and displayed in a data sheet (readied for data analysis), indicator researchers treat them as statistical variables. As such, a variable is any characteristic, number, or quantity that can be measured or counted. In statistics, we refer to variables as “data items.” Box 7.1: Spatial Data Geographic data are displayed in spatial form. They are displayed in individual or collection of tables. Each table (or collection of tables) reflect data related to a particular geographic area with features (e.g., points of interest such as schools, lines (e.g., bus routes), or polygons (e.g., school catchment areas). Mapping coordinates (i.e., latitude and longitude) accompany attributes related to each feature. Geocoding is used to place mapping coordinates. Specifically, geocoding is the process of transforming a description of a location (e.g., a pair of coordinates) to a location on the earth’s surface. Data are typically analyzed and visualized in a Geographic Information System (GIS). Source: Adapted from Stevens et al. (2007).

Measurement Scales Let’s go back to the survey items we discussed in the preceding section, namely the three items pertaining to neighborhood safety, effectiveness of law enforcement, and effectiveness of the neighborhood watch. Notice the scales that were used to capture respondents’ ratings of these three aspects of their neighborhood. The response

144 Table 7.2 Example of a survey item with an ordinal response scale

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Data Analysis

Neighborhood A ¼ ___ (specify rank: 1–5) Neighborhood B ¼ ___ (specify rank: 1–5) Neighborhood C ¼ ___ (specify rank: 1–5) Neighborhood D ¼ ___ (specify rank: 1–5) Neighborhood E ¼ ___ (specify rank: 1–5) How safe are the following five neighborhoods in your community? Compare the following neighborhoods (Neighborhood A, Neighborhood B, Neighborhood C, Neighborhood D, and Neighborhood E) in terms of neighborhood safety and rank order them using the following scale: 1 ¼ safest, 2 ¼ second most safe, 3 ¼ third most safe, 4 ¼ fourth most safe, and 5 ¼ least safe

scales are essentially 5-point rating scales. Such a scale is called “interval.” Interval scales assumes that the interval between one response category is equivalent to the interval of the other response categories. Let’s examine the variable capturing the neighborhood safety survey item: “How safe is your neighborhood? Please rate your neighborhood in terms of safety using the following scale: 1¼very unsafe, 2¼somewhat unsafe, 3¼so-so, 4¼somewhat safe, and 5¼very safe.” Note in this case that the psychological distance between the first and second response categories (1 ¼ very unsafe and 2 ¼ somewhat unsafe) is equivalent to the second and third categories (2 ¼ somewhat safe and 3 ¼ so-so). Further, the psychological distance between the third and fourth categories (3 ¼ so-so and 4 ¼ somewhat safe) is equivalent to the psychological distance between the first and the second categories and between the second and third categories. Similarly, the psychological distance between the fourth and fifth categories (4 ¼ somewhat safe and 5 ¼ very safe) is equivalent to the psychological distance between the first and the second categories, between the second and third categories, and between the third and fourth categories as well. The ultimate form of an interval scale is called a ratio scale. Ratio scales not only have response categories that are equidistant but also have an “absolute zero” (i.e., each response category on the scale have a definitive meaning using a wellestablished measure such as weight, height, and temperature. Weight is measured in grams, kilograms, etc. (or ounces and pounds); height is measured in centimeters, meters, etc. (or in inches and feet) Temperature is measured in unites of Celsius or Fahrenheit. Distance is measured in meters and kilometers (or feet, yards, and miles). In contrast to the interval and ratio scales, we have ordinal scales. Ordinal scales are similar to interval scales with the difference being that the response categories are simply ranked or ordered; the response categories are not necessarily equidistant from one another. For example, a survey items may ask respondents to rank several neighborhoods in their community in terms of neighborhood safety (see example in Table 7.2. Then we have nominal scales that are very different from ordinal, interval, and ratio scales. Nominal scales are response scales that have response categories that are unrelated or independent from one another. Common examples of survey items that involve nominal scales include sex (“male,” “female,” “other”), marital status

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Table 7.3 Variables with different response scales

Respondent 1 Respondent 2 Respondent 3 Respondent 4 Respondent 5 Respondent 6 Respondent n

Variable 1 (neighborhood safety) 1

Variable 2 (age of respondent) 69

Variable 3 (sex of respondent) M

Variable 4 (rank of one’s neighborhood safety compared to surrounding four neighborhoods) 3

5

54

M

2

3

33

F

3

4

43

M

2

4

47

F

1

3

51

O

2

5

24

F

3

(“single,” “married,” “divorced,” “widowed,” “cohabitating,” “other”), name of neighborhood (“Neighborhood A,” “Neighborhood B,” “Neighborhood C,” “Neighborhood D,” and “Neighborhood E”). Table 7.3 shows variables (survey items) that have different response (measurement) scales. Note that responses to the neighbor safety item in the survey questionnaire (Variable 1 in the data sheet) are captured using an interval scale: “How safe is your neighborhood? Please rate your neighborhood in terms of safety using the following scale: 1¼very unsafe, 2¼somewhat unsafe, 3¼so-so, 4¼somewhat safe, and 5¼very safe.” Responses to the age survey item (Variable 2 in the data sheet) of respondent) are captured on a ratio scale (actual age). Sex of the respondent (Variable 3) is captured using a nominal scale (M ¼ male, F ¼ female, O ¼ other). The last variable (Variable 4) captures the rank of one’s neighborhood compared to four other surrounding neighborhoods. As such, the response scale is ordinal (1 ¼ safest, 2 ¼ second most safe, 3 ¼ third most safe, 4 ¼ fourth most safe, and 5 ¼ least safe). Why do we bother with trying to figure out the nature of the response scale (whether it is ratio, interval, ordinal, or nominal)? The answer is simple. Knowing the response scale related to particular variables is important for statistical analysis, a subject that we will now turn to.

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Statistical Analysis Statisticians usually make an important distinction between descriptive and inferential statistics. This is an important distinction because descriptive statistics imply a different set of statistical techniques than inferential statistics. Descriptive statistics summarize the characteristics of variables in a data sheet. In contrast, inferential statistics are designed to do two things: (1) making estimates about populations, and (2) test hypotheses involving relationships between or among variables. Indicators researchers use both descriptive and inferential statistics to accomplish their data analytic tasks.

Descriptive Statistics Descriptive statistics are designed to describe statistical characteristics of a variable usually in terms of four major types of statistics: (1) measures of frequency (e.g., count and percent), (2) measures f central tendency (e.g., mean, median, and mode), (3) measures of dispersion (e.g., range, variance, and standard deviation), and (4) measures of position (e.g., percentile ranks, quartile ranks).

Measures of Frequency A frequency distribution is the most commonly used descriptive statistic used in community indicators research and most other science disciplines. A frequency measure indicates the number of times a particular response category in a distribution of response categories (values) of a particular variable. For example, in the truncated version of the data sheet shown in Table 7.3 (i.e., in which we have only 7 data points; the full data set contain 2000 respondents), the frequency distribution of Variable 1 (neighborhood safety) can be described as follows: a frequency of 1 who described their neighborhood as “very unsafe or 1”; a frequency of 0 who described their neighborhood as “somewhat unsafe or 2”; a frequency of 2 who described their neighborhood as “so-so or 3”; a frequency of 2 who described their neighborhood as “somewhat safe or 4”; and a frequency of 2 who described their neighborhood as “very safe or 5.” Each of the above frequency figure can easily be converted into percent by dividing a target frequency figure by the sum of all frequencies. For example, what is the percent frequency of those respondents who rated their neighborhood as “very safe or 5”? As indicated above, there were 2 respondents with that rating out of 6 respondents (truncated data sheet). As such, the group represent 33.33% of the entire sample.

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Measures of Central Tendency A measure of central tendency is a summary measure that attempts to describe data of a particular variable with a single value that represents the middle or center of its data distribution. There are three main measures of central tendency: the mode, the median and the mean. Let’s refer back to Table 7.3. In that table we see four variables: neighborhood safety, age of respondent, sex of respondent, and rank of one’s neighborhood compared to surrounding neighborhoods. As reminder, we explained that it is important to keep in mind the nature of the response scale related to each variable: ratio, interval, rank, and nominal. Understanding the nature of the response scale allows the indicators researcher to select appropriate statistical techniques to report summary descriptive statistics of the variables in question. Measures of central tendency are commonly used to report summary statistics of variables with ratio or interval scaled responses. Going back to Table 7.3, these are neighborhood safety (Variable 1) and age of respondent (Variable 2). As such, we can report the mode, median, and mean of these two variables. The mode is the most commonly occurring response in a variable. Table 7.3 does not show the full data set (note that data for only six respondents 1–6 are displayed; there are n respondents). Let’s assume that this particular sample contains data from 2000 community residents (N ¼ 2000). It may be that when we take into account the full data set and compute a mode for Variable 1, the mode may be “4” (i.e., most respondents rated their neighborhood as 4 or “somewhat safe.” Remember the response scale: “Please rate your neighborhood in terms of safety using the following scale: 1¼very unsafe, 2¼somewhat unsafe, 3¼so-so, 4¼somewhat safe, and 5¼very safe.” Of course, we will not get into how these statistics are computed given the fact that this is not a statistics book. Readers who are not familiar with descriptive statistics should gain this rudimentary knowledge by reading statistics material on the internet or simply read a book on statistics. How about the median? Focus on a variable and lay out the scores in a distribution arranged in ascending or descending order. The median is identified as the middle score of this distribution. It is as simple as that. Consider the variable capturing age (Variable 2). If we take all the “age scores” shown in Table 7.3 and those not shown in the table (remember that we have 2000 respondents in the data set but only 7 are displayed in the table, we may find that the median age is 48. Finally, the mean is the sum of the scores in a variable divided by number of observations. Remember we have 2000 respondents in this data set (i.e., 2000 observations). If we go back to the age variable, the sum could be easily computed across the 2000 respondents and then divided by 2000. The mean may be 50 years of age. Indicators researchers should be cognizant of advantages and disadvantages of these three different measures of central tendency. A key disadvantage of the mode is the fact that it may not reflect the center of the score distribution better than the median and mean. This may be due to the possibility that the distribution may be

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bi-modal or multi-modal. When we use the mode, we assume (rightly or wrongly) that the distribution of the scores is unimodal, which, of course may not be the case. The advantage of the median (over the mean) is that it is less influenced by outlier scores (the mean is).

Measures of Dispersion Measures of dispersion serve to provide information about the nature of variation in the scores of a particular variable. Range, standard deviation, and variance are commonly used by indicators researchers. The range is the simplest to understand. It reflects the lowest and highest scores in the distribution of scores of a particular variable. Looking at the age variable in Table 7.3, the truncated version of the data set, we see the highest age is 69 years of age and the lowest being 24. As such, the range is 24–69. The standard deviation is another measure of dispersion. It tells you how spread out the scores are from the mean. A low standard deviation (< + or  1 SD) means the scores are clustered around the mean; conversely, a high standard deviation (> + or 1 2 SD) indicates scores are more spread out. Variance is similar to the standard deviation in the way it captures the spread of the scores. However, it is computed a little differently. Standard deviation is technically the square root of the variance. As such, the standard deviation is more commonly used because the spread is captured in terms of standard unites + or - 1, + or – 2, etc. As you can see, range, standard of deviation, and variance are measures of dispersion that are best used with variables reflecting score data (i.e., items measured with interval or ratio scales). Please keep this mind.

Measures of Position Common measures of position used by indicators researchers include percentiles, terciles, and standard scores (aka, z-scores). Assume that the data of a particular variable are rank ordered (the example we used was Variable 4——rank of one’s neighborhood safety compared to surrounding four neighborhoods) from the smallest to the largest. The values that divide rankordered data of a particular variable into 100 equal parts are called percentiles. For example, the data point at the 50th percentile could be identified and that figure would correspond to the median of that variable. Indicators research commonly use the 50th percentile (i.e., median) to split a variable into two discrete groups, namely high and low. For example, a rank of 2 may be the 50th percentile of Variable 4. Indicators researchers may decide to split this variable into two groups: respondents who perceive their neighborhood to be safer than their surrounding neighborhoods and those who perceive their neighborhood to be less safe. Doing so may allow them to better profile the differences between these two groups in terms of

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demographic, geographic, psychographic, and other community-related characteristics. However, when the distribution of ranks is concentrated in the middle of the distribution, forming high and low groups using the 50th percentile could be problematic. This is due to the fact that many cases in the middle of the distribution may fall into the wrong group. Consequently, indicator researchers divide the ranks into terciles or three groups (first tercile, second tercile, and third tercile)——the first tercile are the cases that make up the bottom third of the distribution, the second tercile making up the second group (smack in the middle of the distribution, and the third tercile making up the third group. A common practice is to compare the first tercile group with the third tercile group along a host of other variables (while deleting the second tercile from the analysis altogether). Doing so reduces the “noise in the data” caused by the middle of the road observations. The position of a particular score in a variable can also be identified using a standard score formulation (i.e., z-score). As such a z-score indicates how many standard deviations a particular score in a variable is from the mean. A z-score less than 0 indicates that the target score is less than the mean; a z-score greater than 0 indicates that the target score is greater than the mean; a z-score equal to 0 indicates that the target score is equal to the mean; a z-score equal to 1 indicates that the target score is 1 standard deviation greater than the mean; a z-score equal to 2 means that the target score is 2 standard deviations greater than the mean, and so on. Going back to Table 7.3 and focusing on Variable 1 (neighborhood safety), we can identify the position of Respondent 1 score in relation to all other respondents. This respondent’s score is “1.” Once all the data are incorporated in the data sheet, a z-score can be computed, and the result may be something like “-2.2 (i.e., 2.2 standard deviations less than the mean). It should be noted that measures of position can and are usually transformed into spatial maps using Geographic Information Systems (GIS) techniques (Haddad, 2009). Using SpaceStat in conjunction with ArcView Software, indicators researchers transform a scatter plot into a map. Here are references that can help the reader develop skills: Anselin (1988, 1992, 2003) and Anselin et al. (2021), Longley et al. (1998), and Marcuse and van Kempen (2002).

Inferential Statistics As previously mentioned, descriptive statistics summarize the characteristics of variables in a data sheet. In contrast, inferential statistics are designed to do two things: (1) making estimates about populations, and (2) test hypotheses involving relationships between or among variables.

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Making Estimates about Populations Making estimates about populations apply mostly to primary data such as surveys because of sampling. That is, primary data derived from surveys are based on samples. In contrast, most secondary data tend to capture the entire population – they are not based on samples. For example, indicators researchers conducting a survey to capture residents’ perceptions of neighborhood safety would make use of inferential statistics to make estimates of neighborhood safety for the entire population of neighborhood residents. How do we make these estimates? We use the sample mean (e.g., respondents’ ratings of neighborhood safety) and the standard deviation to make an interval estimate (a range of ratings of neighborhood safety). We usually refer the interval estimate as confidence interval. Typically, we use a 95% conference interval. This means that if we repeat the survey with a new sample in exactly the same way 100 times, we can expect our estimate to lie within the specified range of values 95 times. Translating this into numbers, the mean of neighborhood safety is computed as 2.3 (on a 5-point rating scale with 1 being “very unsafe” and 5 being “very safe”). By considering the standard deviation of neighborhood safety, we can report that 2.3 is the parameter of the entire neighborhood population with a 95% confidence interval – the range around the mean of 2.3 with a 95% confidence interval is 2.0–2.6.

Hypothesis Testing Although we, as indicators researchers, rely much on descriptive statistics and population estimates to analyze the data and report the results, sometimes we do hypothesis testing. For example, we may be guided by information extracted from a focus group that Neighborhood A has an acute safety problem, which is significantly worse than the other neighborhoods (B, C, D, and E). As such, we may wish to test this hypothesis. We have survey data about residents’ perceptions of safety in their own neighborhood (Variable 1), and we have information about which neighborhood they belong to (Variable 4). The data are shown in Table 7.4. We can test this hypothesis by performing an analysis of variance. The results of this analysis should produce information that tells us whether our hypothesis is supported by the data. The analysis-of-variance results should show that the neighborhood safety mean associated with Neighborhood A is significantly lower than the safety means associated with neighborhood B, C, D, and E. We may have another hypothesis: Older people are likely to report higher levels of safety problems than younger people. Do we have survey data to test this hypothesis? Yes, Table 7.4 shows the age of respondent (Variable 2). We can test this hypothesis using a simple Pearson correlation (or a Simple Regression Analysis). The results should produce a significant negative correlation (or negative beta weight) – the older the person is the lower the neighborhood safety perception.

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Table 7.4 Perceptions of safety in one’s neighborhood and which neighborhood one belongs

Respondent 1 Respondent 2 Respondent 3 Respondent 4 Respondent 5 Respondent 6 Respondent n

Variable 1 (neighborhood safety) 1

Variable 2 (age of respondent) 69

Variable 3 (sex of respondent) M

Variable 4 (which neighborhood) A

5

54

M

B

3

33

F

A

4

43

M

C

4

47

F

C

3

51

O

D

5

24

F

E

How about a hypothesis related to sex differences? We may hypothesize that more women perceive neighborhood safety problems than men. We can test this hypothesis using a simple t-test. The hypothesis should be supported if the t-test results show lower mean of neighborhood safety ratings associated with women respondents compared to men respondents. Notice that the above exercise involved three different inferential statistics tests: Analysis of Variance, Pearson Correlation, and t-test. How did I go about selecting these statistics tests appropriate for each hypothesis? To answer this question, I have to remind the reader of our discussion on measurement scales: ration, interval, ordinal, and nominal scales. To choose the right statistical test, we take note of the measurement scale associated with the variables related to the study constructs. The first hypothesis about neighborhood safety perceptions being significantly lower in Neighborhood A than neighborhoods B, C, D, and E involve two constructs: (1) perception of neighborhood safety which is captured on an interval scale and the resulting data are shown in Table 7.4 as Variable 1; and (2) one’s neighborhood which is captured by having the respondent check off the neighborhood s/he belongs to – a nominal measurement scale (see Variable 4 in Table 7.4). As such, the first hypothesis involves two constructs reflected in variables 1 and 4. The data pertaining to Variable 1 are score data (from an interval scale), whereas the data from Variable 4 are categorical (from a nominal scale). The rule then is that Analysis of Variance is best suited to testing a hypothesized relationship between a variable involving categorical data and another variable involving score data. Regarding Hypothesis 2 (older people are likely to perceive their neighborhood as less safe than younger people), the two constructs are perceptions of neighborhood safety and age (variables 1 and 2 respectively as shown in Table 7.4). The rule is that Pearson Correlation or a Simple Regression Analysis are well-suited to testing a hypothesized relationship between two variables with score data.

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Table 7.5 A decision-making tool to help select appropriate bivariate statistical tests Statistical test t-test

Nominal regression

Type of data inherent in the criterion (dependent) variable Score data (produced from interval or ratio scales) Score data (produced by interval or ratio scales) Score data (produced by interval or ratio scales) Rank data (produced by an ordinal scale) Categorical data (produced by a nominal scale) Score data (produced by interval or ratio scales) Categorical data (produced by a nominal scale); limited to two categories Categorical data (produced by a nominal scale)

Ordinal regression

Rank data (produced by an ordinal scale)

Analysis of variance Pearson Correlationa Spearman Correlationa Chi-Square Simple regression Logistic regression

Type of data inherent in the predictor (or independent variable) Categorical data (produced from nominal scales); limited to two categories Categorical data (produced by nominal scales); 3+ categories Score data (produced by interval or ratio scales) Rank data (produced by ordinal scales) Categorical data (produced by nominal scales) Score data (produced by interval or ratio scales) Score, rank, or categorical data (produced by interval, ration, ordinal, or nominal scales, respectively) Score, rank, or categorical data (produced by interval, ration, ordinal, or nominal scales, respectively) Score, rank, or categorical data (produced by interval, ration, ordinal, or nominal scales, respectively)

a

Pearson and Spearman correlation tests do not make a distinction between dependent (criterion) and independent (predictor) variables

With respect to Hypothesis 3 (women are likely to perceive their neighborhood as less safe than men), the two constructs are perceptions of neighborhood safety and sex (variables 1 and 3 respectively as shown in Table 7.4). The rule is that a t-test is well-suited to test a hypothesized relationship between two variables: one with score data, the other with categorical data. Table 7.5 provides the reader with a handy decision tool of basic statistical test well-suited for hypothesis testing. However, before discussing this decision-making tool, the reader should understand two distinctions: (1) the difference between an independent (predictor) variable and a dependent (criterion) variables, and (2) the difference between bi-variate and multivariate statistics. The main difference between independent and dependent variables is the fact that independent variables are customarily manipulated to examine their effects on the dependent variable. As such, the independent variable involves some sort of experimental treatment which typically translates into nominal scales (producing categorical data). In contrast, the dependent variable is the observed phenomenon, which customarily involves measures using ratio, interval, ordinal, or nominal scales. Consider the following hypothesis: X dose of a drug A is likely to be most effective in treating Symptom 1 of some medical condition (e.g., Diabetes) than Y and Z doses of the same drug. The dependent variable in this hypothesis involves Symptom 1 of the medical condition, whereas the independent variable involves the three different

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doses of drug A. Note that we used a medical research example here, not a community indicators example. This is because the terms independent and dependent variables are terms relegated to experimental research; hence, this terminology is well-accepted in the experimental sciences (medical research included, of course). We don’t do experiments in community indicators research. We typically measure things in the community and try to use statistical techniques to test hypothesis. As such, we use different terms for the dependent and independent variables. The dependent variable is referred to as the “criterion variable,” whereas the independent variable is referred to as the “predictor variable.” For example, a hypothesis that posits that older folk are likely to perceive their neighborhood to be less safe than younger folk involves two variables: a predictor variable and a criterion variable. The predictor variable is age and the criterion variable is perception of neighborhood safety. That is, age is used to predict perception of safety in the neighborhood (the criterion variable or what we call “the phenomenon of interest”). Testing a hypothesis that involves a theoretical link between two variables (predictor and criterion variable) calls for bivariate statistics. But some hypotheses may involve multiple variables. Here is an example: Age is likely to have a negative predictive effect on perceptions of neighborhood safety, amplified by sex. Specifically, the negative relationship between age and safety perceptions is likely to be more evident for women than men. Notice that this hypothesis involves the interaction between two predictor variables (age and sex) and one criterion variable, namely safety perceptions – a total of three variables. Bivariate statistics cannot test hypotheses with more than two variables; multivariate statistics do. Multivariate statistics is much more complex. Unfortunately, we do not have the space in this book to explain pertinent multivariate statistics. Interested readers should consult other books on multivariate statistics (e.g., Barbara, 2017; Johnson & Wichern, 2018; Pituch, 2015; Tabachnick, 2019).

Developing a Composite Index Some community indicators projects have attempted to summarize their data (either primary or secondary data or both) using composite indices.1 There is a variety of approaches in computing composite indices. These include the simple average method, the item-total correlations method, the cost-adjustment method, the balanced method, and the weighting-by-experts method. Please note that the science of composite indicators is highly refined. For a good read of composite indicators in the

1 For an overview of well-known composite quality-of-life indices at the national level, see Hagerty et al. (2001). For examples of community indicators projects that have employed composite indices, see the Living Conditions Index of the City of Amsterdam Monitor (Schyns & Boelhouwer, 2004), and the Charlotte Neighborhoods Quality of Life (Metropolitan Studies Group, 2010).

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making country comparisons using quality-of-life indicators please refer to (Joint Research Centre-European Commission, 2008).

The Simple Average Method This method does not assume differential weights among the specified indicators. Once each indicator is transformed into some uniform scale, the scores across the indicators are simply averaged into a composite. Consider the very popular United Nations’ Human Development Index (HDI). The HDI combines three indicators: health (life expectancy at birth), education (adult literacy and combined gross enrollment ratio) and income (real GDP per capita). These indicators are then transformed into values with a minimum of 0 and a maximum of 1 and averaged across the three indicators (United Nations Development Programme, 1998).

The Item-Total Correlations Method This method assumes that indicators should be weighted before their scores are computed (possibly by summing or averaging) into an overall index score. The itemtotal correlations method assumes that indicators that are more highly correlated to the total scores of all indicators combined should be weighed more than indicators that are less highly correlated. This assumption is based on the notion that indicators should be reliable—show consistency among other indicators. For example, the City of Amsterdam Monitor describe the Living Conditions Index was developed (Schyns & Boelhouwer, 2004). This index involved eight domains (housing, health, purchasing power, leisure activities, mobility, social participation, sport activity, and holiday activity). The index was statistically constructed using nonlinear canonical correlation analysis (a statistical program called OVERALS). The first dimension of the OVERALS solution was used to describe the living conditions in Amsterdam. The dimension has a mean of 0 and a standard deviation of 1. In doing so, the program weighs the indicators in such a manner to maximize the sum of the item-total correlations.

The Cost-Adjustment Method The cost-adjustment method focuses on an established metric such as Gross Domestic Product (GDP) per capita and adjusts the scores by taking into account cost measures (e.g., environmental costs). Consider the Genuine Progress Indicator (GPI) produced by Redefining Progress (Cobb et al., 2000) as an example of the use of this method.

Developing a Composite Index Table 7.6 Comparing Balanced Sustainability Scores across U.S Major Metropolitan Areas

155 Metropolitan region Raleigh-Durham, NC Houston, TX Baltimore, MD Orange County, CA United States Austin-San Marcos, TX Minneapolis-St. Paul, MN Sacramento, CA Portland-Salem, OR-WA Atlanta, GA Washington, DC San Diego, CA Phoenix-Mesa, AZ Boston, MA-NH Denver-Boulder, CO Tampa-St. Pete., FL Seattle-Tacoma, WA Pittsburgh, PA Miami-Ft. Lauderdale, FL San Jose, CA Norfolk-VA Beach, VA San Francisco, CA

Variance 29.6 40.2 113.6 169.6 626.0 644.2 910.9 1110.2 1317.6 1429.6 1754.0 1784.0 1784.2 1793.6 2046.9 2117.6 2166.0 2220.2 2546.9 3256.9 6520.2 6696.0

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

Source: Adapted from Jarosz and Williams (2004, p. 202)

The GPI computes an initial factor made up of personal consumption, capital investment, government spending, and next exports. Then other factors are used to adjust the GPI score (social, environmental, and economic factors that diminish or enhance quality of life such as environmental costs, costs of crime, and contributions made by unpaid housework and childcare).

The Balanced Method Some indicators researchers are concerned about the balance among the major dimensions that make up the composite index. To address the balance issue (or perhaps imbalance problem), variance is estimated and used as a measure of balance. The larger the variance the greater the imbalance. To address this problem, the geographic units that are being compared using the composite index are ranked ordered on their variance with the lowest variance being ranked highest. Here is an example described in Jarosz and Williams (2004) comparing different metropolitan areas using the balanced method (see Table 7.6).

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The Weighting-by-Experts Method Some indicators researchers have used experts in weighting the sub-indices that are used to compute the overall composite score. Specifically, experts specializing in a topical area related to a sub-index (e.g., environmental scientists focusing on the environmental wellbeing dimension of quality of life; economists focusing on the economic wellbeing dimension of quality of life, sociologists focusing on the social wellbeing dimension of quality of life) are asked to rate the importance of each indicator in the sub-index related to their specialty expertise on a 10-point importance rating scale (from 1 ¼ unimportant to 10 ¼ extremely important). They are asked to rate the importance of each indicator in explaining changing patterns and trends related to the overall sub-index (i.e., quality-of-life dimension) of the region in question. These ratings are then averaged across experts within each sub-index and normalized on a .0–1.0 scale (lower scores indicating lower importance and vice versa). The weights pertaining to each indicator is then multiplied by each indicator score and averaged across all indicators to compute an overall sub-index score. Once this is done, other experts specializing in the general field of study related to quality of life would do the same in relation to the sub-indices. That is, each sub-index is then rated by its importance in explaining changing patterns and trends related to quality of life overall in the region in question. Again, these importance ratings are then averaged across experts within each sub-index and normalized on a .0–1.0 scale (lower scores indicating lower importance and vice versa). These ratings are then averaged across experts within each sub-index and normalized on a .0–1.0 scale (lower scores indicating lower importance and vice versa). The weights pertaining to each sub-index is then multiplied by each sub-index score and averaged across all sub-indices to compute an overall quality-of-life score for that region. See example of the Hong Kong Social Development Index (Estes, 2004). Weighted Social Development Index for Hong Kong 2000¼ Average of (Science & Technology Sub-index score X .70), (Education Sub-index score X .88), (Arts & Entertainment Sub-index score X .67), (Internationalization Sub-index score X .70), (Health Sub-index score X .83), (Personal Safety Sub-index score X .76), (Strength of Civil Society Sub-index score X .79), (Political Participation Sub-index X .75), (Housing Sub-index score X .82), (Crime & Public Safety Sub-index X .81), (Economic Sub-index score X .87), (Sports & Recreation Sub-index score X .62), (Environmental Quality Sub-index X .83), and (Family Solidarity Sub-index X .71).

Data Mining Data mining refers to the analysis of the large quantities of data saved on data bases. Data mining has been heavily used in the medical field using patient medical records. For example, the Mayo Clinic collaborated with IBM to develop an online computer

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system to identify how patients with the same gender, age, and medical history had responded to particular treatments (Swartz, 2004). Data mining has gained much momentum in marketing. Marketers have used data mining techniques to develop customer profiles (subset of customers most profitable to the firm). These profiles would then be used to develop more personalized and effective marketing communication to motivate these customers to purchase the firm’s offering. Data mining techniques have also been used to identify profitable customers who have been captured by competitor firms in attempt to target them through promotion and persuade them to switch to the firm’s brand (Linoff, 2004). Traditional statistical analysis involves for the most part hypothesis testing as described in the previous section. Hypothesis testing is theory driven. We can characterize this approach to data analysis as “top-down.” In contrast, data mining is involves a” bottom-up” approach to data analysis. It is not driven by theory; it is driven by data, period. A variety of analytic techniques have been used in data mining. Common data mining techniques include regression analysis (normal regression for prediction, logistic regression for classification), neural networks, and decision trees. More sophisticated techniques include association rules for initial data exploration, fuzzy data mining approaches, rough set models, support vector machines, and genetic algorithms (Olson & Delen, 2008). In this section we’ll concentrate on addressing the common, not advanced, data mining techniques such as neural networks and decision trees. We already touched on the use of regression analysis in the previous section. In a data mining context, regression analysis is used to make predictions. A large number of predictor variables are entered into a regression equation to account for variation in a criterion variable. For example, going back to perceptions of neighborhood safety, instead of testing specific hypotheses as we did in the previous section, data mining in this instance involves identifying and using all of most of the variables used in the survey – demographic, psychographic, geographic, political, technological, socio-cultural, consumption and work-related variables. This is multiple regression at work. The regression produces results showing which predictor variables entered in the regression equation accounts for how much of the variance in perceptions of neighborhood safety. It is as simple as that. “Neural networks” is a concept based on a brain metaphor for information processing. Neural computing refers to a method for classification, clustering, feature mining, prediction, and pattern recognition. There are several neural network models well-established in the data mining literature: multilayered feedforward neural networks, Hopefield neural networks, and self-organizing neural networks (Smith & Gupta, 2000). We will limit our discussion to multilayered feedforward neural networks (MFNN) to provide the reader a very basic understanding of how the neural network concept is used in data mining in a community indicators context. This MFNN technique solves problems that involve learning relationships between a set of inputs and known outputs. It is referred to as “supervised learning” because the technique requires a set of training data to learn the relationships. The MFNN architecture consists of two or more layers of neurons connected by weights. The

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flow of information is from left to right with inputs x being passed through the network via hidden layer of neurons to the output layer. Each hidden neuron computes its output determined by the amount of stimulation it receives from a vector of inputs. As such, the network produces an output (or a set of outputs) from a given pattern of inputs. The output is then compared to the known desired response. The weights of the network are then modified to reduce the error, and the next input pattern is presented in a continuous cycle in which the weights are continually adjusted until the total error across all training patterns is reduced to some tolerance level. The algorithm for determining the optimal weights for a given training set operates like a regression, where the weights are analogous to regression coefficients. Applying neural networks to community indicators, we can treat a set of outputs to variables capturing various aspects of neighborhood safety (perceptions, evaluations, and behaviors related to neighborhood safety and crime). The goal is to continuously predict these output variables by new information related to the same set of inputs through a process of continuous iterations. Until the prediction error is reduced to some tolerance level.

Summary This chapter covered a wide range of topics related to data analyses commonly used in indicators projects. We started out the chapter by describing what a variable is and distinguishing this concept from a measurement scale. A variable is any characteristic, number, or quantity that can be measured or counted. Scales are used to capture respondents’ ratings to, for example, aspects of neighborhood. We described four forms of measurement scales: ratio, interval, ordinal, and nominal. Interval scales assumes that the interval between one response category is equivalent to the interval of the other response categories. Ratio scales not only have response categories that are equidistant but also have an “absolute zero” (i.e., each response category on the scale have a definitive meaning using a well-established measure such as weight, height, and temperature). Ordinal scales are similar to interval scales with the difference being that the response categories are simply ranked or ordered; the response categories are not necessarily equidistant from one another. Nominal scales are response scales that have response categories that are unrelated or independent from one another (e.g., sex is measured in terms of three response categories: “male,” “female,” and “other”). Identifying the measurement scale (whether it is ratio, interval, ordinal, or nominal) is important for statistical analysis. We then made the distinction between descriptive and inferential statistics. Descriptive statistics summarize the characteristics of variables. In contrast, inferential statistics are designed to achieve two objectives: making estimates about populations and test hypotheses involving relationships between or among variables. We described four major types of descriptive statistics: measures of frequency (e.g., count and percent); measures of central tendency (e.g., mean, median, and

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mode); measures of dispersion (e.g., range, variance, and standard deviation); and measures of position (e.g., percentile ranks, quartile ranks). A frequency measure indicates the number of times a particular response category in a distribution of response categories (values) of a particular variable. Frequency figures can easily be converted into percent by dividing a target frequency figure by the sum of all frequencies. A measure of central tendency is a summary measure that attempts to describe data of a particular variable with a single value that represents the middle or center of its data distribution. There are three main measures of central tendency: the mode, the median and the mean. The mode is the most commonly occurring response in a variable. The median is identified as the middle score of this distribution. The mean is the sum of the scores in a variable divided by number of observations. A key disadvantage of the mode is the fact that it may not reflect the center of the score distribution better than the median and mean. This may be due to the possibility that the distribution may be bi-modal or multi-modal. When we use the mode, we assume (rightly or wrongly) that the distribution of the scores is unimodal, which, of course may not be the case. The advantage of the median (over the mean) is that it is less influenced by outlier scores (the mean is). Measures of dispersion serve to provide information about the nature of variation in the scores of a particular variable. The range reflects the lowest and highest scores in the distribution of scores of a particular variable. The standard deviation informs us how spread out the scores are from the mean. A low standard deviation (< + or 1 SD) means the scores are clustered around the mean; conversely, a high standard deviation (> + or 1 2 SD) indicates scores are more spread out. Variance is similar to the standard deviation in the way it captures the spread of the scores. However, it is computed a little differently. Standard deviation is technically the square root of the variance. As such, the standard deviation is more commonly used because the spread is captured in terms of standard unites + or – 1, + or – 2, etc. Common measures of position used by indicators researchers include percentiles, terciles, and standard scores (aka, z-scores). The values that divide rank-ordered data of a particular variable into 100 equal parts are called percentiles. The position of a particular score in a variable can also be identified using a standard score formulation (i.e., z-score). As such a z-score indicates how many standard deviations a particular score in a variable is from the mean. A z-score less than 0 indicates that the target score is less than the mean; a z-score greater than 0 indicates that the target score is greater than the mean; a z-score equal to 0 indicates that the target score is equal to the mean; a z-score equal to 1 indicates that the target score is 1 standard deviation greater than the mean; a z-score equal to 2 means that the target score is 2 standard deviations greater than the mean, and so on. Descriptive statistics summarize the characteristics of variables. In contrast, inferential statistics are designed to achieve two objectives: making estimates about populations and test hypotheses involving relationships between or among variables. Making estimates about populations apply mostly to primary data such as surveys because of sampling. That is, primary data derived from surveys are based on samples. In contrast, most secondary data tend to capture the entire population– they are not based on samples. We make these estimates by using the sample mean

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and the standard deviation to make an interval estimate (i.e., confidence interval— we usually use 95%--i.e., if we repeat the survey with a new sample in exactly the same way 100 times, we can expect our estimate to lie within the specified range of values 95 times). Indicators researchers also are charged to test specific hypotheses. Hypotheses tend to focus on testing relationships between variables. There are many inferential statistics commonly used by indicators researchers: t-test, analysis of variance, Pearson correlation, Spearman correlation, Chi-square, simple regression, logistic regression, nominal regression, and ordinal regression. To choose the right statistical test, we take note of the measurement scale associated with the variables (ratio, interval, ordinal, and nominal). For example, analysis of variance is best suited to testing a hypothesized relationship between a variable involving categorical data (nominal scale) and another variable involving score data (interval or ratio scale). Pearson correlation or a simple regression analysis are well-suited to testing a hypothesized relationship between two variables with score data (interval or ratio scales). A t-test is well-suited to test a hypothesized relationship between two variables: one with score data (interval or ratio scale), the other with categorical data (nominal scale). To aid in selecting the right inferential statistical method, we also made a distinction between independent (predictor) variables and dependent (criterion) variables. Additionally, we made the distinction between bi-variate and multivariate statistics. The main difference between independent (predictor) and dependent (criterion) variables is the fact that independent variables are customarily manipulated to examine their effects on the dependent variable. Testing a hypothesis that involves a theoretical link between two variables (predictor and criterion variable) calls for bivariate statistics. But some hypotheses may involve multiple variables, hence the need for multivariate statistical methods. Composite indices can also be constructed. There is a variety of approaches in computing composite indices. These include the simple average method, the itemtotal correlations method, the cost-adjustment method, the balanced method, and the weighting-by-experts method. The simple average method involves computing a simple average after each indicator is transformed into some uniform scale. The item-total correlation method assumes that indicators should be weighted before their scores are computed (possibly by summing or averaging) into an overall index score. The item-total correlations method assumes that indicators that are more highly correlated to the total scores of all indicators combined should be weighed more than indicators that are less highly correlated. The cost-adjustment method focuses on an established metric such as Gross Domestic Product (GDP) per capita and adjusts the scores by taking into account cost measures (e.g., environmental costs). The balanced method involves estimating the variance among the major quality-of-life dimensions making up the composite. The larger the variance among the dimensions the greater the imbalance. To address this problem, the geographic units that are being compared using the composite index are ranked ordered on their variance with the lowest variance being ranked highest. Finally, the

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weighting-by-experts method involves the use of experts in weighting the indicators and sub-indices making up the composite index. Data mining refers to the analysis of the large quantities of data saved in data bases. Traditional statistical analysis involves for the most part hypothesis testing. Hypothesis testing is theory driven--top-down approach to data analysis. In contrast, data mining involves a bottom-up approach to data analysis. It is not driven by theory; it is driven by data, period. Common data mining techniques include regression analysis (normal regression for prediction, logistic regression for classification), neural networks, and decision trees. More sophisticated techniques include association rules for initial data exploration, fuzzy data mining approaches, rough set models, support vector machines, and genetic algorithms. 6.Progress Check 1. What are variables in statistical terms? 2. What are measurement scales in statistical terms? 3. What is the distinction between descriptive and inferential statistics 4. What are commonly used descriptive statistics in indicators projects? 5. What are frequency and percent descriptive statistics? 6. What are commonly used descriptive statistics capturing central tendency? 7. What are some advantages and disadvantages related to central tendency descriptive statistics? 8. What are commonly used descriptive statistics capturing data dispersion? 9. What are commonly used descriptive statistics capturing position? 10. What are common objectives of inferential statistics? 11. What are inferential statistical methods Indicators commonly used by indicators researchers to test hypotheses? 12. How do indicators researchers go about selecting the proper inferential statistical technique to test hypotheses? 13. What is the distinction between independent and dependent variables? Predictor and criterion variables? How is this distinction helpful in hypothesis testing? 14. How are composite indices constructed? 15. What is data mining and how is data mining used in indicators projects? 7.Progress Check Answers 1. What are variables in statistical terms? A variable is any characteristic, number, or quantity that can be measured or counted. 2. What are measurement scales in statistical terms? There are four forms of measurement scales: ratio, interval, ordinal, and nominal. Interval scales assumes that the interval between one response category is equivalent to the interval of the other response categories. Ratio scales not only have response categories that are equidistant but also have an “absolute zero” (i.e., each response category on the scale have a definitive meaning using a wellestablished measure such as weight, height, and temperature). Ordinal scales are similar to interval scales with the difference being that the response categories are simply ranked or ordered; the response categories are not necessarily equidistant from one another. Nominal scales are response scales that have

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response categories that are unrelated or independent from one another (e.g., sex is measured in terms of three response categories: “male,” “female,” and “other”). Identifying the measurement scale (whether it is ratio, interval, ordinal, or nominal) is important for statistical analysis. What is the distinction between descriptive and inferential statistics? Descriptive statistics summarize the characteristics of variables. In contrast, inferential statistics are designed to achieve two objectives: making estimates about populations and test hypotheses involving relationships between or among variables. What are commonly used descriptive statistics in indicators projects? There are four major types of descriptive statistics: measures of frequency (e.g., count and percent); measures of central tendency (e.g., mean, median, and mode); measures of dispersion (e.g., range, variance, and standard deviation); and measures of position (e.g., percentile ranks, quartile ranks). What are frequency and percent descriptive statistics? A frequency measure indicates the number of times a particular response category in a distribution of response categories (values) of a particular variable. Frequency figures can easily be converted into percent by dividing a target frequency figure by the sum of all frequencies. What are commonly used descriptive statistics capturing central tendency? A measure of central tendency is a summary measure that attempts to describe data of a particular variable with a single value that represents the middle or center of its data distribution. There are three main measures of central tendency: the mode, the median and the mean. The mode is the most commonly occurring response in a variable. The median is identified as the middle score of this distribution. The mean is the sum of the scores in a variable divided by number of observations. What are some advantages and disadvantages related to central tendency descriptive statistics? A key disadvantage of the mode is the fact that it may not reflect the center of the score distribution better than the median and mean. This may be due to the possibility that the distribution may be bi-modal or multimodal. When we use the mode, we assume (rightly or wrongly) that the distribution of the scores is unimodal, which, of course may not be the case. The advantage of the median (over the mean) is that it is less influenced by outlier scores (the mean is). What are commonly used descriptive statistics capturing data dispersion? Measures of dispersion serve to provide information about the nature of variation in the scores of a particular variable. Measures of dispersion include range, standard deviation, and variance. The range reflects the lowest and highest scores in the distribution of scores of a particular variable. The standard deviation informs us how spread out the scores are from the mean. A low standard deviation (< + or  1 SD) means the scores are clustered around the mean; conversely, a high standard deviation (> + or 1 2 SD) indicates scores are more spread out. Variance is similar to the standard deviation in the way it captures the spread of the scores. However, it is computed a little differently. Standard

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deviation is technically the square root of the variance. As such, the standard deviation is more commonly used because the spread is captured in terms of standard unites + or  1, + or  2, etc. What are commonly used descriptive statistics capturing position? Common measures of position used by indicators researchers include percentiles, terciles, and standard scores (aka, z-scores). The values that divide rank-ordered data of a particular variable into 100 equal parts are called percentiles. The position of a particular score in a variable can also be identified using a standard score formulation (i.e., z-score). As such a z-score indicates how many standard deviations a particular score in a variable is from the mean. A z-score less than 0 indicates that the target score is less than the mean; a z-score greater than 0 indicates that the target score is greater than the mean; a z-score equal to 0 indicates that the target score is equal to the mean; a z-score equal to 1 indicates that the target score is 1 standard deviation greater than the mean; a z-score equal to 2 means that the target score is 2 standard deviations greater than the mean, and so on. What are common objectives of inferential statistics? Descriptive statistics summarize the characteristics of variables. In contrast, inferential statistics are designed to achieve two objectives: making estimates about populations and test hypotheses involving relationships between or among variables. Making estimates about populations apply mostly to primary data such as surveys because of sampling. That is, primary data derived from surveys are based on samples. In contrast, most secondary data tend to capture the entire population–they are not based on samples. We make these estimates by using the sample mean and the standard deviation to make an interval estimate (i.e., confidence interval—we usually use 95%--i.e., if we repeat the survey with a new sample in exactly the same way 100 times, we can expect our estimate to lie within the specified range of values 95 times). What are inferential statistical methods Indicators commonly used by indicators researchers to test hypotheses? Hypotheses tend to focus on testing relationships between variables. There are many inferential statistics commonly used by indicators researchers: t-test, analysis of variance, Pearson correlation, Spearman correlation, Chi-square, simple regression, logistic regression, nominal regression, and ordinal regression. How do indicators researchers go about selecting the proper inferential statistical technique to test hypotheses? To choose the right statistical test, we take note of the measurement scale associated with the variables (ratio, interval, ordinal, and nominal). For example, analysis of variance is best suited to testing a hypothesized relationship between a variable involving categorical data (nominal scale) and another variable involving score data (interval or ratio scale). Pearson correlation or a simple regression analysis are well-suited to testing a hypothesized relationship between two variables with score data (interval or ratio scales). A t-test is well-suited to test a hypothesized relationship between two variables: one with score data (interval or ratio scale), the other with categorical data (nominal scale).

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13. What is the distinction between independent and dependent variables? Predictor and criterion variables? How is this distinction helpful in hypothesis testing? To aid in selecting the right inferential statistical method, we also made a distinction between independent (predictor) variables and dependent (criterion) variables. Additionally, we made the distinction between bi-variate and multivariate statistics. The main difference between independent (predictor) and dependent (criterion) variables is the fact that independent variables are customarily manipulated to examine their effects on the dependent variable. Testing a hypothesis that involves a theoretical link between two variables (predictor and criterion variable) calls for bivariate statistics. But some hypotheses may involve multiple variables, hence the need for multivariate statistical methods. 14. How are composite indices constructed? Composite indices can also be constructed. There is a variety of approaches in computing composite indices. These include the simple average method, the item-total correlations method, and the cost-adjustment method. The simple average method involves computing a simple average after each indicator is transformed into some uniform scale. The item-total correlation method assumes that indicators should be weighted before their scores are computed (possibly by summing or averaging) into an overall index score. The item-total correlations method assumes that indicators that are more highly correlated to the total scores of all indicators combined should be weighed more than indicators that are less highly correlated. The costadjustment method focuses on an established metric such as Gross Domestic Product (GDP) per capita and adjusts the scores by considering cost measures (e.g., environmental costs). The balanced method involves estimating the variance among the major quality-of-life dimensions making up the composite. The larger the variance among the dimensions the greater the imbalance. To address this problem, the geographic units that are being compared using the composite index are ranked ordered on their variance with the lowest variance being ranked highest. Finally, the weighting-by-experts method involves the use of experts in weighting the indicators and sub-indices making up the composite index. 15. What is data mining and how is data mining used in indicators projects? Data mining refers to the analysis of the large quantities of data saved in data bases. Traditional statistical analysis involves for the most part hypothesis testing. Hypothesis testing is theory driven--top-down approach to data analysis. In contrast, data mining involves a bottom-up approach to data analysis. It is not driven by theory; it is driven by data, period. Common data mining techniques include regression analysis (normal regression for prediction, logistic regression for classification), neural networks, and decision trees. More sophisticated techniques include association rules for initial data exploration, fuzzy data mining approaches, rough set models, support vector machines, and genetic algorithms.

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References Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publishers. Anselin, L. (1992). SpaceStat tutorial. University of Illinois. Anselin, L. (2003). GeoDa 0.9 user’s guide. Spatial analysis laboratory, University of Illinois. Anselin, L., Li, X., & Koschinsky, J. (2021). GeoDa, from the desktop to an ecosystem for exploring spatial data. Geographical Analysis. Barbara, F. (2017). Using multivariate statistics (6th ed.). Pearson India. Cobb, C., Goodman, G., & Kliejunas, J. (2000). Blazing sun overhead and clouds on the horizon. The Genuine Progress Report for 1999. Redefining Progress. Retrieved November 1, 2003 from http://www.rprogress.org/projects/gpi Estes, R. (2004). Toward a social development index for Hong Kong: The process of community engagement. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 209–234). Kluwer Academic Publishers. Haddad, M. A. (2009). Examining the spatial distribution of urban indicators in Sao Paulo, Brzil: Do spatial effects matter? In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-oflife indicators: Best cases III (pp. 99–121). Springer. Hagerty, M. R., Cummins, R., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., Sharpe, S., & M. J. & Vogel, J. (2001). Quality of life indexes for national policy: Review and agenda for research. Social Indicators Research, 55(1), 1–96. Jarosz, B., & Williams, M. D. (2004). Creating an index to evaluate a region’s competitiveness. In M. J. Sirgy, D. Rahtz, & D.-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 183–207). Kluwer Academic Publishers. Johnson, R., & Wichern, D. (2018). Applied multivariate statistical analysis. Pearson. Joint Research Centre-European Commission. (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing. Linoff, G. S. (2004). Survival data mining for customer insight. Intelligent Enterprise, 7(12), 28–33. Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (Eds.). (1998). Geographical information systems: Principles, techniques, management and applications. Wiley. Marcuse, P., & van Kempen, R. (Eds.). (2002). Of states and cities: The partitioning of urban space. Oxford University Press. Metropolitan Studies Group (2010). Charlotte Neighborhood Quality of Life Study. Retrieved March 22, 2022, from Microsoft Word - 2010_5_19_Quality_of_Life_Report.doc (charlottenc.gov) Olson, D. L., & Delen, D. (2008). Advanced data mining techniques. Springer. Pituch, K. A. (2015). Applied multivariate statistics for the social sciences. Routledge. Schyns, P., & Boelhouwer, J. (2004). The state of the city Amsterdam monitor: Measuring quality of life in Amsterdam. In M. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators (pp. 133–152). Springer. Smith, K. A., & Gupta, J. N. D. (2000). Neural networks in business: Techniques and applications for operations researchers. Computer & Operations Research, 27, 1023–1044. Stevens, D., Dragicevic, S., & Rothley, K. (2007). iCity: A GIS–CA modelling tool for urban planning and decision making. Environmental Modelling & Software, 22(6), 761–773. Swartz, N. (2004). IBM, Mayo clinic to mine medical data. The Information Management Journal, 38(6), 8. Tabachnick, B. G. (2019). Using multivariate statistics. Pearson Education. United Nations Development Programme. (1998). Human Development Report 1998. Oxford University Press.

Chapter 8

Data Reporting

Learning Objectives After reading this chapter you should be able to answer the following questions: 1. 2. 3. 4. 5.

What is the purpose of indicators reports? What does the public report include? What are popular formats used in preparing the public report? What does the research report include? How to report results in actionable terms?

Introduction In this chapter we will describe aspects related to data reporting--tasks related to publishing the indicators report. The indicators report serves to inform community planners and other leaders about the current versus past state of community quality of life. Such information should help decision makers assess the impact of certain programs and policies on community quality of life and make new decisions accordingly. The report also serves to document the concepts, methods, and measures used in the indicators project. Such documentation serves to help community leaders as well as the general public better understand the reported data. The documentation also serves future community indicator researchers conduct future studies by replicating and extending on the current research. From that vantage point, we typically prepare two reports: a public report and a research report. The public report is a condensed version of the research report. The research report is much more technical and is written for community indicator researchers and other technical people who desire to examine the concepts, methods, and measures guiding the project.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_8

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The Public Report The public report is designed to disseminate the results of the indicators project to get the broadest exposure possible within the community. Ideally, every community resident should have access to the public report to help them understand the qualityof-life profile of their own community. The same report is also directed to other stakeholders such as the media, business leaders, educators, and locally elected and appointed officials.

Content As previously stated, the public report is, in essence, a condensed version of the research report. As such we select only a few elements of the research report to include in the public report. What are these elements? The public report includes an explanation of what is community quality of life and how it was measured in the indicators project. The report also includes basic information about each indicator and its trends. Finally, the report includes interpretation of the trends and the need for change (if needed). Recommended change, of course, comes in the form of recommended community programs and policies.

Format Because it is designed for public consumption, the public report is usually easily readable and visually striking. That is, the report format involves minimal text, large headings, short definitions and explanations, and short tables and clear and striking graphs (see Box 8.1). Here is a list of formats commonly used (Steven et al., 2021, pp. 62–63): • • • • • • • • • • • • •

Article Billboard Blog post Charts and graphs Dashboard Digital interactive Editorial Handheld interactive Improv Maps Marketing and advertisements Microsite (standalone website) Music

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Newsletter Podcast Presentation Press release Scorecard Short-form report Social media post Infographic Interview Landing page on a website Long-form report Text message Theatre Video Webinar Box 8.1: Minnesota Compass and the Baltimore Neighborhood Indicators Alliance The Minnesota Compass (https://www.mncompass.org/) indicators project is a best practice case in data reporting. The data is reported visual and in a customized form. The site is replete with tables, figures, and maps labeled clearly in terms of description of geography, time period of the data, and information about the groups represented. This is for those who are only seeking a cursory glance. The site has “Data & Notes” for those interested in greater detail. What is most interesting about the Minnesota Compass website is ability to provide the visitor with a customized view (digital interactive). That is, the visitor can select certain geographic regions and certain indicators for display. The Baltimore Neighborhood Indicators Alliance (https://bniajfi.org/) is equally compelling. The project produces an annual scorecard containing many community indicators (100–125 indicators). The website includes an interactive portal with mapping and data visualization. Source: Adapted from Stevens et al. (2021, p. 63)

The Research Report As previously stated, the research is important for the research people and other technical staff. It is designed to help researchers replicate and extend on the current research in future studies. Further, the research report is designed to help the staff (and others who are involved with the indicators project such as the chairperson, steering committee members, task force members, sponsoring organizations, funders, etc.) answer queries about the concepts, methods, and measures used in

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the project. In addition to those constituencies, the research report should be disseminated to the community public and academic libraries, local planning agencies, local government agencies, and economic-development organizations, including Chambers of Commerce. See Box 8.2: an example of an indicators project that made a clear distinction between public and research reports. Box 8.2: The Central New York (CNY) Vitals Indicators Project: Data Dissemination Indicators researchers at CNY Vitals (https://cnyvitals.org/) have long recognized that different people in the area and stakeholders have different information needs about their community. As such, the CNY Vitals’ website was designed with two audiences in mind: a general audience section and a separate login-only section for “pro” users. Source: Adapted from Stevens et al. (2021, p. 80) How should the research report be structured? Typical research reports include the following: • A title page; • An acknowledgement (recognition of people and/or organizations—especially volunteers and contributors to the indicators project); • Preface (a personal statement about the use, misuse, and abuse of the indicators project; • A table of content; • An executive summary of the entire project; • Overall purpose of the indicators project; • Background information about the community (perhaps a demographic profile, the geography of the community, and its governance); • Community quality-of-life concepts guiding the selection of the major community quality-of-life dimensions (these may be developed through a theoretical model—a top-down process—or derived from visioning process involving major stakeholders in the community—a bottom-up process); • Selection of the quality-of-life indicators related to the selected dimensions: • Secondary data – Method—procedure used to secure statistics from different secondary information sources; – Results—tables and figures capturing summary statistics and trends based on secondary data (with narrative and data sources referenced); – Discussion—interpreting the secondary data results and making recommendations for community action;

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• Primary data – Selection of indicators based on primary data (survey measures) – Method—procedure used to administer the survey to community residents (sampling, data collection, and measure validation) – Results—tables and figures capturing survey results (with narrative) – Discussion—interpreting the primary data (survey) results and making recommendations for community action • Conclusion—summary recommendations for community action • References—any scientific and technical resources used to make various decisions throughout the project as cited in the body of the report • Appendices—technical and detailed information that was not included in the body of the research report such as the survey questionnaire, data sources, definitions, etc.

Translating Outcome Indicators into Action Indicators As previously mentioned, indicators projects are designed not only to monitor the quality of life of a community but also to help community leaders identify strengths and weaknesses for future action. That is, indicators projects have to be actionoriented. As such, there is a need to translate outcome indicators (sometimes referred to as “system indicators”) into action indicators (sometimes referred to as “performance indicators,” “program indicators,” “policy indicators,” “process indicators,” and “input/process/output indicators”. The steering committee of an indicators project collaborates with a specific constituency (e.g., environmental organizations). As such, the steering committee is obligated to deliver specific constituencies information about the wellbeing in terms of indicators that these groups translate into programs and policies to improve the wellbeing of their own people. To reiterate, outcome indicators provide feedback about the overall well-being of the community. In contrast, action indicators provide policymakers and other decision makers with feedback about the success of specific programs and policies. For example, compliance rates for permitted air emissions can be viewed as an action indicator related to air pollution, the latter being an outcome indicator. Compliance with policies of air emissions is an action that leads to the outcome of reduction of overall air pollution. Teenage pregnancy rate is an outcome indicator; awareness of the negative effects of pregnancies among teenagers can be construed as an action indicator. In other words, educating teenagers about the adverse social, economic, and health effects of pregnancy should lead to a reduction in teenage pregnancy rates. Crime rate is considered an outcome indicator. Number of police officers per 1000 residents may be considered an action indicator. The assumption here is that the more police officers are patrolling the streets and enforcing the law the lower the overall crime rate in the community.

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Summary This chapter described the purpose of preparing two reports: a public report and a research report. The purpose of these two reports is to inform community planners and other leaders about the current versus past state of community quality of life. Such information should help decision makers assess the impact of certain programs and policies on community quality of life and make new decisions accordingly. The public report is a condensed version of the research report. The research report is much more technical and is written for community indicator researchers and other technical people who desire to examine the concepts, methods, and measures guiding the project. Specifically, the public report is designed to disseminate the results of the indicators project to get the broadest exposure possible within the community. Ideally, every community resident should have access to the public report to help them understand the quality-of-life profile of their own community. The same report is also directed to other stakeholders such as the media, business leaders, educators, and locally elected and appointed officials. The content of the public report contains only a few elements of the research report: an explanation of what is community quality of life and how it was measured in the indicators project; basic information about each indicator and its trends; and interpretation of the trends and recommended changes in terms of programs and policies. The public report is usually easily readable and visually striking. That is, the report format involves minimal text, large headings, short definitions and explanations, and short tables and clear and striking graphs. There are many formatting elements used in preparing and disseminating the public report. These include article, billboard, blog post, charts and graphs, dashboard, digital interactive, editorial, handheld interactive, maps, marketing and advertisements, microsite (standalone website), music, newsletter, podcast, presentation, press release, scorecard, short-form report, social media post, infographic, interview, landing page on a website, text message, theatre, video, and webinar. In contrast, the research report is written for the research people and other technical staff. It is designed to help researchers replicate and extend on the current research in future studies. Further, the research report is designed to help the staff (and others who are involved with the indicators project such as the chairperson, steering committee members, task force members, sponsoring organizations, funders, etc.) answer queries about the concepts, methods, and measures used in the project. It is structured to reflect the following: a title page; an acknowledgement section; preface; a table of content; an executive summary; overall purpose of the indicators project; background information about the community; the key community quality-of-life concepts guiding the selection of the indicators; selection of the indicators; method; results; discussion (the latter three sections can be further divided in terms of primary versus secondary data); conclusion; references; and appendices.

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Finally, we discussed the need for both public and research reports to be actionoriented. As such, outcome indicators (sometimes referred to as “system indicators”) are commonly translated into action indicators (sometimes referred to as “performance indicators,” “program indicators,” “policy indicators,” “process indicators,” and “input/process/output indicators.” To reiterate, outcome indicators provide feedback about the overall well-being of the community. In contrast, action indicators provide policymakers and other decision makers with feedback about the success of specific programs and policies. 6.Progress Check 1. What is the purpose of indicators reports? 2. What does the public report include? 3. What are popular formats used in preparing the public report? 4. What does the research report include? 5. How to report results in actionable terms? 7.Progress Check Answers 1. What is the purpose of indicators reports? Indicators reports (public and research reports) serve to inform community planners and other leaders about the current versus past state of community quality of life. The research report also serves to document the concepts, methods, and measures used in the indicators project. Such documentation serves to help community leaders as well as the general public to better understand the reported data. The documentation also serves future indicator researchers conduct future studies by replicating and extending on the current research. 2. What does the public report include? The public report is designed to disseminate the results of the indicators project to get the broadest exposure possible within the community. The public report should include an explanation of what is community quality of life and how it was measured in the indicators project. The report also includes basic information about each indicator and its trends. Finally, the report includes interpretation of the trends and the need for change (if needed). Recommended change, of course, comes in the form of recommended community programs and policies. 3. What are popular formats used in preparing the public report? The public report is usually easily readable and visually striking. That is, the report format involves minimal text, large headings, short definitions and explanations, and short tables and clear and striking graphs. There are many formatting elements used in preparing and disseminating the public report. These include article, billboard, blog post, charts and graphs, dashboard, digital interactive, editorial, handheld interactive, maps, marketing and advertisements, microsite (standalone website), music, newsletter, podcast, presentation, press release, scorecard, short-form report, social media post, infographic, interview, landing page on a website, text message, theatre, video, and webinar. 4. What does the research report include? Typical research reports include a title page, acknowledgements, preface, a table of content, an executive summary, overall purpose of the project, background information, the community quality-

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of-life model, selection of indicators based on secondary data, method pertaining to secondary data, results of secondary data analysis, discussion of secondary data analysis, selection of indicators based on primary data, method pertaining to primary data, results of primary data analysis, discussion of primary data analysis, conclusion, references, and appendices. 5. How to report results in actionable terms? The results of an indicators project have to be reported in actionable terms. That is, both public and research reports have to be action-oriented. To reiterate, outcome indicators provide feedback about the overall well-being of the community. In contrast, action indicators provide policymakers and other decision makers with feedback about the success of specific programs and policies.

Reference Stevens, C., de Blois, M., Hemberg, R., Baldwin, J., & The Community Indicators Consortium. (2021). Community indicators project development guide. Community Indicators Consortium.

Chapter 9

Promotion

Learning Objectives 1. How to promote the indicators reports through printing and hard copy distribution? 2. How to promote the indicators reports through a public-relations campaign 3. How to promote the indicators reports through E-marketing? 4. How to promote the indicators reports through other forms of promotion? 5. How to stimulate community action guided by the findings of the indicators report?

Introduction In this chapter we will discuss promotion. That is, the information contained in the indicators reports has to be disseminated to the public and specific stakeholder groups. The promotion campaign involves printing and distributing the reports (public and research reports), and conducting other forms of promotion such as public relations, E-marketing, among other forms of promotions. For example, the Long Island indicators project have an outreach program (Golob, 2009). Research publication (indicator studies, special analyses reports, and survey reports) are disseminated in different ways. Indicators studies and special analyses reports are disseminated through press releases, press launch, and direct mail. The press releases go to media outlets (local and national—newspapers, television, radio, and websites). The press launch is directed to both media outlets and elected officials and non-profit organizations in the community. The direct mail campaign is directed to school districts, colleges, civic organizations, and libraries. The survey reports on the other hand, are disseminated through the Long Island Index website, monthly articles in community papers, op-eds in newspapers, local advertising, and presentations of key findings to local audiences. Of course, all these promotion methods are ultimately designed to reach the general public. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_9

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Please note that many of the promotion tools discussed here have been briefly discussed in the context of developing and sustaining a budget in Chap. 4.

Printing and Distributing the Reports Both public and research reports should be printed professionally. Much of the work can be done with an in-house computer with desktop publishing software. However, if the budget allows it, it is best to use a professional graphic designer and copy editor to prepare the reports for large-scale printing. Many printing companies offer the graphic design staff to help with the layout and visual look of the report. A budget must be developed to help make several printing decisions. That is, we must find out how much money we can spend on printing the reports. The budget will affect decisions such as whether the report can be printed on a printing press or photocopier, in black and white or color (and how many colors—2, 3, or 4 colors), number of reports to be distributed, the quality of the paper, the binding method, hiring of a graphic designer, etc. The distribution plan must be worked out before making printing decisions too. That is, we need to know how the public and research reports will be distributed. Knowing the distribution plan should help us knowing how many reports should be printed. Also, knowing whether some of the reports will be mailed affects the design of the cover pages. Special design is required to qualify for bulk rate postage in the U.S. (so we need these requirements with the Postal Service before settling on the design). Many community indicators projects have now shifted to distributing the reports on CDs and other forms of digital media instead of hard copy. Doing so may result in a significant savings. In developing the distribution plan, we typically consider the target audiences of the indicators project. Who are we trying to reach and educate? For example, a mass mailing of the research and public reports may go to libraries, all citizen volunteers who worked on the project, selected public officials, the media, resource persons who presented at task forces meetings, among others. The public report is distributed in other ways. Typically, the public report is distributed through government offices, community events, religious organizations, public hearings, among others. See example in Box 9.1. Box 9.1: The Publication and Distribution of Reports: The Santa Cruz County California Community Assessment Project The Santa Cruz County California Community Assessment Project has customarily published their annual reports in several forms. The larger report consists of approximately 330 pages and is available for purchase for $25. The (continued)

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Box 9.1 (continued) report is given to selected elected officials for free, including members of the county board of supervisors and the two Congressional representatives for the district, the state assembly member, the state senator and several city council members for the four cities. The larger report is also placed on the website of the research partner: ASR and the United Way. The shorter report (essentially an executive summary) is prepared by the local hospital and is sent to every resident in the county in the context of the hospital’s newsletter. Source: Adapted from Zachary (2009, p. 6)

Launching a Public-Relations Campaign Promoting the indicators reports to the target audiences is typically done with a good public-relations campaign. Elements of a good campaign include press release, press conference, exclusive interviews, publicity, and public service announcements. Press release is a short description of the indicators project (and findings) sent to the news departments of mass media (local television stations, radio stations, newspapers, and magazines). Typically, the press release is accompanied with both public and research reports. Included in the letter sent to the news departments is a statement indicating a date for a possible press conference and willingness to grant interviews with news reporters about the indicators project and findings. A press conference should be held to inform news reporters of the local mass media about the indicators project and findings. A specific time and place is set and key people involved in the project are invited to provide to speak at that conference. It usually helps a great deal to invite prominent politicians or community leaders (e.g., town or city major) to be present at the press conference. In many cases, the chair of the steering committee contacts one of the large news organizations in the area and makes a concession to grant an exclusive interview with a news reporter of that organization. An exclusive interview is usually an attractive proposition to news organizations because they get to write (or broadcast) this story “exclusively,” giving them an advantage over competitor news organizations. Exclusive interviews are considered an attractive proposition for news organizations only if the chief editor of the news organization finds the “story” newsworthy. Therefore, in contacting the editor, the chair of the steering committee must communicate the newsworthiness of the indicators project and its findings. Also, exclusive interviews take form by scheduling appearances on local television and radio shows to discuss the project. Publicity is another form of public relations commonly used by steering committees of indicators projects. The focus here is to write (or produce a tape of) a “story” about the indicators project and findings and convince the local news media to publish (or broadcast) this story. In other words, we make it easier for the local

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news organizations to pick up the story because not only the story is newsworthy but also we have done most of the work (writing the story or producing the tape) for the news organization. This is essentially an easy sell and most news organizations take us up on that offer. Public Service Announcement (PSA) is also used to promote the reports of the indicators project. Local mass media are usually receptive to help develop PSAs related to the indicators project and placing these PSAs in their media for airing. PSAs, by definition, are free. In other words, mass media organizations are obligated by law to help community organizations advertise social messages that can benefit the community at large. The challenge then is to develop a persuasive PSA that motivates the target audience to obtain the indicators project reports or seek additional information on how to obtain them. Customarily, these PSAs include a URL address of the indicators project website prompting the target audience to visit the website. Other public relations venues are common too. For example, an honor and recognition program which recognizes positive change is a good public relations venue. See the honor and recognition program instituted by the Santa Cruz County California Community Assessment Project in Box 9.2. Box 9.2: The Recognition Program of the Santa Cruz County California Community Assessment Project The Santa Cruz County California Community Assessment Project has instituted a recognition program to publicize positive change in the county. Specifically, the indicators project team has annually honored community heroes—selected individuals whose efforts helped Santa Cruz County toward the achievement of community goals. This program is implemented through the Santa Cruz Sentinel, a local newspaper. The paper sponsors the selection of annual Community Heroes. The selection process is guided by recommendations submitted by readers. Heroes are selected in each of the indicators project goal areas and are honored at the annual indicators project press conference. The heroes are also profiled on local talk radio. The press conference is also recorded by Public Access Community Television and broadcast throughout the year. Source: Adapted from Zachary (2009, pp. 4–5)

Conducting E-Marketing In today’s promotion, E-marketing is probably the most effective promotion tool. E-marketing in the case of promoting the indicators project’s reports involves website development, e-mail distribution, links, PSAs, and specialty advertising. It is imperative for any indicators project to have its own website accessible to the community residents, leaders, policy makers, and the news media. Typical websites

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do not only include the actual public and research reports (that can easily be downloaded as PDF files) but also description of the entire project, its history, the volunteer and paid staff (with the people of the steering committee and task forces), indicators in the form of links that expand into definitions and charts with other links to data sources and other relevant information, and so on. As a matter of fact, putting both public and research reports on the website (as PDF files) should cut down on printing a high quantity of these reports and therefore saves money. Furthermore, many people would rather have access to a website (and downloadable PDF files) rather bulky hard copies that clutter their offices. The challenge is not only to develop a decent website but also to promote the use of the website. How is this done? Through e-mail distribution, links, and specialty advertising. E-mail distribution involves contacting people in our target audience and informing them of the availability of the indicators project reports on a designated website (usually a domain name that has key words such as “quality of life,” “community indicators,” “community well-being,” “community quality of life,” “indicators of community living,” etc.). We usually attach the public report to the e-mail message and make reference to a link that would allow the message recipient to access the research report on the designated website. Also, it helps to have a prominent community leader (e.g., town or city mayor) send that e-mail message. Source credibility is very important in this instance. Otherwise, many recipients are likely to delete the e-mail message without bothering to read it. Links are also very important in promoting the indicators project website and the reports. What do we mean by links? We try to make arrangements to install links to the Indicators project website from other community websites (e.g., town or city website, Chamber of Commerce website, websites of local hotels and economic development organizations). Furthermore, the website domain should be registered so that search engines can easily identify with certain key words such as “quality of life,” “community indicators,” “community well-being,” “community quality of life,” and “indicators of community living.” As previously mentioned, PSAs (or Public Service Announcement) are typically used to publicize the URL address of indicators project website. PSAs are free advertisements placed in the local media. And, of course, data displayed on the indicators project website can go a long way.1 See example in Box 9.3.

1

It is interesting to note that many community indicator projects have made significant efforts to “democratize data”—to make information more available to the general public. Such democratization means both greater access to data and greater input to the focus and design of data collection.

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Box 9.3: Promoting Results Through Website The Central New York Community Foundation sponsors and manages the Central New York (CNY) Vitals indicators project. The data are reported on the project’s website (https://pro.cnyvitals.org/). The website involves two components that highlight key community trends in the areas of poverty, education, health, housing, economy and arts and people (or demographics). The first component is a data visualization engine, which routinely updates the database with publicly available statistics from sources such as the United States Census Bureau, the Department of Labor and the state departments of health and education. The second component is a website that takes selected data visualizations from the first web engine and embeds them on a more journalistically focused website placing a human face on the data available in the visualization engine. Source: Adapted from Ridzi (2017)

Other Forms of Promotion Other forms of promotion used tend to be organizing a formal release event, presentations at community forums, and other ways to encourage citizens’ use of the indicators project reports. A formal event to release the report is also commonly used. This takes form in a luncheon or dinner convened by the funding or sponsoring organization. The event serves to acknowledge and express gratitude to all people who participated in the project. Furthermore, such an event attracts media attention and turns into a press conference (typically held after the event). Yet another way to promote the indicators project reports is through presentations at community forums (e.g., regular meetings of civic, social, religious, and professional organizations). Many of these groups hold weekly or monthly meetings and welcome presentations about what is happening in the community. Volunteers from the steering committee and the task forces usually serve as speakers. These presentations can be prompted by sending a note (requesting a presentation) to these organizations accompanied with the public report. See an example of a community forum run by the Vancouver indicators project in Box 9.4. Box 9.4: Community Conversations The Vancouver indicators project (https://www.vancouverfoundation.ca/ourwork/initiatives/vital-signs) has hosted webinars to help interpret the survey data, and further how it could be used. Community Conversations toolkits were created. The toolkits allowed representatives from various stakeholder groups to interpret the data. The toolkits included things such as posters and (continued)

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Box 9.4 (continued) social media promotional templates; customized data sheets for the area; conversation worksheets specific to selected topics, signage, copies of the report, and an event guide book. Source: Adapted from Ridzi and Prior (2020) Finally, is the use of specialty advertising in promoting the website of the indicators project. Many indicators project teams use pens, coffee mugs, key chains, t-shirts, mouse pads, and other forms of memorabilia to plaster the URL link of their websites. These specialty advertising items are distributed to people such as news reporters, business leaders, policy makers, and members of the volunteer staff and members of the steering committee and task forces.

Stimulating Community Action Although much of what we described in the form of promotion does stimulate community action indirectly, more can be done directly. The best way to stimulate community action directly is to get involved with policy makers and other community leaders to help them use the indicators project results in their decision-making. Here are some examples: • We make presentations of the indicators project and its environmental well-being findings to local environmental advocacy groups (e.g., local Sierra Club), and discuss with them how these findings can help set target goals to achieve. The discussion could also lead to the formulation of specific environmental programs that can help achieve target goals. An instance of such action is Truckee Meadows Tomorrow (TMT) in Reno, Nevada, USA. In July 2001, TMT worked closely with the Board of County Commissioners to adopt a Quality-of-Life Compact over a one-year period. The goal of this Quality-of-Life Compact was to improve the community’s natural environment. This global goal was to be achieved by five strategies directly linked to environmental well-being indicators, namely (a) reduced vehicle trips, (b) waste reduction, (c) energy conservation, and (d) water conservation/quality, and (e) public education. • We make presentations of the indicators project and its economic and consumer well-being findings at the local Chamber of Commerce meetings. These meetings are usually attended by local business leaders. Invite the local economic development specialists to these meetings. We discuss with business leaders how these findings can help set target goals related to economic and consumer well-being. We discuss specific economic development strategies guided by the economic and consumer indicators.

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• We make presentations of the indicators project and its social well-being findings at local religious, school, and other civic meetings (e.g., school board meetings, neighborhood association meetings, church meetings). We invite leaders from local social service agencies to these meetings. These meetings are usually attended by a wide range of social activists and concerned citizens. We discuss how the social indicators can help set target goals related to social well-being. We also discuss specific social programs and how these may impact the social indicators. • We make presentations of the indicators project and its health well-being findings at public health meetings and meetings involving local healthcare organizations. We discuss with public and private health officials how these findings can help set target goals related to community health. We discuss specific healthcare programs that can be implemented to enhance community health and achieve target goals. • We make presentations of the indicators project at city (or town) council meetings. We encourage public policy officials to use the report as a tool for government accountability. We discuss with public officials how to set target goals and possible public policies that can be developed and enforced to achieve these goals. Besides making presentation to various community stakeholder groups, there are creative ways to stimulate community action for positive change. One approach that seems to be quite effective is the Adopt-an-Indicator Program. This program is specifically designed to have specific individuals claim ownership of specific indicators, monitor trends over time, and work with various constituents within the community to effect positive change (Zachary, 2009). Another example of how to stimulate community action is shown in Box 9.5. Box 9.5: Community Change for Youth and Substance Abuse: Santa Cruz County California Community Assessment Project In 1995, the Santa Cruz County California Assessment Project showed alarming rates of youth use of cigarettes, marijuana, and alcohol. The data were based on a community survey asking adolescents how much they consumed cigarettes, marijuana, and alcohol during the last 30 days. The results showed that consumption of these substances was significantly above the average rates in California at large. For example, in 1994, 51% of Santa Cruz County 9th graders reported consumption of alcohol in the last 30 days compared to 44% in California at large. Similarly, 40% of Santa Cruz County 11th graders reported using marijuana in the last month compared to 26% in California at large. Guided by these statistics, the United Way of Santa Cruz developed a coalition of 110 agencies and individuals (the “Together for Youth/Unidos Para Los Jovenes”) to effect positive change in youth consumption of these substances. This group decided to focus on increasing youth confidence and (continued)

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Box 9.5 (continued) skills to reduce the risk factor. The group developed programs such as public education, youth leadership training, referral and home visitations, new teen centers and treatment services, and public policy panel on youth access to alcohol. After 1996, the trend in cigarette, alcohol, and marijuana consumption began to decline. The data showed consistent decreases in overall alcohol and drug use among 9th and 11th graders. Substance use has also decreased in the State of California as a whole, but the decreases in Santa Cruz County were larger than the decreases in the State. Specifically, the data showed a dramatic decline in the use of alcohol, marijuana, and cigarettes for 11th graders from 1994 to 2004. The ninth graders also showed similar declines, from 51% using alcohol in 1994 to 33% in 2004. Thirty-five percent of 9th graders used marijuana within the last 30 days in 1994, declining to 18% in 2004. The data also showed a growing trend of binge drinking among those who did use alcohol. As such, renewed efforts have focused on tackling the problem of binge drinking. Source: Adapted from Zachary (2009, pp. 12–13)

Summary This chapter focused on promotion—disseminating the public and research reports to the public and specific stakeholder groups. We discussed issues related to printing and distributing the reports and conducting promotion in the form of public relations, E-marketing, among other forms of promotions. With respect to printing and distributing the reports, we discussed how the reports have to be professionally designed and budgeted. The budget has to account for the cost of graphic and design as well as the number of copies to be printed. In doing so, the distribution plan has to be fully formulated to identify the number of recipients of both research and public reports, thus printing and mailing costs. With respect to the public relations campaign, we discussed press release, press conference, exclusive interviews, publicity, and public service announcements (PSAs). The press release is customarily accompanied with both public and research reports. Included in the letter sent to the news departments is a statement indicating a date for a possible press conference and willingness to grant interviews with news reporters about the indicators project and findings. A press conference should be held to inform news reporters of the local mass media about the indicators project and findings. Prominent politicians or community leaders are customarily involved in the press conference. Exclusive interviews with news media personnel are common too. Publicity involves writing (or producing a tape of) a “story” about the indicators project and findings and requesting the local news media to publish (or broadcast) this story. PSAs are also used to promote the reports of the indicators

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project. As such, local mass media help to develop PSAs and placing them in their media for airing. E-marketing involves website development, e-mail distribution, links, digital PSAs, and specialty advertising. Building a website and making it accessible to the community residents, leaders, policy makers, and the news media is of paramount importance. The website includes both public and research reports, description of the entire project (i.e., history, the volunteer and paid staff), indicators in the form of links that expand into definitions and charts with other links to data sources and other relevant information, etc. Encouraging people to use the website is equally important. This is done through e-mail distribution, links, and specialty advertising. E-mail distribution involves contacting people in our target audience and informing them of the availability of the indicators project reports on a designated website. Customarily, a prominent community leader is selected to send that e-mail message. Links are also very important in promoting the indicators project website and the reports. Links are installed in other community websites (e.g., town or city website, Chamber of Commerce website, websites of local hotels and economic development organizations). PSAs are typically used to publicize the URL address of indicators project website. Other forms of promotion are usually used: organizing a formal release event, presentations at community forums, and other ways to encourage citizens’ use of the indicators project reports. Then there is specialty advertising—the use of pens, coffee mugs, key chains, t-shirts, mouse pads, and other forms of memorabilia to plaster the URL link of their websites. Stimulate community action is also an important aspect of the promotion campaign. This means getting involved with policy makers and other community leaders to help them use the indicators project results in their decision-making. Examples include making presentations of the indicators project and its environmental wellbeing findings to local environmental advocacy groups (e.g., local Sierra Club), and discuss with them how these findings can help set target goals to achieve. Other examples include making presentations of the indicators project and its economic and consumer well-being findings at the local Chamber of Commerce meetings; making presentations about social well-being findings at local religious, school, and other civic meetings; making presentations about health well-being findings at public health meetings and meetings involving local healthcare organizations; and making presentations of the indicators project at city (or town) council meetings to encourage public policy officials to use the data as a tool for government accountability.

Progress Check 1. How to promote the indicators reports through printing and hard copy distribution? 2. How to promote the indicators reports through a public-relations campaign

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3. How to promote the indicators reports through E-marketing? 4. How to promote the indicators reports through other forms of promotion? 5. How to stimulate community action guided by the findings of the indicators report?

Progress Check Answers 1. How to promote the indicators reports through printing and hard copy distribution? The reports have to be professionally designed and budgeted. The budget has to account for the cost of graphic and design as well as the number of copies to be printed. As such, the distribution plan has to be fully formulated to identify the number of recipients of both research and public reports, thus printing and mailing costs. 2. How to promote the indicators reports through a public-relations campaign? A public-relations campaign usually involves the use of press release, press conference, exclusive interviews, publicity, and public service announcements (PSAs). The press release is customarily accompanied with both public and research reports. Included in the letter sent to the news departments is a statement indicating a date for a possible press conference and willingness to grant interviews with news reporters about the indicators project and findings. A press conference should be held to inform news reporters of the local mass media about the indicators project and findings. Prominent politicians or community leaders are customarily involved in the press conference. Exclusive interviews with news media personnel are common too. Publicity involves writing (or producing a tape of) a “story” about the indicators project and findings and requesting the local news media to publish (or broadcast) this story. PSAs are also used to promote the reports of the indicators project. As such, local mass media help to develop PSAs and placing them in their media for airing. 3. How to promote the indicators reports through E-marketing? E-marketing involves website development, e-mail distribution, links, digital PSAs, and specialty advertising. Building a website and making it accessible to the community residents, leaders, policy makers, and the news media is of paramount importance. The website includes both public and research reports, description of the entire project (i.e., history, the volunteer and paid staff), indicators in the form of links that expand into definitions and charts with other links to data sources and other relevant information, etc. Encouraging people to use the website is equally important. This is done through e-mail distribution, links, and specialty advertising. E-mail distribution involves contacting people in our target audience and informing them of the availability of the indicators project reports on a designated website. Customarily, a prominent community leader is selected to send that e-mail message. Links are also very important in promoting the indicators project website and the reports. Links are installed in other community websites (e.g., town or city website, Chamber of Commerce website, websites of local hotels and

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economic development organizations). PSAs are typically used to publicize the URL address of indicators project website. 4. How to promote the indicators reports through other forms of promotion? Other forms of promotion include organizing a formal release event, presentations at community forums, and using specialty advertising (i.e., the use of pens, coffee mugs, key chains, t-shirts, mouse pads, and other forms of memorabilia to plaster the URL link of their websites). 5. How to stimulate community action guided by the findings of the indicators report? Stimulating community action is also an important aspect of the promotion campaign. This means getting involved with policy makers and other community leaders to help them use the indicators project results in their decision-making. Examples include making presentations of the indicators project and its environmental well-being findings to local environmental advocacy groups (e.g., local Sierra Club), and discuss with them how these findings can help set target goals to achieve. Other examples include making presentations of the indicators project and its economic and consumer well-being findings at the local Chamber of Commerce meetings; making presentations about social well-being findings at local religious, school, and other civic meetings; making presentations about health well-being findings at public health meetings and meetings involving local healthcare organizations; and making presentations of the indicators project at city (or town) council meetings to encourage public policy officials to use the data as a tool for government accountability.

References Golob, A. (2009). The Long Island index. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 25–58). Springer. Ridzi, F. (2017). Community indicators and the collective goods criterion for impact. In M. Holden, R. Phillips, & C. Stevens (Eds.), Community quality-of-life indicators: Best cases VII (pp. 35–52). Springer. Ridzi, F., & Prior, T. (2020). Community leadership through conversations and coordination: The role of local surveys in community foundation run community indicators projects. International Journal of Community Well-Being. https://doi.org/10.1007/s42413-020-00098-z Zachary, D. (2009). Connecting outcomes to indicators: The Santa Cruz County California community assessment project (CAP). In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases (pp. 1–20). Springer.

Chapter 10

Follow-Up

Learning Objectives After reading this chapter, the student should be able to answer the following questions: 1. 2. 3. 4. 5.

What do annual reviews serve to accomplish? What is a review committee? What is the composition of this committee? What questions does the review committee try to answer? What are examples of review questions capturing the What, Why, When, Where, and Who? 6. How do indicators researchers measure the effectiveness of their indicators project?

Introduction In this chapter we will discuss two major topics related to following-up—what to do during and after promoting the indicators project, namely conducting annual reviews and measuring impact of the project.

Annual Reviews Indicators projects are not a one-shot deal. They are designed to monitor the state of community well-being over time. Target goals are set based on current data and a system that monitors community outcomes to gauge progress towards the goals. Indicator researchers are not directly involved in setting target goals. Only the decision-makers do, and different decision makers may have different target goals. Our responsibility is to monitor the state of well-being in the community over time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0_10

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Updating the data is usually a staff function; however, many indicator projects have a volunteer review committee. Customarily, the review committee is charged with examining the last published research report for accuracy and make recommendations to the staff about possible changes (e.g., delete certain indicators, add others). The review committee serves a good purpose. It not only identifies needed changes in the project but also enhances community citizenship. It does so by allowing community leaders to stay actively involved in the project. And in some ways, this citizenship helps maintain commitment of funders and support of key organizations. The review committee is very similar to the steering committee that was organized in the planning stages of the project. It is usually composed of representatives from the sponsoring organization(s), funding source(s), as well as experts in various wellbeing sectors (e.g., economic, social, health, environmental well-being). Further, it is advisable to have representatives from media and news organizations on the review committee. Also advisable is the diversity of the members. Review committees—should be balanced in terms of representation of gender, ethnicity, and neighborhoods. What should be reviewed? We can break down the review elements in terms of process that can be captured through a checklist (Steven et al., 2021). Here is the checklist. • Were resources needed for launching a successful indicators project identified and used? • Were important stakeholders represented on the steering committee? • Was input from representatives of the stakeholder groups accepted by the steering committee or shut out? • Were subject-matter experts consulted? • Were the quality-of-life dimensions selected based on accepted and wellestablished scientific criteria? • Were best practices for data management (e.g., external backup, data planner, data sharing agreements) used? • Has the reporting done regularly in a manner friendly to target audiences? • Were communication tools (e.g., website, social media, publications) used and tracked? • Did the project reach out to the community through participation in events, public meetings, data days, etc.? • Have steps been taken to ensure sustainability of the project? A much more elaborate review process was also suggested by Steven et al. (2021) broken down by the traditional five questions: What, Why, When, Where, and Who.

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What? • Did the metrics of community indicators quantify values, community, conditions, outcomes, and results important to wide-ranging community residents? • Did the visioning process involve citizens, key stakeholders in the community? • Were the metrics developed through consensus-building?

Why? • Were the community indicators helpful in telling a story of where the community is today compared to its past? • Was there public dialogue and debate that led identifying priorities of community residents and other stakeholder groups? • Was the public debate helpful in determining the whys of community conditions, generating strategies to improve community conditions, and identifying resources to implement the strategies?

When? • Were the community indicators helpful in plotting historical trends alerting need for improvement of community conditions? • Were leading and lagging indicators benchmarked for measurable improvement or decline? • Were long-term and annual goals and targets established periodically and progress measured and publicly reported?

Where? • Were the community indicators explicitly defined in terms of geographic boundaries? • Were the data compared with other comparable communities?

Who? • Were community residents in a position to control or influence community conditions?

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• Were key community stakeholders involved too? • What about coalitions, networks, and other groups involved in leading community change?

Measuring the Impact of Indicator Reports Many community indicators projects do not engage in any systematic evaluation of their indicator reports. They should do so to be more effective. The reluctance may be related to the difficulty in measuring the impact. What should these impact measures be? We can conceive a hierarchy of effects related to the impact of indicator reports, namely awareness, knowledge, attitude, and action.

Awareness Awareness can be captured objectively and subjectively. An objective measure of awareness may be in the form of column inches of newspaper reporting related to the indicator reports and number of citations in the news media of the indicator reports. A subjective measure of awareness can be in the form of a survey directed to target audiences (e.g., business leaders in the community, government officials, leaders of community service organizations, community residents) conducted periodically (once a year or so) in which sample respondents are asked the following question: “Are you aware of the XYZ Community Indicators Project and its public reports describing how well this community is doing in terms of economic, social, health, and environmental indicators?” A 3-point scale with the following response categories can be used: “I am not aware at all,” “I am a little aware,” “I am very much aware.”

Knowledge Knowledge of the community indicators project and its reports can best be captured subjectively in the context of the same survey used to capture awareness. Knowledge goes beyond awareness to capture specific information about the community indicators project, its reports, and the content of these reports. Example survey items include: (a) “Are you aware the XYZ Community Indicators project has been around since (specific date)?” (b) “Are you aware the XYZ Community Indicators Project issued (frequency) of public reports about the well-being of this community?” (c) “Are you aware that the teenage pregnancy rate in this community is significantly above the national average?” A “Yes-No” response scale can be used to capture overall knowledge.

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Attitude Attitude can also be captured by the same survey. Examples of survey items may include: “Do you have a favorable or unfavorable opinion of the XYZ Community Indicators Project and its reports?” (“very favorable,” “favorable,” “neither favorable not unfavorable,” “unfavorable,” “very unfavorable”); or “Do you believe that the XYZ Community Indicators Project is making a difference to your community?” (making a big difference,” “making a little difference,” “making no difference”).

Action Finally, action can be measured using objective indicators. Examples include: number of people showing up to meetings arranged by the indicators project committee, number of public speeches made by public officials and other community leaders making specific plans to establish programs that can be directly monitored by the community indicators project, number of actual programs created (or adjusted) as a direct function of the work of the community indicators project, and increase in expenditures on programs directly attributable to the work of the community indicators project. What we just described in a traditional method of program evaluation. However, not all indicators projects conduct program evaluation in the traditional sense. There are notable exceptions. For example, see the Results Based Accountability program used by the Central New York Community Foundation in Box 10.1. Here the evaluation criterion focused on action in terms of actual changes in community conditions. Also see how the Santa Cruz County California Community Assessment Project conduct their program evaluation in Box 10.2. Box 10.1: Results Based Accountability In the State of New York, state funding of poverty program requires program evaluation. This is referred to as Results Based Accountability (RBA). The Central New York Community Foundation managed to use results from their indicators project to conduct a program evaluation. Specifically, the Central New York Community Foundation had their clients complete the local community survey to report trends of declining unmet needs. Doing so allowed the foundation to conduct program evaluation in a manner consistent with RBA. For example, one organization funded by the foundation reported significant numbers of clients who were provided transportation services in a select group of census tracts. This figure was then matched with the survey data that focused on the same census tracts to examine whether the reported need for transportation declined. Source: Adapted from Ridzi and Prior (2020)

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Box 10.2: A Stakeholder Survey to Evaluate the Program The Santa Cruz County California Community Assessment Project has performed a program evaluation using a stakeholder approach. Specifically, the survey involved 46 respondents from non-profit organizations, human services, county/city government, law enforcement, business, health services, and the arts/culture humanities. The respondents were selected based on their experience with the project—funders, people who used the indicators project reports in the past, leaders of organizations that had past exposure to the indicators project, and other individuals who expressed interest in using the reports to guide their decision-making. The results of the survey indicated that 57% of the respondents were very satisfied with the indicators project and 33% were somewhat satisfied. With respect to report use, 5% indicated that they have used the project’s report weekly, 28% used the reports monthly 34% indicated that they have used the indicators project reports every 2–6 months, 18% used the reports every 7–12 months, 16% used them rarely, and 5% have never used them. When asked how often the reports should be published, 46% of the respondents indicated every 2 years, while 39% indicated yearly publication. When asked why they use the reports, the majority indicated that the reports help in grant writing. Source: Adapted from Zachary (2009, p. 6)

Summary In this chapter we discussed two major topics related to following-up—what to do during and after promoting the indicators project, namely conducting annual reviews and measuring impact of the project. Annual reviews serve to ensure the sustainability of the indicators project. Indicators projects are designed to monitor the state of community well-being over time. Target goals are set based on current data and a system that monitors community outcomes to gauge progress towards the goals. As such, indicators projects monitor the state of well-being in the community over time. This means updating the data on an annual basis. The annual review function is delegated to a review committee. The review committee is usually composed of representatives from the sponsoring organization(s), funding source(s), as well as experts in various wellbeing sectors (e.g., economic, social, health, environmental well-being). The committee has representatives from media and news organizations.

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The review usually involves answering questions such as were resources needed for launching a successful indicators project identified and used; were important stakeholders represented on the steering committee; was input from representatives of the stakeholder groups accepted by the steering committee or shut out; were subject-matter experts consulted; were the quality-of-life dimensions selected based on accepted and well-established scientific criteria; were best practices for data management (e.g., external backup, data planner, data sharing agreements) used; has the reporting done regularly in a manner friendly to target audiences; were communication tools (e.g., website, social media, publications) used and tracked; did the project reach out to the community through participation in events, public meetings, data days, etc.; and have steps been taken to ensure sustainability of the project? A much more elaborate review process can be captured by answering a similar set of questions categorized under the traditional five questions: What, Why, When, Where, and Who. Review items capturing the What question include “Did the metrics of community indicators quantify values, community, conditions, outcomes, and results important to wide-ranging community residents?” “Did the visioning process involve citizens, key stakeholders in the community?” And “Were the metrics developed through consensus-building?” Review items capturing the Why question include “Were the community indicators helpful in telling a story of where the community is today compared to its past?” “Was there public dialogue and debate that led identifying priorities of community residents and other stakeholder groups?” And “Was the public debate helpful in determining the whys of community conditions, generating strategies to improve community conditions, and identifying resources to implement the strategies?” Review items capturing the When question include “Were the community indicators helpful in plotting historical trends alerting need for improvement of community conditions?” “Were leading and lagging indicators benchmarked for measurable improvement or decline?” And “Were long-term and annual goals and targets established periodically and progress measured and publicly reported?” Review items capturing the Where question include “Were the community indicators explicitly defined in terms of geographic boundaries?” And “Were the data compared with other comparable communities?” Review items capturing the Who question include “Were community residents in a position to control or influence community conditions?” “Were key community stakeholders involved too?” And “What about coalitions, networks, and other groups involved in leading community change?” The impact of community indicator projects is measured periodically to gauge their effectiveness in serving their communities. This is done by measuring the impact of the indicators project and their reports on target audiences along a set of objectives referred to as the hierarchy of effects. A hierarchy of effects involves impact measures such as awareness of the community indicators project, knowledge of its process and function, attitude toward the indicators project, and action motivated by the indicators project.

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Progress Check 1. 2. 3. 4. 5.

What do annual reviews serve to accomplish? What is a review committee? What is the composition of this committee? What questions does the review committee try to answer? What are examples of review questions capturing the What, Why, When, Where, and Who? 6. How do indicators researchers measure the effectiveness of their indicators project?

Progress Check Answers 1. What do annual reviews serve to accomplish? Annual reviews serve to ensure the sustainability of the indicators project. Indicators projects are designed to monitor the state of community well-being over time. Target goals are set based on current data and a system that monitors community outcomes to gauge progress towards the goals. As such, indicators projects monitor the state of well-being in the community over time. This means updating the data on an annual basis. 2. What is a review committee? The annual review function is delegated to a review committee. 3. What is the composition of this committee? The review committee is usually composed of representatives from the sponsoring organization(s), funding source (s), as well as experts in various wellbeing sectors (e.g., economic, social, health, environmental well-being). The committee has representatives from media and news organizations. 4. What questions does the review committee try to answer? The review usually involves answering questions such as were resources needed for launching a successful indicators project identified and used; were important stakeholders represented on the steering committee; was input from representatives of the stakeholder groups accepted by the steering committee or shut out; were subject-matter experts consulted; were the quality-of-life dimensions selected based on accepted and well-established scientific criteria; were best practices for data management (e.g., external backup, data planner, data sharing agreements) used; has the reporting done regularly in a manner friendly to target audiences; were communication tools (e.g., website, social media, publications) used and tracked; did the project reach out to the community through participation in events, public meetings, data days, etc.; and have steps been taken to ensure sustainability of the project? 5. What are examples of review questions capturing the What, Why, When, Where, and Who? Review items capturing the What question include “Did the metrics of community indicators quantify values, community, conditions, outcomes, and results important to wide-ranging community residents?” “Did the visioning

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process involve citizens, key stakeholders in the community?” And “Were the metrics developed through consensus-building?” Review items capturing the Why question include “Were the community indicators helpful in telling a story of where the community is today compared to its past?” “Was there public dialogue and debate that led identifying priorities of community residents and other stakeholder groups?” And “Was the public debate helpful in determining the whys of community conditions, generating strategies to improve community conditions, and identifying resources to implement the strategies?” Review items capturing the When question include “Were the community indicators helpful in plotting historical trends alerting need for improvement of community conditions?” “Were leading and lagging indicators benchmarked for measurable improvement or decline?” And “Were long-term and annual goals and targets established periodically and progress measured and publicly reported?” Review items capturing the Where question include “Were the community indicators explicitly defined in terms of geographic boundaries?” And “Were the data compared with other comparable communities?” Review items capturing the Who question include “Were community residents in a position to control or influence community conditions?” “Were key community stakeholders involved too?” And “What about coalitions, networks, and other groups involved in leading community change?” 6. How do indicators researchers measure the effectiveness of their indicators project? The impact of community indicator projects is measured periodically to gauge their effectiveness in serving their communities. This is done by measuring the impact of the indicators project and their reports on target audiences along a set of objectives referred to as the hierarchy of effects. A hierarchy of effects involves impact measures such as awareness of the community indicators project, knowledge of its process and function, attitude toward the indicators project, and action motivated by the indicators project.

References Ridzi, F., & Prior, T. (2020). Community leadership through conversations and coordination: The role of local surveys in community foundation run community indicators projects. International Journal of Community Well-Being. https://doi.org/10.1007/s42413-020-00098-z Stevens, C., de Blois, M., Hemberg, R., Baldwin, J., & The Community Indicators Consortium. (2021). Community indicators project development guide. Community Indicators Consortium. Zachary, D. (2009). Connecting outcomes to indicators: The Santa Cruz County California community assessment project (CAP). In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases (pp. 1–20). Springer.

Appendix

Data Sources Bureau of Justice Statistics provides data on crime, criminal offenders, victims of crime, and the operations of justice systems at all levels of government in the U.S. (http://bjs.ojp.usdoj.gov/). Bureau of Labor Statistics (BLS) and their Local Area Unemployment Statistics (https://data.bls.gov/cgi-bin/dsrv) provides data on size of labor force, unemployment rate, and number of unemployed for most states, counties, and cities in the U.S. Center for Disease Control and Prevention (CDC) provides much data related to public health. The data and statistics site includes much data but mostly at the U.S. national level (www.cdc.gov/DataStatistics). CDC WONDER (Wideranging OnLine Data for Epidemiologic Research; http://wonder.cdc.gov) provides U.S. data about mortality, natality, and incidence and rates of many diseases and illnesses. The Youth Risk Behavior Surveillance System (YRBSS; www. cdc.gov/healthyyouth/dta/yrbs/index.htm) provides data on six types of healthrisk behaviors associated with leading causes of death and disability among U.S. youth and adults. Community Commons (www.communitycommons.org/maps-data/) provides community-level (U.S.) data and maps on various topics related to equity, the economy, education, food, health, and the environment. County Health Rankings provides U.S. county-level data on health-related factors such as obesity, smoking, access to unhealthy foods, quality of air and water, teen births, etc. at www.countyhealthrankings.org/. Dartmouth Atlas of Health Care uses Medicare data to provide users data about health care at different levels of analysis (national, regional, and local markets) at www.dartmouthatlas.org/.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 M. J. Sirgy, Community Quality-of-Life Indicators, Community Quality-of-Life and Well-Being, https://doi.org/10.1007/978-3-031-10208-0

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Data.gov (www.data.gov) has much data collected by various U.S. federal government agencies (e.g., ecosystem vulnerability, food security, higher education, housing affordability). The site has information about how to access the data sets. Department of Housing and Urban Development (HUD) provides data on the homeless in county and states in the U.S. (www.hudexcahnge.info/programs/coc/ coc-homeless-populations-and-subpopulations-reports). Furthermore, HUD USER provides access to the American Housing Survey at www.huduser.gov. Feeding America provides reports on food insecurity in relation to vulnerable populations in the U.S. such as children, the elderly, rural residents. The organization provides an interactive tool to help users obtain food insecurity data at http://map.feedingamerca.org/. Health Resources and Administration (HRSA) Data Warehouse provides data related to HRSA activities related to the uninsured, underserved, and special needs populations in the U.S. (https://datawarehouse.hrsa.gov/) Hispanic Research Center provides users with several data tools related to the economic conditions of Hispanic families in the U.S. at www. hispanicresearchcenter.org/resources/interactive-data-tools. Home Mortgage Disclosure Act (HMDA) provides data related to U.S. lending institutions and loans at www.ffiec.gov/hmda/default.htm. KIDS COUNT Data Center of the Annie E. Casey Foundation provides data on children wellbeing across communities in the U.S. at http://datacenter.kidscount. org/. Management Institute for Quality-of-Life Studies (MIQOLS) provides community-level data for U.S. communities along many quality-of-life dimensions: demographics, economic wellbeing, education wellbeing, environmental wellbeing, health wellbeing, housing wellbeing, safety wellbeing, shopping wellbeing, and work wellbeing at http://www.miqols.org/toolbox/usqoli.html. The site allows the user to select the geographic level of analysis (block, block group, census track, county, state, and national levels) and compare one community with two others. National Association of Realtors (NAR) publishes report on housing values and periodically releases data sets on housing values in the U.S.. Furthermore, the NAR provides data on median home price by metropolitan area at www.realtor. org/topics/metropolitan-median-area-prices-and-affordability/data. National Center for Educational Statistics (NCES) provides data related to education in the U.S. at www.nces.gov. National Equity Atlas provides users an interactive tool that generates graphic at different levels of analysis of the U.S. (state, regional, and city level) using a diverse set of indicators that signal racial and ethnic equity and inequity issues (http://nationalequityatlas.org/indicators). U.S. Census Bureau provides much data about the country’s people and economy (www.census.gov). The Census bureau provides data from the Population and Housing Census, Economic Census, Census of Governments, American Community Survey, among other demographic and economic surveys. The Census Bureau help indicators researchers on how to use the census data – how to access

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and download the many thousands of data tables through www.factfinder.census. gov. U.S. Department of Agriculture and Food Administration (USDA) Atlas provides data on food environment factors (e.g., store/restaurant proximity), food prices, food and nutrition assistance programs and other U.S. community characteristics at www.ers.usda.gov/FoodAtlas. U.S. Environmental Protection Agency (EPA) provides data on drinking water at www.epa.gov/waterdata/drinking-water-tool.

Websites Providing Useful Information Community Indicators Consortium. Indicators Projects (http:// communityindicators.net/indicators-projects/). Institute for Research on Poverty has a list of websites varied topics related to poverty (e.g., health and child wellbeing) at https://www.irp.wisc.edu/faqs/faq6. htm. Partners in Information Access for the Public Health Workforce is a website that provides timely, convenient access to selected U.S. public health resources on the internet (https://phpartners.org/health_stats.html). State of the Cities Data Systems (SOCDS) provides data for individual metropolitan areas, central cities, and suburbs at www.huduser.gov/portal/datasets/socds. html.

Web Sites of Community Indicators Projects Towns, Cities, and Counties Adams County, Illinois (USA): Adams County Community Needs Assessment (https://www.adcogov.org/news/community-needs-assessment) Albuquerque (City of), New Mexico (USA): Albuquerque Progress Report (http:// abqprogressreport.sks.com/) Baltimore (City of), Maryland (USA): Vital Signs of Baltimore Neighborhood Indicators Alliance (https://bniajfi.org/). Berks County, Pennsylvania (USA): Berks Vital Signs (https://berksvitalsigns.com/) Boston (Greater Boston), Massachusetts (USA): Boston Indicators (https://www. bostonindicators.org/?q&sortBy¼date&sortOrder¼desc&page¼1) Boulder County, Colorado (USA): Boulder County Trends (https://www. commfound.org/trends) Calgary (City of), Alberta (Canada): Sustainable Calgary (http://www. sustainablecalgary.org/) Calhoun County, Michigan (USA). MiCalhoun (http://www.micalhoun.org/)

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Dakota County, Minnesota (USA): Data County Community Indicators (https:// www.co.dakota.mn.us/Government/Analysis/Demographics/Pages/default.aspx) Denver, Colorado (USA): Shift Research Lab (https://www.shiftresearchlab.org/ open-data-tools) Durham (City of), North Carolina (USA): Durham Neighborhood Compass (https:// compass.durhamnc.gov/en) Edmonton (City of), Alberta (Canada): City of Edmonton Citizen Dashboard (https://dashboard.edmonton.ca/) Erie County, Pennsylvania (USA): Erie Vital Signs (https://www.erievitalsigns.org/) Essex County, Massachusetts (USA): Impact Essex County (https://www. impactessexcounty.org/) Fond du Lac County, Wisconsin (USA): L.I.F.E. Fond du Lac County (https://www. lifefdlc.com/) Forsyth County, North Carolina (USA): Forsyth Futures (https://www. forsythfutures.org/) Greensboro, North Carolina (USA): GreensbOrometer (https://www.greensboro-nc. gov/departments/planning/request/mapping-census-info-data-analysis/greens boro-population-statistics/greensborometer) Issaquah (City of), Washington (USA): Sustainability Indicators (https://www. issaquahwa.gov/373/Sustainability-Indicators) Jackson, Florida (USA): Citizen Engagement PACT of Jacksonville (https:// communityindicators.net/indicator-projects/citizen-engagement-pact-of-jackson ville-formerly-jacksonville-quality-of-life-indicators/) King County, Washington (USA): Communities Count (https://www. communitiescount.org/) Lancaster County, Pennsylvania (USA): Lancaster County Community Indicators (https://lancasterindicators.com/) Lincoln, Nebraska (USA): Lincoln Vital Signs (https://www.lincolnvitalsigns.org/) Long Island, New York (USA): Long Island Index (http://www.longislandindex.org/ ) Los Angeles (City of), California (USA): Los Angeles Sustainable Development Goals (https://sdg.lamayor.org/our-work/data-reporting-platform) Omaha, Nebraska (USA): The Landscape Omaha (https://omahafoundation.org/ research/) Pierce County, Washington (USA): United Way of Pierce County Community Indicators (http://indicators.uwpc.org/index.htm) Port Alberni, British Columbia (Canada): Alberni Valley’s Vital Signs (https:// albernifoundation.ca/vital-signs) Rochester, New York (USA): ACT Rochester (https://www.actrochester.org/) San Francisco, California (USA): Central Market/Tenderloin Data Portal (https:// cmtldata.org/) Santa Cruz County, California (USA): Data Share (https://www.datasharescc.org/) Seattle, Washington (USA): Sustainable Seattle (https://sustainableseattle.org/) Toronto, Ontario (Canada): Toronto’s Vital Signs (https://torontofoundation.ca/ vitalsigns2019/)

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Vancouver, British Columbia (Canada): Vital Signs (https:// vancouverfoundationvitalsigns.ca/) Walla Walla County, Washington (USA): Walla Walla Trends (http://www. wallawallatrends.org/) Yakima County, Washington (USA): Yakima Valley Trends (http:// yakimavalleytrends.org/) Yolo County, California (USA): Community Indicator Dashboard (https://www. yolocounty.org/government/general-government-departments/county-administra tor/community-indicator-dashboard) York County, Pennsylvania (USA): York Counts Indicators (https://www. yolocounty.org/government/general-government-departments/county-administra tor/community-indicator-dashboard)

Regions Benton-Franklin Counties in Washington (USA): Benton and Franklin Trends (http://bentonfranklintrends.org/index.cfm) Berkley, Charleston & Dorchester Counties, South Carolina (USA): 2020–2021 Regional Economic Scorecard (https://www.crda.org/economicscorecard/) Carver County, Minnesota (USA): Carver County Quality of Life Indicators (http:// www.otsego.org/qol/Research/MN%202006_QOL_Report.pdf) Charlotte and Mecklenburg, North Carolina (USA): Charlotte/Mecklenburg Quality of Life Explorer (https://mcmap.org/qol/) Chelan and Douglas Counties, Washington (USA): Cheland-Douglas Trends (http:// chelandouglastrends.com/) Coastal Georgia (USA). Coastal Georgia Indicators Coalition (https://www. coastalgaindicators.org/) Central Texas (USA): Austin Area Sustainability Indicators (http://www. austinindicators.org/) Greater Christchurch (New Zealand): Greater Christchurch Outcomes Monitoring (https://greaterchristchurch.org.nz/our-work/indicators/) Greater Louisville, Kentucky (USA): Greater Louisville Project (https://www. greaterlouisvilleproject.org/) Greater New Haven, Connecticut (USA): Greater New Haven Community Index (https://ctdatahaven.org/reports/greater-new-haven-community-index) Greater New Orleans, Louisiana (USA): The New Orleans Index at Ten ( https:// www.datacenterresearch.org/reports_analysis/new-orleans-index-at-ten/) Greater Montreal, Quebec (Canada): Vital Signs of Greater Montreal (http://www. fgmtl.org/en/signesvitaux_arch.php) Greater Minneapolis St-Paul, Minnesota (USA): Regional Indicators Dashboard (https://www.greatermsp.org/regional-indicators-2019/) Greater Victoria, British Columbia (Canada): Victoria’s Vital Signs (https:// victoriafoundation.bc.ca/vital-signs/)

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Houston, Texas (USA): Houston Sustainability Indicators Quality of Life Atlas (http://www.houstoncommunitysustainability.org/) Indianapolis and Marion County, Indiana (USA): INDY Indicators (http:// indyindicators.iupui.edu/) Kansas City Region, Missouri and Kansas (USA): Metro Outlook. (https://www. marc2.org/metrooutlook/) Lackawanna and Luzerene Counties, Pennsylvania (USA): The Indicators Project (https://www.institutepa.org/indicators.php) Metropolitan Philadelphia, Pennsylvania (USA): Metropolitan Philadelphia Indicators Project (https://mpip.temple.edu/) Newfoundland and Labrador (Canada): Community Accounts (https://nl. communityaccounts.ca//Default.asp?) Puget Sound, Washington (USA): Puget Sound Vital Signs (https://vitalsigns. pugetsoundinfo.wa.gov/) Reno/Sparks/Washoe Counties, Nevada (USA): Measuring What Matters (https:// www.truckeemeadowstomorrow.org/) Richmond, Virginia (USA): Capital Region Collaborative (https:// capitalregioncollaborative.com/wp-content/uploads/2018/03/Snapshot-2018-forweb.pdf) Southern Arizona (USA): MAP – Making Action Possible for Southern Arizona (https://www.mapazdashboard.arizona.edu/) Southern Appalachia (USA): Southern Appalachian Vitality Index (http:// southernappalachianvitalityindex.org/) Spartanburg Region, South Carolina (USA): Spartanburg Community Indicators Project ( https://www.strategicspartanburg.org/) Spokane County, Washington (USA): Spokane Trends (http://spokanetrends.org/) Tasajera Island, La Paz (El Salvador): Mareas De Tasajera (http://www. mareasdetasajera.com/)

States and Provinces Arkansas (USA): Aspire Arkansas (https://www.aspirearkansas.org/) Colorado (USA): Our Impact (https://www.thealliancecenter.org/ourimpact/) Connecticut (USA): CT Data (https://www.ctdata.org/) Florida (USA): The Florida Scorecard (https://thefloridascorecard.org/? AspxAutoDetectCookieSupport¼1) Idaho (USA): Indicators Idaho (http://www.indicatorsnorthwest.org/) Indiana (USA): Stats Indiana (http://www.stats.indiana.edu/) Minnesota (USA): Minnesota Compass (https://www.mncompass.org/) Nova Scotia (Canada): Nova Scotia Quality of Life (https://www.nsqualityoflife.ca/) South Dakota (USA): South Dakota Dashboard (https://www.benchmarkdatalabs. org/sddashboard/) Virginia (USA): Virginia Performs (http://vaperforms.virginia.gov/)

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Waikato (New Zealand): Waikato Progress Indicators – Tupurango (https://www. waikatoregion.govt.nz/Community/Waikato-Progress-Indicators-TupurangaWaikato/) Wellington (New Zealand): Genuine Progress Index (GPI): Measuring the Region’s Well-Being (http://www.gpiwellingtonregion.govt.nz/indicators/) Winnipeg (Canada): PEG (https://www.mypeg.ca/)

Countries Australia: Australian National Development Index (http://www.andi.org.au/) Australia: Australian Urban Observatory (https://auo.org.au/) Canada: Canadian Index of Wellbeing (https://uwaterloo.ca/canadian-indexwellbeing/) Canada: Community Information Database (https://www.cid-bdc.ca/#c¼indicator& f¼0&i¼popchange.pop_change&s¼2011-2016&view¼map16)

Indicators Projects with Special Focus Annie E. Casey Foundation (USA): Kids Count Data Center (https://datacenter. kidscount.org/) Austin, Texas (Government): Austin City and Travis County Community Health Assessment (https://austintexas.gov/communityhealthplan) Australian Government Initiative: Australian Early Development Census (https:// www.aedc.gov.au/) Canadian Council on Learning: Composite Learning Index (http://www. niagaraknowledgeexchange.com/wp-content/uploads/sites/2/2014/10/2010CLIBooklet_EN.pdf) Center for Disease Control and Prevention (USA): Alzheimer’s Disease and Healthy Aging Data Portal (https://www.cdc.gov/aging/agingdata/index.html) Center for International Earth Science Information Network. Earth Institute, Columbia University: Environmental Sustainability Index (https://sedac.ciesin.colum bia.edu/data/collection/esi/) Chattanooga, Tennessee (USA): Economy and Entrepreneurship Dashboard (https://dashboards.mysidewalk.com/chattanooga-tn-economy-entrepreneurship/ home) Chesapeake Bay Program (USA). State of the Chesapeake (https://www. chesapeakebay.net/state) Citizens’ Committee for Children of New York (USA): Keeping Track Online the Status of New York City Children (https://data.cccnewyork.org/) Federal Interagency Forum on Aging Related Statistics (USA): Key Indicators of Well-Being (https://agingstats.gov/)

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Hispanic Institute (USA): Child Trends (https://www.childtrends.org/indicators?a-z) National Center for Education Statistics (USA): National Assessment of Educational Progress (https://nces.ed.gov/nationsreportcard/) Population Reference Bureau (California): KidsData (https://www.kidsdata.org/) Tomkins County, New York (USA): Community Indicators (focusing on youth) (https://www2.tompkinscountyny.gov/youth/communityindicator) United Health Foundation: America’s Health Rankings (https://www. americashealthrankings.org/) University of Wisconsin River Falls (USA): State of the Valley (https://www.uwrf. edu/StateOfTheValley/) Vision for Children at Risk (Greater St. Louis Region, USA): Data and Research (https://www.visionforchildren.org/data-research/) Wisconsin (USA): County Health Rankings and Roadmaps (https://www. countyhealthrankings.org/)

Other Web Resources Association for Community Health Improvement (2003). Healthy communities and collaborative resources. http://www.hospitalconnect.com/communityhlth/ resources/hlthycommunities.html Besleme, K. (1999). Community indicator projects that work: how five communities gauge progress. http://sustainable.state.fl.us:/fdi/fscc/news/world/9901/kate_ind. htm Besleme, K., Maser, E., and Silverstein, J. (1999). A community indicators case study: addressing the quality of life in two communities. San Francisco, California, USA: Redefining Progress. http://www.redefiningprogress.org/publi cations/pdf/CI_CaseStudy1.pdf CitiesPLUS30 (www.icsc.ca/cities30.html) Coalition for Healthier Cities and Communities in the U.S. (www.hospitalconnect. com/communityhlth/about/history.html) Cobb, C. and Rixford, C. (1998). Lessons learned from the history of social indicators. San Francisco, California, USA: Redefining Progress. http://www. redefiningprogress.org/newpubs/1998/SocIndHist.pdf Compendium of Sustainable Development Indicators Initiatives (http://iisd1.iisd.ca/ measure/compindex.asp) Crooks, J. B. (2000). Twenty-five years together: JCCI and the city. http://www.jcci. org/about/jccifirst25years.aspx Global Reporting Initiative (2002). 2002 sustainability reporting guidelines. http:// www.globalreporting.org/guidelines/2002.asp Hardi, P. and Barg, S. with Hodge T., and Pinter, L. (1997). Measuring sustainable development: review of current practice. http://strategic.ic.gc.ca/pics/ra/op17-a. pdf

Appendix

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Hardi, P. and Zdan, T. (eds) (1997). Assessing sustainable development: principles in practice. http://www.iisd.org/pdf/bellagio.pdf Hart, M. (2003). What is an indicator of sustainability. http://www. sustainablemeasures.com/Indicators/WhatIs.html International Institute for Sustainable Development (2003). Compendium: a global directory to indicator initiatives. http://www.iisd.org/measure/comendium International Institute for Sustainable Development (www.iisd.org) International Society for Quality-of-Life Studies (www.isqols.org) Local Agenda 21 (www.iclei.org/iclei/la21.htm) MacGillivary, A., et al. (1998). Communities count: a step-by-step guide to community sustainability indicators. London, England: New Economics Foundation. http://www.creativecommunities.org.uk/essays/30.html Money Magazine Best Places to Live (http://money.cnn.com/magazines/ moneymag/bplive/2006/) National Neighborhood Indicators Partnership (www.urban.org/nnip) New Economics Foundation (2000). Community indicators map. London, England: New Economics Foundation. http://www.neweconomics.org/gen/z_sys_ PublicationDetail.aspx?PID¼12 Redefining Progress (www.rprogress.org) Redefining Progress (2002). Sustainability starts in your community: a community indicators guide. http://www.redefiningprogress.org/newpubs/2002/ciguide.pdf Redefining Progress (1998). California community indicators projects’ taxonomy. http://www.redefiningprogress.org/newpubs/1998/CI-Taxonomy.pdf Rijkens-Klomp, N., van Asselt, M. and Rotmans, J. (2000). Towards an integrated planning tool for sustainable cities. International Centre for Integrative Studies, Maastricht, Netherlands. http://www.icis.unimass.nl/publ/downs/00_67.pdf Sustainable Communities Network (www.sustainable.org/casestudies/studiesindex. html) Sustainability Institute (www.sustainabilityinstitute.org)

In-house Publications Alliance for Regional Stewardship (2006). Regional indicator: Telling stories, measuring trends, inspiring action. Alliance for Regional Stewardship. Bauer, R., Baker, B., & Johnson, K. (1996). Sustainable community checklist. 1st ed. Northwest Policy Center. Bjornlund, L. (2000). Beyond data: Current uses of comparative performance measures in local government. International City/County Management Association. Bossel, H. (1999). Indicators for sustainable development: theory, method, applications. International Institute for Sustainable Development. www.iisd.org/pdf/ balatonreport.pdf

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Appendix

British Columbia Ministry of Environment, Lands, and Parks and Commission on Resources and the Environment (1997). Progress British Columbia: An assessment of British Columbia’s progress toward sustainability. British Columbia Ministry of Environment, Lands, and Parks. British Columbia Population Health Resource Branch (1995). Health indicator workbook: A tool for healthy communities. 2nd ed. British Columbia Ministry of Health and Ministry Responsible for Seniors. Chiras, D. (1994). Sustainable development in Colorado: A background report on indicators, trends, definitions, and recommendations. Sustainable Futures Society. Cobb, C. (2000). Measurement tools and quality of life. Redefining Progress. Duhl, L. & Hancock, T. (1988). A guide to assessing healthy cities. WHO Europe/ FADL. Epstein, P. D. (1988). Using performance measurement in local government: A guide to improving decisions, performance, and accountability. National Civic League Press. Hardi, P. & Pinter, L. (1995). Models and methods of measuring sustainable development performance. International Institute of Sustainable Development. Hart, M. (1996). Guide to sustainable community indicators. 2nd ed. QLF/Atlantic Center for the Environment. Hellman, E. (1997). Signs of progress, signs of caution: How to prepare a healthy, sustainable progress report card. City of Tomorrow/Ontario Healthy Communities Coalition. Hren, B. J. & D. M. Hren (eds) (1996). Community sustainability. The Izaak Walton league of America. International Council for Local Environmental Initiatives (ICLEI) (2000). Measuring progress: Cities21 Pilot Project final report. ICLEI. Kingsley, T., Coulton, C., Brandt, M., Sawicki, D., & Tatian, P. (1997). Mapping your community: using geographic information to strengthen community initiatives. Department of Housing and Urban Development. Kinsley, M. J. (1996). Economic renewal guide: a collaborative process for sustainable community development. Rocky Mountain Institute. Kline, E. (1995). Sustainable community indicators. Consortium for Regional Sustainability. Norris, T. (ed) (1994). Healthy communities resource guide. National Civic League Phillips, R. (2003). Community indicators. Planning Advisory Service Report Number 517. American Planning Association. Pinter, L. & Hardi, P. (1995). Performance measurement for sustainable development: compendium of experts, initiatives and publications. International Institute for Sustainable Development. Rowcliffe, P. & Trepanier, R. (1995). Report on community economic development assessment and indicators. British Columbia/Yukon Community Futures Association of the Department of Human Resources Development. Seattle, City of, Office of Sustainability and Environment (OSE) (2001). Sustainable Seattle: our defining challenge. City of Seattle

Appendix

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Seymoar, N. K., Peachey, K., & Midgley, C. (2001). Sustainable communities in the greater Vancouver regional district. International Centre for Sustainable Cities. Scruggs, P. & Selzer, E. (1995). A pathway to sustainability. The Institute for Portland Metropolitan Studies. Smolko, Roxanne (2006). The community indicators handbook: measuring progress toward healthy and sustainable communities, 2nd ed. Redefining Progress. Truckee Meadows Tomorrow (1997). Quality of life in the Truckee meadows. Truckee Meadows Tomorrow. United Way of the Bay Area (1995). Understanding outcomes. Northern California Community Service Council, Inc. Urban Ecology (1996). Blueprint for a sustainable Bay Area. Urban Ecology. U.S. Health Corporation (1994). Healthy community assessment: standard indicators menu. U.S. Health Corporation. Walker, P. (1997). Community visions resource pack. New Economics Foundation. Walker, P. (1997). Creating community visions. New Economics Foundation. Zachary, J. (1995). Sustainable community indicators: guideposts for local planning. Gildea Resource Center.

Bibliography Alliance for Regional Stewardship (2006). Regional indicators: Telling stories, measuring trends, inspiring action. Alliance for Regional Stewardship. Ammons, D. N. (1996). Municipal benchmarks: Local performance and establishing community standards. Sage Publications. Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publishers. Anselin, L. (1992). SpaceStat tutorial. University of Illinois. Anselin, L. (2003). GeoDa 0.9 user’s guide. Spatial Analysis Laboratory, University of Illinois. Attwood, H. E. (2013). The influence of quality-of-life research on quality-of-life: CLIQ case studies from KwaZulu-Natal, South Africa. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 1– 18). Springer. Bank, L., & Kamman, E. (2011). Quality of life in Buffalo City: The changing position of African women in a Post-apartheid city. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 267– 292). Springer. Barbara, F. (2017). Using multivariate statistics. 6th ed. Pearson India. Barrington-Leigh, C., & Escande, A. (2018). Measuring progress and well-being: A comparative review of indicators. Social Indicators Research, 135(3), 839–925. Beritone, G., Parry, S. C., Kubani, D., & Wolch, J. (2006). Indicators in action: The use of sustainability indicators in the City of Santa Monica. In M. J. Sirgy,

208

Appendix

D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 43–60). Kluwer Academic Publishers. Besleme, K., and Mullin, M. (1997). Community indicators and healthy communities. National Civic Review, 86(1), 43–52. Bodenhamer, D. J., Colbert, J. T., Frederickson Comer, K., & Kandris, S. M. (2011). Developing and sustaining a community information system for Central Indiana: SAVI as a case study. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 21–46). Springer. Burd-Sharps, S., Guyer, P. N., & Lechterman, T. (2011). The American Human Development Index: Results from Mississippi and Louisiana. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 113–136). Springer. Burk, J., & Knopf, R. C. (2009). Improving the quality of life in a city of Phoenix, Arizona neighborhood through collaborative investment. In M. J Sirgy, R. Phillips, &D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 1–24). Springer. Burrett, J. (2009). Measuring quality of life in Canadian municipalities. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-of-Life Indicators: Best Cases III (pp. 155–164). Springer. Cradoso, R., Sobhani, A., & Meijers, E. (2021). The cities we need: Towards an urbanism guided by human needs satisfaction. Urban Studies, first published October 7, 2021. https://doi.org/10.1177/00420980211045571. Cavric, B. I. (2011). Integrating tourism into sustainable urban development: Indicators from a Croatian coastal community. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 219–265). Springer. Chambers, M., & Swain, D. (2006). Quality indicators for progress: A guide to community quality-of-life assessments. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II. Springer Publishers. Chanan, G. (2002). Community development foundation measure of community: A study for the active community unit and research. Development and Statistics Unit of the Home Office. Cittaslow (2006). Cittaslow, Vereinigung der lebenswerten Stadte in Deutschland. Retrieved March 24, 2006 from http://www.slowcity-deutschland.de Cobb, C. (2000). Measurement tools and quality of life. San Francisco, CA. Retrieved from http://www.reprogress.org Cobb, C., Goodman, G., & Kliejunas, J. (2000). Blazing sun overhead and clouds on the horizon. The Genuine Progress Report for 1999. Redefining Progress. Retrieved November 1, 2003 from http://www.rprogress.org/projects/gpi Cobb, C., & Rixford, C. (1998). Lessons learned from the history of social indicators. Redefining Progress. Conway, S., Aguero, J., & Navis, I. L. (2009). The Clark County Monitoring System—An early warning indicator system for Clark County, Nevada. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-of-Life Indicators: Best Cases III (pp. 41–77). Springer.

Appendix

209

Cook, D., & Te Linde, J. (2009). The indices of community well-being for Calgary community districts: A neighborhood-based approach to quality of life reporting. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-of-Life Indicators: Best Cases III (pp. 165–179). Springer. Cornwell, T. L. (2003). Vital Signs: Quality-of-life indicators for Virginia’s Technology Corridor. M. J. Sirgy, D. Rahtz, & D-J. Lee (Eds.), Community quality-oflife indicators: Best cases. Kluwer Academic Publishers. Davern, M. T., West, S., Bodenham, S., & Wiseman, J. (2011). Community indicators in action: Using indicators as a tool for planning and evaluating the health and wellbeing of a community. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 319–338). Springer. Delfim Santos, L., & Martins, I. (2013). The moniroting system on quality of life of the city of Porto. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 77–98). Springer. De Sousa, C. A., Gramling, B., & LeMoine, K. (2009). Evaluating progress toward sustainable development in Milwaukee’s Menomonee River Valley: Linking Brownfield’s revelopment with community quality-o-f-life. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-of-Life Indicators: Best Cases III (pp. 79–97). Springer. Dissart, J-C. & Deller, S. C. (2000). Quality of life in the planning literature. Journal of Planning Literature, 15(1). Epstein, P., Coates, P. M., & Wray, L. D., with Swain, D. (2006). Results that matter: Improving communities by engaging citizens, measuring performance, and getting things done. Jossey-Bass Publishers. Estes, R. (2004). Toward a social development index for Hong Kong: The process of community engagement. In M. J. Sirgy, D. Rahtz, & D-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 209–234). Kluwer Academic Publishers. Estes, R. J., Land, K. C., Michalos, A. C., Phillips, R., & Sirgy, M. J. (2017). Wellbeing in Canada and the United States. In R. J. Estes & M. J. Sirgy (Eds.), The pursuit of well-being: The untold global history (pp. 257–300). Springer Publishers. Federal Interagency Forum on Child and Family Statistics (2003). America’s children: Key national indicators of well-being 2002. Federal Interagency Forum on Child and Family Statistics, U.S. Government Printing Office. Findeisen, N. (2006). Using community indicators to improve the quality of life for children: The Sacramento County Children’s Report Card. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 203–228). Kluwer Academic Publishers. Glewwe, P., & van der Gaag, J. (1990). Identifying the poor in developing countries: Do different definitions matter? World Development, 18(6) [published by the World Bank]. Golob, A. (2009). The Long Island Index. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 25–58). Springer.

210

Appendix

Greene, G., Haines, A., & Halebsky, S. (2000). Building our future – A guide to community visioning. Cooperative Extension of the University of Wisconsin. Retrieved from https://learningstore.uwex.edu/Assets/pdfs/G3708.pdf Grunkemeyer, W. T., & Moss, M. L. (2004). Grzeskowiak, S., Sirgy, M. J., Lee, D-J., & Claiborne, C. B. (2006), Housing well-being: Developing and validating a measure. Social Indicators Research, 79, 503–541. Grzeskowiak, S., Sirgy, M. J., & Widgery, R. (2003). Residents’ satisfaction with community services: Predictors and outcomes. Journal of Regional Analysis and Policy, 33(2), 1–36. Haddad, M. A. (2009). Examining the spatial distribution of urban indicators in Sao Paulo, Brazil: Do spatial effects matter? In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-of-Life Indicators: Best Cases III (pp. 99–121). Springer. Hagerty, M. R., Cummins, R., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., Sharpe, A., Sirgy, M. J., & Vogel, J. (2001). Quality-of-life indexes for national policy: Review and agenda for research. Social Indicators Research, 55(1), 1–96. Hammond, A., Rodenberg, E., Bryant, D., & Woodward, R. (1995). Environmental indicators: A systematic approach to measuring and reporting on environmental policy performance in the context of sustainable development. World Resources Institute. Hardi, P., & Pinter, L. (2006). City of Winnipeg Quality-of-Life Indicators. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 127–176). Kluwer Academic Publishers. Hardi, P., & Zdan, T. (1997). Assessing sustainable development: Principles in practice. Winnipeg: International Institute for Sustainable Development. Harrow, J., Jung, T., & Phillips, S. (2016). Community foundations: Agility in the duality of foundation and community. The Routledge companion to philanthropy, pp 308–321. Hart, M. (1999). Guide to sustainable community indicators. QLF/Atlantic, Maine: Center of the Environment. Haworth, J., & Hart, G. (2007). Well-being: Individual, community and social perspectives. Palgrave Macmillan. Heggeness, M. L., Buschmann, P., & Walkington, T. (2004). The 2002 Hennepin County Community Indicators Report: Aligning community indicators with government mission, vision, and overarching goals. In M. J. Sirgy, D. Rahtz, & D-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 111–131). Kluwer Academic Publishers. Helmstetter, C., Mattessich, P., Egbert, A., Brower, S., Hartzler, N., Franklin, J., & Lloyd, B. (2011). Sustaining the operations of the community indicators projects: The case of Twin Cities Compass. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 47–66). Springer. Holden, M. (2006). Sustainable Seattle: The case of the proptotype sustainability indicators project. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community

Appendix

211

quality-of-life indicators: Best cases II (pp. 177–202). Kluwer Academic Publishers. Holden, M., Owens, C., & Mochrie, C. (2009). Lessons from a community-based process in regional sustainability indicator selection: The case of the Regional Vancouver Urban Observatory. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 59–80). Springer. Housing and Urban Development (2020). Community indicator initiatives: A listing of community indicator projects and resources across the country. https:// archives.huduser.gov/healthycommunities/sites/default/files/public/Community %20Indicator%20Projects.pdf Innis, J. E. (1990). Knowledge and public policy: The search for meaningful indicators. Transaction Publishers. Innis, J. E., & Booher, D. E. (2000). Indicators for sustainable communities: A strategy for building complexity theory and distributed intelligence. Planning Theory and Practice, 1(2), 173–186. Jamieson, K. (2004). A collaborative approach to developing and using quality of life indicators in New Zealand’s largest cities. In M. J. Sirgy, D. Rahtz, & D-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 75–109). Kluwer Academic Publishers. Jany-Catrice, F., & Marlier, G. (2013). Regional indicators of well-being: The case of France. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-oflife indicators: Best cases VI (pp. 19–44). Springer. Jarosz, B., & Williams, M. D. (2004). Creating an index to evaluate a region’s competitiveness. In M. J. Sirgy, D. Rahtz, & D-J. Lee (Eds.), Community qualityof-life indicators: Best cases (pp. 183–207). Kluwer Academic Publishers. Johnson, R., & Wichern, D. (2018). Applied multivariate statistical analysis. Pearson. Joint Research Centre-European Commission. (2008). Handbook on constructing composite indicators: Methodology and user guide. OECD Publishing. Kahn, C. (2006). A framework for social change. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 23– 42). Kluwer Academic Publishers. Keiner, M., & Schultz, B. (2004). Measuring sustainability and quality-of-life in the city of Zurich. In M. J. Sirgy, D. Rahtz, & D-J. Lee (Eds.), Community quality-oflife indicators: Best cases (pp. 235–245). Kluwer Academic Publishers. Kingsley, G. T., & Pettit, K. L. S. (2011). Quality of life at a finer grain: The National Neighborhood Indicators Partnership. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 97–111). Springer. Kusago, T. (2011). A sustainable well-being initiative: Social divisions and the recovery process in Minamata, Japan. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 67–96). Springer. Land, K. C., Michalos, A. C., & Sirgy, M. J. (Eds.) (2012). Handbook of social indicators and quality-of-life research. Springer Publishers.

212

Appendix

Land, K. C., & Sirgy, M. J. (2020). Social Indicators. In L. Spillman (Ed.), Oxford bibliographies in sociology. Oxford University Press. Lee, S., & Guhathakurta, S. (2013). Bridging environmental sustainability and quality of life in metropolitan Atlanta’s urban communities. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 207–231). Springer. Leonard, J. (2014). Merging money and mission: Becoming our community's development office. In T. Mazany & D. Perry (Eds.), Here for good community foundations and the challenges of the 21st century (pp. 107–122). M.E. Sharp. Lewis, K., & Burd-Sharps, S. (2010). The measure of America 2010–2011: Mapping risks and resilience. A joint publication of the Social Science Research Council and New York University Press. Linoff, G. S. (2004). Survival data mining for customer insight. Intelligent Enterprise, 7(12), 28–33. Lockwood, B., Martin, J., Yinghui Cao, C., & Schmitt, M. (2011). The Metropolitan Philadelphia Indicators Project: Measuring a diverse region. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 137–154). Springer. Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (Eds.) (1998). Geographical information systems: Principles, techniques, management and applications. York: John Wiley and Sons. Lopez-Tamayo, J., Royuela, V., & Surinach, J. (2013). Building a “quality in work” index in Spain. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community qualityof-life indicators: Best cases VI (pp. 233–262). Springer. Marcuse, P. & van Kempen, R. (Eds.) (2002). Of states and cities: The partitioning of urban space. Oxford University Press. Martinez-Fernandez, C., & Potts, T. (2009). Quality of life through innovation indicators: The case of peripheral suburbs of Sydney. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-of-Life Indicators: Best Cases IV (pp. 191–207). Springer. Max-Neef, M. (1989). Human scale development: Conception, application and further reflections. Apex. Mayer, H., & Knox, P. (2006). Pace of life and quality of life: The Slow City charter. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases III (pp. 21–39). Springer. Mazany, T., & Perry, D. C. (2014). The second century: Community foundations as foundations of community. In T. Mazany & D. C. Perry (Eds.), Here for good: Community foundations and the challenges of the 21st century (pp. 17–40). Routledge. McAslan, D., Prakash, M., Pijawka, D., Guhathakurta, S., & Sadalla, E. (2013). Measuring quality of life in border cities: The Border Observatory Project in the US-Mexico border region. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 143169). Springer.

Appendix

213

Metropolitan Studies Group (2010). Charlotte Neighborhood Quality of Life Study. Retrieved March 27, 2022, from Microsoft Word - 2010_5_19_Quality_of_Life_ Report.doc (charlottenc.gov) Mohanty, S. (2009). Quality of life and cultural diversity in Peel Region (Ontarion, Canada). In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-ofLife Indicators: Best Cases III (pp. 123–154). Springer. Mohanty, S. (2011). Portraits of Peel – A community indicators portal project. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 155–169). Springer. Moller, V., & Radloff, S. (2011). Monitoring indicators of living conditions in a South African urban community. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 293–318). Springer. Moreno Minguez, A. (2009). Gender equality and quality of life: Examples of best practices from nine European cities: The EQUALABEL Project. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 163–189). Springer. Murphy, D. A. (1999). Presenting community-level data in outcomes and indicators framework: Lessons from Vermont’s experience. Public Administration Review, 59(1), 76–77. Okubo, D. (2000). The community visioning and strategic planning handbook. National Civic League Press. Olson, D. L., & Delen, D. (2008). Advanced data mining techniques. SpringerVerlag. Patten, M., & Lyons, S. (2009). Vital signs: Connecting community needs to community philanthropy in Canada. The Philanthropist, 22, 56–61. Philipp, A., & Traylor, T. (2014). Ensuring there is “community” in the community foundation. In T. Mazany & D. C. Perry (Eds.), Here for good: Community foundations and the challenges of the 21st century (pp. 137–144). Routledge. Phillips, R. (2005). Community indicators measuring systems. Ashgate Publishing Limited. Phillips, R., Sung, H-K, & Whitsett, A. (2013). State level applications. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 99–117). Springer. Phillips, R., & Wong, C. (2017). Introduction. In R. Phillips & C. Wong (Eds.), Handbook of Community Well-Being Research (pp. xxiv–xxxviii). Springer. Pituch, K. A. (2015). Applied multivariate statistics for the social sciences. Routledge. Prabhakar, D. Qualls-Hampton, R. Y., Jackson, R., & Cardarelli, K. M. (2009). Mental Health Indicator Parity: Integrating national, state, and local data. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 81–109). Springer. Prescott-Allen, R. (2001). The wellbeing of nations. Island Press. Rabinowitz Bussell, M., & Sheldon, K. (2011). Comprehensive local community development via collaborative quality of life planning: Best practices from two San Diego neighborhoods. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases V (pp. 1–20). Springer.

214

Appendix

Rasker, R., Johnson, J., & York, V. (1996). Measuring change in rural communities: A workbook for determining demographic, economic, and fiscal trends. The Wilderness Society. Rahtz, D., & Sirgy, M. J. (2000). Marketing of health care within a community: A quality-of-life needs assessment model and method. Journal of Business Research, 4, 165–176. Rahtz, D. R., Sirgy, M. J., & Lee, D-J. (2004). Further validation and extension of the quality-of-life/community-healthcare model and measures. Social Indicators Research, 69(2), 167–198. Rawls, J. (1971). A theory of justice. Belknap Press of Harvard University Press. Rawls, J. (1975). Fairness to goodness. Philosophical Review, 84, 530–543. Reyes, C. (2003). Diagnosing poverty at the local level. Micro Impacts of Macroeconomic Adjustment Policies, 10(3), 1–3. Rezvani, M. R., & Mansourian, H. (2011). The development of quality-of-life indicators in rural areas in Iran: Case study – Khaveh Shomali District, Lorestan Province. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-oflife indicators: Best cases V (pp. 171–191). Springer. Richards, R. (2011). Working for water: A baseline study on the impact of a South African public works program in improving the quality of life of program beneficiaries. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community qualityof-life indicators: Best cases V (pp. 193–217). Springer. Richards, R., & Kamman, E. (2006). Living in a post-apartheid city: A baseline survey of quality of life in Buffalo City. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 229–248). Kluwer Academic Publishers. Ridzi, F. (2017). Community indicators and the collective goods criterion for impact. In M. Holden, R. Phillips, & C. Stevens (Eds.), Community quality-of-life indicators: Best cases VII (pp. 35–52). Springer. Ridzi, F., & Prior, T. (2020). Community leadership through conversations and coordination: The role of local surveys in community foundation run community indicators projects. International Journal of Community Well-Being. https://doi. org/10.1007/s42413-020-00098-z. Sawicki, D. S. & Flynn, P. (1996). Neighborhood indicators: A review of the literature and an assessment of conceptual and methodological issues. Journal of the American Planning Association, 62(2), 165–183. Schmandt, H. J. & Bloomberg, W. (Eds.) (1969). The quality of urban life. Sage Publications. Schuster, L., & Ciurczak, P. (2018). A report from Boston Indicators: Boston’s booming . . . but for whom? Building shared prosperity in a time of growth. The Boston Foundation. Retrieved from https://www.bostonindicators.org/-/media/ indicators/boston-indicators-reports/report-files/bostons-booming-2018.pdf? la¼en&hash¼94DE67E74983CB7DF3EBCB4EFA80F02346719C8B Schyns, P., & Boelhouwer, J. (2004). The state of the city Amsterdam monitor: Measuring quality of life in Amsterdam. In M. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators (pp. 133–152). Springer.

Appendix

215

Shepherd, S., & McMahon, S. (2009). The importance of local information: Quality of life indicators in Bristol. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 111–119). Springer. Shultz II, C. J., Rahtz, D. R., & Sirgy, M. J. (2017). Distinguishing flourishing from distressed communities: Vulnerability, resilience, and a systemic framework to facilitate well-being.” In R. Phillips & C. Wong (Eds.), The handbook of community well-being (pp. 403–422). Springer Publishers. Sirgy, M. J. (2001). Handbook of quality-of-life research: An ethical marketing perspective. Kluwer Academic Publishers. Sirgy, M. J. (2011). Theoretical perspectives guiding QOL indicator projects. Social Indicator Research, 103, 1–22. Sirgy, M. J. (2016). Toward a new concept of residential well-being based on bottom-up spillover and need hierarchy theories. In F. Maggino (Ed.), A life devoted to quality-of-life research: Festschrift in honor of Alex C. Michalos (pp. 131–150). Springer Publishers. Sirgy, M. J. (2017). Distinguishing indicators of well-being from indicators of ill-being. In R. E. Anderson (Ed.), Alleviating world suffering: The challenge of negative quality of life (pp. 87–108). Springer Publishers. Sirgy, M. J. (2018). What types of indicators should be used to capture community well-being comprehensively? International Journal of Community Well-Being, 1(1), 3–9. Sirgy, M. J., & Cornwell, T. (2002). How neighborhood features affect quality of life. Social Indicators Research, 59, 79–114. Sirgy, M. J. & Cornwell, T. L. (2001). Further validation of the Sirgy et al.’s measure of community quality of life, Social Indicators Research, 56, 125–143. Sirgy, M. J., Estes, R. J., & Selian, A. N. (2017). How we measure well-being: The data behind the history of well-being. In R. J. Estes & M. J. Sirgy (Eds.), The pursuit of well-being: The untold global history (pp. 135–160). Springer Publishers. Sirgy, M. J., Gao, T., & Young, R. F. (2008). How does residents’ satisfaction with community services influences quality of life (QOL) outcomes? Applied Research in Quality of Life, 3, 81–105. Sirgy, M. J., Mentzer, J. T., Rahtz, D. R., & Meadow, H. L. (1991). Satisfaction with healthcare marketing services consumption and life satisfaction among the elderly. Journal of Macromarketing, 11(1), 24–39. Sirgy, M. J., Michalos, A. C., Ferriss, A. L., Easterlin, R., Patrick, D., & Pavot, W. (2006). The quality-of-life (QOL) research movement: Past, present, and future. Social Indicators Research, 76(3), 343–466. Sirgy, M. J., Rahtz, D., Cicic, M., & Underwood, R. (2000). A method for assessing residents’ satisfaction with community-based services: A quality-of-life perspective. Social Indicators Research, 49, 279–316. Sirgy, M. J., & Rahtz, D. (2006). A measure and method to assess community quality-of-life. In M. J. Sirgy & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases II. Springer Publishers. Sirgy, M. J., Rahtz, D., Lee, D-J. (Eds.) (2004). Community quality-of-life indicators: Best cases. Kluwer Academic Publishers.

216

Appendix

Sirgy, M. J., Rahtz, D., & Swain, D. (Eds.) (2006). Community quality-of-life indicators: Best cases II. Kluwer Academic Publishers. Sirgy, M. J., Phillips, R., & Rahtz, D. (Eds.) (2009). Community quality-of-life indicators: Best cases IV. Springer Publishers. Sirgy, M. J., Phillips, R., & Rahtz, D. (Eds.) (2009). Community quality-of-life indicators: Best cases III. Springer Publishers. Sirgy, M. J., Phillips, R., & Rahtz, D. (Eds.) (2011). Community quality-of-life indicators: Best cases V. Springer Publishers. Sirgy, M. J., Phillips, R., & Rahtz, D. (Eds.) (2013). Community quality-of-life indicators: Best cases VI. Springer Publishers. Sirgy, M. J., Rahtz, D., Cicic, M., & Underwood, R. (2000). A method for assessing residents’ satisfaction with community-based services: A quality-of-life perspective, Social Indicators Research, 49, 279–316. Sirgy, M. J., Widgery, R., Lee, D-J., & Yu, G. B. (2010). Developing a measure of community well-being based on perceptions of impact in various life domains. Social Indicators Research, 96(2), 295–311. Smith, K. A., & Gupta, J. N. D. (2000). Neural networks in business: Techniques and applications for operations researchers. Computer & Operations Research, 27, 1023–1044. Smolko, R., Strange, C. J., & Ventoulis, J. (2006). The community indicators handbook: Measuring progress toward healthy and sustainable communities (2nd ed.). Redefining Progress. Statistics New Zealand (2002). Monitoring progress towards a sustainable New Zealand. Statistics New Zealand. Stevens, C., deBlois, M., Hemberg, R., Baldwin, J., & the Community Indicators Consortium (2021). Community indicators project development guide. Community Indicators Consortium. Sung, H., & Phillips, R. (2016). Conceptualizing a community well-being and theory construct. In Y. Kee, S. J. Lee, & R. Phillips (Eds.), Social factors and community well-being (pp. 1–13). Springer. Sung, H-K., & Phillips, R. G. (2018). Indicators and community well-being: Exploring a relational framework. International Journal of Community Well-Being, 1(1), 63–79. Swain, D. (2002). Measuring progress: Community indicators and quality of life. Jackson Community Council Inc. Swartz, N. (2004). IBM, Mayo clinic to mine medical data. The Information Management Journal, 38(6), 8. Tabachnick, B. G. (2019). Using multivariate statistics. Pearson Education. Terry, A. (2009). Understanding the political significance of community sustainable development indicators in post-apartheid South Africa: A case study from Sobantu Township, Pietermaritzburg. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 121–139). Springer.

Appendix

217

United Nations Commission on Sustainable Development (2001). Indicators of sustainable development: Guidelines and methodologies. United Nations Division for Sustainable Development. United Nations Development Programme (1998). Human Development Report 1998. Oxford University Press. US Census Bureau (2019). 2010 census tallies of census tracks, block groups & blocks. US Census Bureau, January 21, 2019, Retrieved on March 9, 2021 from 2010 Census Tallies of Census Tracts, Block Groups & Blocks - Geography U.S. Census Bureau (archive.org) Waddell, S. (1995). Lessons from the Healthy Cities Movement for social indicator development. Social Indicators Research, 34, 213–235. Warner, J. B. (2006). The Jacksonville, Florida, experience. In M. J. Sirgy, D. Rahtz, & D. Swain (Eds.), Community quality-of-life indicators: Best cases II (pp. 1–22). Kluwer Academic Publishers. Warner, J. B. (2009). Jacksonville’s Race Relations Progress Report: Creating change through community indicators. In M. J Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases IV (pp. 141–161). Springer. Warner, J. B. (2014). The future of community indicator systems. In Federal Reserve Bank of San Francisco & the Urban Institute (authors). What Counts: Harnessing Data for America’s Communities, pp 45–57. http://www.whatcountsforamerica. org/portfolio/the-future-of-community-indicator-systems/ Warner, L. H., & Heath, A. A. (2013). The Fox River Region leading indicators for excellence: The benefits and challenges of regional collaboration. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 171–206). Springer. Weffer, S. E., Mullooly, J. J., Sylvester, D. E., DeLugan, R. M., & Hernandez, M. D. (2013). State level applications. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 119–141). Springer. Whyte, B., & Lyon, A. (2013). Understanding Glasgow: Developing a new set of health and wellbeing indicators for use within a city. In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community quality-of-life indicators: Best cases VI (pp. 45–76). Springer. Widgery, R. (2004). A three-decade comparison of residents’ opinions on and beliefs about life in Genesee County, Michigan. In M. J. Sirgy, D. Rahtz, & D-J. Lee (Eds.), Community quality-of-life indicators: Best cases (pp. 153–182). Kluwer Academic Publishers. Wismer, S. & Vaidyanathan (2002-03). Quality of life indicators and sustainable community development: A case study in Wollwich Township, Ontario, Canada. Indicators, 2(1), 113–139. Zachary, D. (2009). Connecting outcomes to indicators: The Santa Cruz County California Community Assessment Project (CAP). In M. J. Sirgy, R. Phillips, & D. Rahtz (Eds.), Community Quality-of-Life Indicators: Best Cases III (pp. 1–20). Springer.