A study of the prediction of accident-proneness of motorcycle operators

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A STUDY OP THE PREDICTION OP ACCIDENT-PRONENESS MOTORCYCLE OPERATORS

A Dissertation Presented to the Faculty of the Graduate School The University of Southern California

In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

by Paul C . Buchanan June 1950

UMI Number: DP30386

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T h is dissertation, w ritten by Pajjil..McMnM........................ under the guidance of hJJ..... F a c u lty C om m ittee on Studies, and app ro ved by a l l its members, has been presented to and accepted by the C o u n c il on G ra d u ate Study and Research, in p a r tia l f u l ­ fillm en t of requirements f o r the degree of DOCTOR

OF

P H IL O S O P H Y

D ean

.....

Committee on Studies

*

TABLE OF CONTENTS CHAPTER I.

PAGE

THE PROBLEM AND OUTLINE OF THE S T U D Y ...........

1

The p r o b l e m ..................................

1

Statement of the problem . . . . ............

1

Importance of the s t u d y ...................

2

Organization of the remainder II,

REVIEW OF THE LITERATURE .

...................

4 5

Generality of factors in accident-proneness

•

8

.......................

10

................................

13

DATA AND P R O C E D U R E S ............................

15

Specification of the p r o b l e m .................

15

The population sample

17

Summary

IV.

3

The nature of accident d a t a ...........

Methods of prediction

III.

ofthe study . .

...............

The c r i t e r i o n ...............................

18

Tests u s e d ....................................

28

Methods of analyzing the d a t a ...............

30

Selection of test b a t t e r i e s ...............

30

Testing the h y p o t h e s e s .....................

35

ANALYSIS OF THE DATA AND PRESENTATION

OFRESULTS

The t-ratio p r o c e d u r e s ................... The multiple regression procedure The modified multiple cuttingscore procedure

37 37

.

43 45

iii CHAPTER

PAGE The successive cutting score procedure . . . •

47

Results of the application of these procedures

55

Factors affecting the interpretation of r e s u l t s ....................................

58

Limitations of the c r i t e r i a ...............

58

Applicability of test selection procedures .

60

Restriction of range in the test scores

61

. •

Evaluation of the h y p o t h e s e s .......... General considerations......................

62

Evaluation of

the first

65

Evaluation of

the second hypothesis

Evaluation of

the third

hypothesis .... . . . .

66

hypothesis ....

66

ofspecific tests . . .

67

SUMMARY AND C O N C L U S I O N S .......................

70

S u m m a r y ......................................

70

C o n c l u s i o n s ..................................

73

Relative effectiveness V.

62

BIBLIOGRAPHY

..................................

76

APPENDIX A .............................................

79

APPENDIX B .............................................

89

LIST OP TABLES TABLE 1.

PAGE

Distribution of Ages for Criterion and Experi­ mental G r o u p s ..................................

2•

21

Distribution of Safety Quotients for Criterion G r o u p .................. . ......................

3.

22

Distribution of Accidents During the Sixteen Month Period for the Experimental Group . . . .

4.

24

Criterion Sub-Groups Matched for Experience on Basis of Total Accident Criterion..............

5*

js-Ratios of Differences Between Proportions of Top and Bottom Halves of the Criterion Group

•

6.

Work Sheet for Optimal Cutting Score Method . . .

7.

Correlations Between Tests and the Criterion

8.

Work Sheet for Multiple Cutting Score Procedure: Median Score of Each Criterion Group

9.

27

•

........

39 41

.

44

46

Work Sheet for Multiple Cutting Score Procedure: Median Criterion Score and Per Cent Selected by Single Test Score Standards

10.

...............

48

Work Sheet for Multiple Cutting Score Procedure: Median Criterion Scores and Per Cent Selected by Multiple Score Standards ...................

11*

49

Work Sheet for Multiple Cutting Score Procedure: Combination of Best Multiple Cuttings .........

50

TABLE 12*

PAGE

Work Sheet for Successive Cutting Score Procedure: Median Scores for Top Half of Criterion Group on Each T e s t ..................................

13*

51

Work Sheet for Successive Cutting Score Procedure: Median Accident Hates and Percentages Selected by Combinations of Test S c o r e s ...............

14*

Summary of Methods of Selection as Applied to the Criterion Group ................................

15*

53

Effectiveness of the Predictions from Test Batteries Selected by Each Method .............

16*

52

56

Test Scores Selected by the Various Selection Procedures, Including Specific Cutting Scores •

59

CHAPTER I THE PROBLEM AND OUTLINE OP THE STUDY The reduction of accident rates among operators of motor vehicles has been a major problem in the transportation industry, and since the beginning of industrial psychology has been the subject of extensive research both in this country and abroad.

Its importance arises from the loss in

human life or permanent injury to the persons concerned and from the damage of property and equipment which result from traffic accidents, I,

THE PROBLEM

The present study was an attempt to detect accidentprone operators of motorcycles through the use of psycho­ logical tests, and touched only indirectly on psychological factors in training, indoctrination, or discipline.

The

motorcycle operators concerned in the study were members of the Traffic Enforcement Division of the Los Angeles Police Department, Statement of the problem.

Three hypotheses concerning

the detection of accident-proneness were posited in the planning of the study: 1,

Pencil and paper tests can be used to distinguish effectively between motorcycle riders with good

accident records and those with poorer accident records• 2.

Paper and pencil tests which were successfully applied in the selection of bus and streetcar operators also distinguish between good and poor motorcycle operators*

3.

Test batteries selected by cutting score pro­ cedures are as effective as those selected by multiple regression procedures.

Importance of the study.

The importance of the first

hypothesis lies in the greater saving of time and expense involved in the use of paper and pencil tests as compared with apparatus tests.

In a larger sense this also involves

the whole question of whether or not characteristics of accident-proneness can be predicted.

The second hypothesis

deals with the generality of characteristics of safe motor vehicle operators, and is important from a scientific as well as a practical standpoint.

The third one is important es­

pecially from the view of industrial psychology where simple procedures are more acceptable to management than psycho­ logical techniques which require skills and technical knowledge frequently not available to the industrial staff, and because the cutting score procedure involves less time in application than the regression techniques.

3 II.

ORGANIZATION OF THE REMAINDER OF THE STUDY

In the next chapter is presented a review of the literature, with special emphasis on the distribution of accident data, their use as a criterion of accident-proneness, and on the methodology of research.

Chapter III is a

description of the sample population on which the study is based, of the criteria of accident-proneness used, of the prediction measures, and of the manner in which the data were analyzed.

In Chapter IV the data are presented and

analyzed and the results of the study are given.

The study

is summarized and conclusions are stated in the final chapter.

CHAPTER II REVIEW OF THE LITERATURE Historically, investigation of accident-proneness in motor vehicle operators has followed two general lines.

One

has been the interest of industry in selecting vehicle operators who have relatively few accidents, the other the interest of departments of public safety in eliminating dangerous drivers from public highways and in training them to become safe drivers. The pioneer work of Munsterburg and later Greenwood and Woods, Marbe, Newbold, Viteles,1 etc., dealt mainly with industrial selection, but during the 1930’s large-scale studies were made of factors common to safe drivers on the highways.

A summary of the early work In the field is given 2 by Viteles and a critical review of all work completed prior to 1939 is supplied by Johnson.5

In almost every case the

interest in these studies has been (1) to examine accident

1 The original articles describing the work of these authors are not available in local libraries. Information concerning them has been found in secondary sources, mainly in Morris S. Viteles, Industrial Psychology (New York: W. W. Norton Company, 1932), Chapters XVII and XVIII. ^ Lac. cit. 3 H. M. Johnson, ’’Detection and Treatment of Accident Prone Drivers,” Psychological Bulletin, 43:489-532, November, 1946.

data to determine their statistical characteristics,

(2) to

isolate and measure traits of vehicle operators which are factors in accident-proneness, and (3) to minimize the effect of these characteristics by retraining or by screening out from men selected as operators those who have such qualities. This chapter presents a brief summary of research relating directly to the problems at hand, with emphasis on methodological considerations. I.

THE NATURE OF ACCIDENT DATA

One of the major issues in research on accidents has concerned the nature of accident data.

Greenwood and Woods

were the first to consider the question carefully.

4

They

formulated three hypotheses as to the occurrence of accidents, expressed each in terms of an appropriate formula from which the frequency of occurrences of zero, one, or more accidents could be predicted, and analyzed several sets of accident data in terms of these hypotheses.

Their conclusion, sub­

stantiated by subsequent research, most recently by Mintz and Blum,^ is that people differ initially In their liability to accidents and that the data approximate a composite Poissonian 4 Viteles,

0 £.

cit., pp. 335-40.

5 A. Mintz and M. L. Blum, flA Re-examination of the Accident Proneness Concept,” Journal of Applied Psychology, 33:195-211, June, 1949.

distribution which is a continuous skewed unimodal curve (Pearson Type III)*

In most cases this hypothesis has been

evaluated by comparing the obtained distribution of accidents with the distribution predicted from the hypothesis* method was also used by Greenwood and Woods.

Another

They found the

correlation between the accident records of factory employees during two successive three-month periods to see if those who had high records during the first period also had high records during the second*

Recently Johnson

7

also used this method*

He found that the correlation between the data for the first and second periods in several sets of accident records was between 4*0.50 and 4=0.50.

Both series of investigations are

considered to support the hypothesis of unequal accident liability. Validation of the above mentioned hypothesis concerning the distribution of accidents has been of fundamental^Im­ portance in accident research.

First, it provides a rationale

for research by showing that accidents involve more than just chance factors.

It also implies a differentiation between

accident rate and accident-proneness.

A driver’s accident

rate is a function of unsystematic (chance) factors in the driving situation, of systematic factors other than character­ istics of the driver himself (such as his use of a poor 6 Viteles, ££. cit., pp. 338-39. 7 Ibid., p. 499.

7 machine), and of the driverfs accident-proneness, where proneness is defined as factors or characteristics of the driver himself which increase his likelihood of having an accident*

Then this hypothesis provides a method of determi­

ning the extent to which the accident rate computed from the records of a certain set of drivers is due to chance or un­ systematic factors and how much is due to systematic factors* For individual drivers in a sample, the systematic factors, such as length of exposure to hazards, conditions of roads, condition of equipment used, etc., are equated for all the drivers in the sample*

For example, if one hundred motorcycle

officers drive the same number of miles under the same driving conditions and with similar equipment, etc., the differences among their accident rates will be the result of chance factors and of their individual accident-proneness.

The

method in question applies only when systematic factors in the environment are very nearly alike for all drivers in the sample.

The method, discussed by Cobb,

Q

is based on the fact

that in a Poissonian distribution the variance is equal to the mean, and so in a composite of Poissonian distributions the mean of the variances is equal to the mean of the means. Each of the composite distributions, representing drivers of

8 Percy W. Cobb, ”The Limit of Usefulness of Accident Rate as a Measure of Accident-Proneness,” Journal of Applied Psychology, 24*154-59, 1940.

a given proneness to accidents, would constitute a sub-group. The variance due to chance factors is equal to the mean number of accidents per person.

This variance subtracted

from the obtained variance of the accident records gives an estimate of the variance due to differences in accidentproneness;

thus the per cent of variance in the accident data

which is due to differences in proneness can be found.

This

procedure enables one to determine the value of a set of accident data as a criterion, provided the driving hazards, as mentioned above, are relatively constant for all members of the sample. II.

GENERALITY OF FACTORS IN ACCIDENT-PRONENESS

Any study of accident-proneness involves certain assumptions concerning the generality of the characteristics of accident-prone drivers.

For prediction to be possible one

or more factors must be operative in all or at least certain kinds of accidents.

That is, any pertinent factor must be

operating in all individuals who have accidents, or in all who have a certain type of accident.

This problem has im­

portant implications for the designing of research.

First,

if an individual has different liability for different kinds of accidents, his total accident record would have to be separated into specific types of accidents, the number he has of each type constituting separate criteria.

A recent study

by Brown and Ghiselli

9

deals with this question*

While the

results are not conclusive, the authors interpret them as giving little support to the generality of accident-proneness* In a study of a large number of vehicle operators in Con­ necticut, C o b b ^ tested the hypothesis that personal weak­ nesses disclosed by tests are related to types of accidents to which the operators are especially susceptible*

For

example, if defects of color vision are factors in accidentproneness, then drivers with these defects should have more accidents at signal-protected crossings than drivers without the defects*

His results were essentially negative.

These

two studies raise the question as to how accident data should be classified in order that the generality of proneness could be evaluated*

Perhaps a first step should be to factor

analyze a large set of accident data.

If different factors

were found, and the factors were then used as separate criteria, the predictable variance of the criterion would be increased, thus improving the basis for evaluating predictive measures* The question of the generality of factors in accidentproneness influences the design of the research, in another

9 C. W. Brown and E* E. Ghiselli, "Accident Proneness, Among Street Car Motormen and Motor Coach Operators," Journal of Applied Psychology* 32:20-23, February, .1948* Johnson,

0 £.

cit•, pp. 514-17.

10 way.

If there are several factors in an individual's

liability to accidents or to certain types of accidents, the question arises as to whether or not a high score on a measure of one or more factors will compensate for a low score on a test of one or more factors.

The answer to this

question has an important hearing in determining the method of selecting measures to be used in a test battery.

For

example, the use of multiple regression procedures for selecting tests for a battery involves the assumption of compensation while the use of a multiple cutting score pro­ cedure does-not, III.

METHODS OF PREDICTION

The various measures which have been used in research on accident-proneness fall into the following types? 1,

Work sample.

The driver is requested to operate

a vehicle while being observed by a trained examiner, 2,

Biographical inventory.

An attempt is made to

find a set of data concerning the driver's personal history which is significantly related to accident-proneness, 3,

Indirect examination.

This procedure includes

^ J, P. Guilford, Fundamental Statistics in Psychology and Education, (New York? McGraw-Hill Book Company, 1942), p. 292.

11 the use of apparatus tests, pencil and paper tests, and physiological tests of blood pressure, vision, etc. for the purpose of finding a measure which correlates with accident-proneness. 4.

Psychiatric or clinical.

Drivers are examined by

psychiatric methods in order that the combination of factors related to accident-proneness might be determined. The work-sample as a measure is limited by the fact that the sample of behavior used is necessarily small and therefore unlikely to be typical of the driver’s behavior, and the conditions under which it is obtained are not likely to be representative of accident provoking situations.

The

psychiatric method is too expensive for regular use in the transportation industry and in general has not increased the 12 13 efficiency of selection. However, the work of Dunbar seems to be at least suggestive.

She and her collaborators

examined the Borschach scores and biographical data of a group of patients hospitalized for fractures incurred in various ways and compared them with the same information on other medical groups.

She did not report the use of

12 Johnson, o£. cit., pp. 518 ff. 13 Flanders Dunbar, Psychosomatic Diagnosis (New York: Harper and Bros., 1943). Flanders Dunbar” hMedical Aspects of Accidents and Mistakes in the Industrial Army and in the Armed Forces,” War Medicine, 4:161-75, 1943.

quantitative procedures in determining the significance of differences among groups, but she did report that she found several characteristics which appeared to differentiate the fracture patients from the other groups*

It would appear

appropriate to subject her findings to careful statistical analysis, and to attempt further validation.

This could be

done by use of a biographical inventory, or perhaps of a projective test. Cobb's study seems to indicate that the biographical inventory is a fruitful method of predicting accidentproneness.

In his study 14 of the Connecticut drivers he

found that a battery of nine tests, most of which were bio­ graphical, gave a shrunken multiple correlation with accident rate of *0.31 while a battery of twenty-two tests, several of which involved elaborate apparatus, gave a shrunken multiple correlation of +0.35. The indirect examination method has been used most frequently, especially tests of reaction time, visual acuity, emotional stability, eye-hand coordination, and perceptual speed.

The results of research projects using these types of * i r

tests are summarized rby Viteles 14 Johnson,

0 £*

j

and Ryan.

In most of *the

cit., p. 505.

15 Viteles, o£. cit., Chapter XVIII. 16 Thomas A. Ryan, Work and Effort (New York: The Ronald Press Company, 194717 Chapter 12.

13 literature dealing with this subject relatively low relation­ ships have been found between measurement scale and accident rate, but as is also the case with other methods, one cannot be sure whether the low predictive value is due to lack of precision or applicability of the measure or to the fact that the criterion is inadequate.

The application of Cobb's pro­

cedure (described above) to these data would help clarify this question.

A further limitation of the studies is that

in few cases is prediction made on a group other than that on which the measure was standardized and thus the amount of shrinkage to be expected in its application is not given. IV.

SUMMARY

Prom this brief review of the literature the following conclusions pertinent to the planning of the present study are drawn: 1.

Analysis of many sets of accident records indicate that the occurrences of accidents are distributed according to a modified Poissonian curve of Pearson’s type III, thus supporting the hypothesis that individual motor vehicle operators differ in their liability for accidents.

2.

Accident-proneness can be defined as the likelihood of a driver's having accidents because of factors or characteristics of the driver himself.

The percentage of the variance in a set of accident data which is due to differences in accidentproneness of the drivers can be appraised, and from this can be estimated the highest possible prediction efficiency of a test battery* There is inadequate information concerning the hypothesis that persons having high liability for one kind of accident also have high liability for other kinds, but it appears that this question could be evaluated through a factor analysis of a large set of accident data. Whether or not high scores on one measure of accident-proneness will compensate for low scores on another has an important bearing on the type of procedure used in selecting tests for use in a battery. With the exception of the work-sample method, there is no strong indication as to which type of prediction method should be used, although bio­ graphical inventories and indirect examinations seem to offer the best possibilities in an indus­ trial situation.

It is apparent, however, that

regardless of the method used, the value of the measure should be determined by applying it to a set of data other than that on which the tests were selected.

In very few studies has this been done.

CHAPTER III DATA AND PROCEDURES As stated in the introduction, three hypotheses were being examined in this study.

One dealt with the usefulness

of paper and pencil tests, one with the generality of factors in accident-proneness, and the third with methodology of treating data.

This chapter describes the manner in which

the conclusions drawn from the review of the literature in the previous chapter were applied to the material of the present study in attempting to evaluate these hypotheses. I.

SPECIFICATION OF THE PROBLEM

In a recent study Wilson

1

used a battery of tests in

detecting accident-prone bus and streetcar operators.

His

results were sufficiently significant to suggest that the same tests be applied to a different sample of drivers.

The

use of his tests on a different type of vehicle operators also would permit an examination of the generality of factors in accident-proneness by applying to the new type of operator 1 Clark L. Wilson, Jr., "A Comparison of the Biserial Correlation Technique with a Non-Correlation Method in the Prediction of Collision Accidents among Bus and Street Car Operators," Research paper on file at the Library, University of Southern California, Los Angeles, 1948. James H. Collins, "Accidents— A Broader Approach," Public Utilities Fortnightly, 40:693-700, November 20, 1947.

16 the critical scores which he found on the bus and streetcar operators*

All the tests which he used were applied in the

present study* Wilson also compared the relative effectiveness of a battery of tests selected by a multiple regression technique with that of a battery selected by a critical score procedure* However, he made this comparison only on the groups on which the tests were selected so was unable to compare their effec­ tiveness when applied to a new group of operators*

The

present study was designed to provide a comparison of the selection methods when the test batteries were applied to new groups of operators. It is obvious that the first hypothesis could not be disproved by a study such as the present.

This was because

only a limited number of tests could be evaluated, and to disprove it one would have to exhaust the number of pertinent tests.

This study was designed, however, to provide the

possibility of an affirmative answer.

It was also obvious

that the third hypothesis could be affirmed in a strict sense only if the data to which the two procedures were applied completely fit the requirements of each procedure.

In the

correlational procedure this involved normality of distribution and homoscedasticity.

The data in this study might not have

met these requirements, as will be considered later.

This

condition has to be considered in the comparison of the

17 results of the two methods.

But to the extent to which these

data were similar to those usually found in accident records the conclusions can be generalized as to the effectiveness of the procedures in selecting batteries for detecting accidentprone drivers. II.

THE POPULATION SAMPLE

The operators on which this study was based were the motorcycle officers of the Traffic Enforcement Division of the Los Angeles Police Department.

This division consists of

approximately three hundred officers who spend approximately eight hours per day, on rotated shifts, riding motorcycles in enforcing traffic regulations.

To apply for police duty in

Los Angeles a man must be between twenty-one and thirty-one years of age, have a high school education or equivalent, and be between 5 feet 9 Inches and 6 feet 5 inches in height and within normal weight limits.

He is given a series of written

tests consisting of the Humm-Wadsworth Personality Test, and two of the following:

Otis Self-Administering Test, the Army

Alpha, American Council on Education Psychological Examination, or California Test of Mental Maturity; a series of measures of physical fitness, strength, agility, and endurance, and an oral interview by a board of three examiners.

Men who have

met the standards set for these measures and have completed their basic police training, with or without experience in

18 other divisions of the police department, can volunteer for duty with the traffic enforcement division.

Officers are

selected from these volunteers on the basis of their scores on the above measures and. an interview by a selection board. At the time this study was begun the enforcement division was expanding greatly, approximately 150 men being selected and trained within a three-month period.

There were

already 156 officers on motorcycle duty in the division, making the total a little over 300.

Of those already on duty

all but four had been on duty for at least one year.

These

officers were tested for the present study in December, 1947. Results from all the tests were obtained from only 124 of them due to omissions of names, failure to take all the tests, etc.

This group was used as a basis for evaluating the tests

and establishing critical scores, and is referred to hereafter as the criterion group.

The newly selected officers were

tested during the second week of their six-month trainingperiod, during the early part of 1948.

Throughout this study

this group will be referred to as the experimental group. III.

THE CRITERION

The Traffic Enforcement Division has maintained detailed records of all accidents in which officers have been involved since the reorganization of the division in March, 1941.

For each officer in the criterion group the accident

19 data were classified by a sergeant in the statistical section of the division according to whether or not the officer was at fault for the accident.

This classification was based

upon the determination given in the official report which was regularly made on every accident immediately after it occurred. A safety quotient for each man of the criterion group was found separately both for ,!at fault” accidents and for the total number of accidents in which he was involved.

For

the criterion group the safety quotient was the number of months of service per accident.

This measure was used rather

than the usual one of accident rate (number of accidents per time period) because the length of time the officers had been on duty varied considerably and the use of a rate would have involved very small fractions. Officers in this group had been on motorcycle duty from 11 to 84 months during the period when accident records were kept, with a median period of service of 39 months. However, some of the older men had been on duty for an in­ definite period before that time, some had been in military service during the time period covered, and others had been on other assignments, then returned, to motorcycle duty.

Thus,

for officers serving the same number of months of motorcycle duty there was a difference in (1) the number of years of previous experience on motorcycle duty,

(2) the distribution

of time spent on this assignment, and (3) the conditions

20 under which they had motorcycle duty (postwar, war, prewar, etc.)*

It appears that these circumstances lessened the

value of the records as a criterion of accident-proneness because of the likelihood of differences of chance factors, of the differences in experience periods covered for each officer, etc.

This also prevented the application of C o b b !s

procedure for estimating the variance due to difference in accident-pronenes s• For the criterion group the age range was from twentyfour to fifty years, with a median of thirty-two years at the time the accident records were collected (March, 1948).

The

number of total accidents for each officer ranged from zero to seven and the quotient ranged from 84 months without an accident to an accident every 3.5 months. time per accident was 12.75 months.

The median riding

For the "at fault"

accidents the range was from 84 months without an accident to an accident every 6 months, with a median of 41 months per accident.

The distributions of these data are given in

Tables I and II. For the experimental group the safety quotient was computed in terms of the total number of accidents per time period.

These men commenced their first assignment involving

motorcycle operation in two groups, one starting in January and the other in April, 1948.

The records of their accidents

were collected in September, 1949, so the period of duty for

21

TABLE I DISTRIBUTION OF AGES FOB CRITERION AND EXPERIMENTAL GROUPS

Age

Frequency Criterion Group Experimental Group

22 - 23

6

24 - 25

7

20

26 - 27

7

23

28 - 29

12

15

30 - 31

23

12

32 - 33

22

8

34 - 35

22

0

36 - 37

10

1

38 - 39

13

40 - 41

5

42 - 43

1

44 - 45 46 - 47 48 - 49

1

50 - 51

1

Median

32

27

22 TABLE II DISTRIBUTION OF SAFETY QUOTIENTS FOR CRITERION GROUP (WHERE SAFETY QUOTIENT IS NUMBER OF MONTHS OF EXPERIENCE PER ACCIDENT)

__________ Frequency _________ Safety Total Number ^At-Fault1* Quotient_______ ___________ Accidents________________ Accidents No Acc idents

8

43

81 - 85

1

3

76 - 80

1

1

71 - 75

2

66 - 70

2

61 - 65

1

56 - 60

1

- 55

3

46 - 50

2

51

41 - 45

2

8

36 - 40

4

1

31 - 35

1

5

26 - 30

7

8

21 - 25

12

9

16 - 20

10

6

11 - 15

35

19

6 - 10

37

10

1 „ 5 Median

6 12.75

41

23 the second assigned group was 16 months.

To obtain comparable

accident data for both sub-groups the criterion was defined as the total number of accidents in which the officers were involved during the first 16 months of motorcycle operation. No effort was made to differentiate ”at fault” from other accidents for reasons given below.

The ages of these officers

ranged from twenty-three to thirty-seven, the median being twenty-seven years and the distribution as shown in Table I, The frequency distribution of their accidents is given in Table III, It was necessary to decide whether the total number of accidents or only the ”at fault” ones should be used in determining accident rates for use as criterion.

Actually,

the point of interest in a study such as the present was to reduce the total number of accidents for the vehicle operators in question, regardless of the cause of the accident.

This

suggests the use of total number of accidents as criterion. However, from the discussion in the previous chapter it was concluded that some accidents are due to uncontrolled and nonsystematic variables in the environment and others to characteristics of the operators.

This presented the problem

of distinguishing effectively between ”at fault” and other accidents.

In the present study where the classification was

made by a member of the organization concerned, and where the basis for the official report on which the distinction was

24

TABLE III DISTRIBUTION OP ACCIDENTS DURING THE SIXTEEN MONTH PERIOD FOR THE EXPERIMENTAL GROUP

Number of Accidents

Frequency

0

13

1

29

2

23

3

10

4

6

5

3

6

1

Number = 85 Mean = 1.765 (T = 1.335

25 made was unknown, a criterion based on such a classification seemed highly tenuous*

It also seemed likely that an

accident with a motorcycle, which is more maneuverable than a bus or truck, or even a car, is more within the control of the operator than accidents with other types of vehicles.

(The

fact that the officers in this study were involved in few accidents per time period as compared with other types of vehicle operators may bear out this possibility*)

In view of

this situation it was intended that ”at fault” accidents and fftotal number” of accidents would be used as separate criteria. However, the accidents of the experimental group could not be classified into ”at fault” and ”not at fault*”

For that

reason, only the total accidents were used in computing the criteria in this study. Another problem encountered at this point was the effect of experience upon the relation between safety quotient and test scores for the criterion group.

While the corre­

lation (biserial) between safety quotient and length of o experience (with the groups matched for age) was only *t*13, the relation between test scores and safety quotient was completely concealed until the officers were matched for experience.

The matching was done as follows:

The officers

2 To cancel the influence of age the officers were grouped according to age, then each sub-group was separated into top and bottom halves on the basis of safety quotients.

26 were first sorted into groups according to length of motor­ cycle duty.

For example, men on duty from 80 to 84 months

were in one group, those from 75 to 79 in another, etc.

The

men in each experience group were then divided into four equal groups in descending order of safety quotients*

The

top quarters from each experience group were next assembled to form the top quarter of the total criterion group, the other three quarters being assembled in the same manner*

The

division into quarters was made for use with the multiple cutting score procedure*

The other methods required only a

division into top and bottom halves*

To obtain this grouping

with the officers matched for experience, the top and second quarter of the above divisions were combined to make the top half, the lower half being formed accordingly*

While the

matching was thus done on an individual basis the over-all picture was also found before the classification was used* This picture is shown in Table IV.

It was this division of

the criterion group into top- and bottom halves that was used throughout the remainder of this study. In view of the wide range,in the ages of the officers, a biserial correlation coefficient was computed between safety quotient and this factor.

The coefficient obtained

was -K009, indicating no relationship between age and safety quotient.

The largest jt-ratio of differences between propor­

tions at various age levels was .338, which agrees in failing

27

TABLE IV CRITERION SUB-GROUPS MATCHED FOR EXPERIENCE ON BASIS OF TOTAL ACCIDENT CRITERION

Top Months of Experience Number of Accidents

Quarters Second Third

Fourth

Halves Top Bottom

Total

1229

1246

1246

1177

2475

2423

4898

34

71

114

160

105

274

379

9.93

7.36

8.84

12.92

Mean Quotient (Months per Accident) 36.15 Number = 124

17.55

23.57

28 to indicate a definite relation between age and safety quotient. IV.

TESTS USED

In the previous chapter it was concluded that the indirect examination method of prediction and the biographi­ cal inventory, especially if it were used to cover some of the factors suggested by Dunbar’s studies, were the most promising types in predicting accident-proneness.

However,

the present study was restricted as to the methods that could be used.

The Los Angeles Civil Service Commission

would not permit biographical information to be collected on its employees at the time of this study.

Furthermore, only

two hours could be utilized for testing each group of officers concerned.

In view of this it was necessary to use only part

of the group of tests originally selected for the study. Another consideration was the need to administer tests used in Wilson’s study in order to test the second hypothesis and because these tests had been found to have some predictive value among the bus and streetcar operators.

This resulted

in the use of the tests .abput to be described. With the exception of the test of mechanical knowledge from the Guilford-Zimmerman battery

the pencil and paper

3 J. P. Guilford and Wayne S. Zimmerman, "The GuilfordZimmerman Aptitude Survey," Journal of Applied Psychology, 32:24-34, February, 1948.

29 4

tests used were developed by Buch. Seven of these have 5 subsequently been published and copies of the others, in­ cluding information concerning time limits, type of items, etc., are included in Appendix A. were used:

Three performance tests

a card-sorting test developed and administered

by Wilson in the study already mentioned, a peg-board originally used by Buch and Miller, mometer.

and a small hand dyna­

Information concerning the administration and

scoring of all but the G-uiIf ord-Zimmerman test (for which published information is available) is given in Appendix A* Testing time for the complete battery was two hours when given to a group of fifty officers.

No information was

available concerning the reliability of the Buch-Wilson tests. Since both speed and accuracy of functioning were suspected but not known to be important in vehicle operation, for each test where it was possible to do so a particular type of score was found which appeared to emphasize each factor separately.

Because of this, when specific tests are

mentioned throughout the remainder of this study reference 4 Ployd L. Buch, mimeographed copies made available for this study. 5 Buch-Wilson Safe Driver Selection System, Psycho­ logical Besearch Center, 981 West Jefferson Blvd., Los Angeles 7, California, 1948. 6 D. B. Miller, "Reliability of Apparatus Tests," Informal Memorandum No. 2, Selection of L.C.V.P. Coxwains, Applied Psychology Panel, NDRC, Project N-117b.

30 is made to special types of scores on a particular test and not merely to separate sets of items, V.

METHODS OP ANALYZING THE DATA*

Selection of test batteries.

Five methods were used

in determining the usefulness of the tests.

Two utilized the

jfc-ratio of the differences between proportions of officers in the top and bottom halves on the criterion who exceeded a certain score on the test, this score being the one where the difference between proportions was greatest.

In those cases

where more than one cutting score yielded equivalent jfc-ratios, the one used was that exceeded or passed by the largest number of the 124 officers of the criterion group,

A par­

ticular test was considered useful or significant if the t-ratio was significant at the 5 per cent level of confidence. While the application of the t-ratio test to this data is questionable due to the fact that the groups to which it was applied were not random samples, it appears that this limitation influences all the tests in the same manner. Since the relative effectiveness of the tests was being evaluated by this statistic i t fs use appears to be justifiable from a practical standpoint. After the tests having significant predictive value were found by the _t-ratio procedure they were combined by two different techniques to form batteries.

First, sub-groups of

31 officers were formed according to the number of significant tests on which they exceeded or passed the cutting point* E.g., sub-group A comprised those who passed all the signifi­ cant tests, sub-group B those who failed only one, etc.

This

yielded data concerning the proportions of officers selected and the median accident rates for each of the sub-groups or selection levels.

This is referred to as the point-score

method of selecting tests.

A limitation of this method is

that it treats each type of test used as having the same selection value, which according to the size of the jt-ratios and the test inter-correlations probably is not the case. The other technique avoided both of these limitations. In this method the data cards for the officers were sorted into pass and fail sub-groups on the basis of the test yielding the highest t-ratio of difference between proportions of the top and bottom halves of the criterion.

The proportion

of officers passing this test and the median safety quotient for the passing group were then computed.

The passing sub­

group was next sorted into pass and fail sub-groups on the basis of the test having next highest _t-ratio, and the same two statistics were computed.

If the average safety quotient

was not improved by the application of the second test, of if the application of it rejected so large a proportion of officers that it became unusable, this test was discarded, the previous grouping obtained, and the test having the next

32 highest ratio was applied in the same manner*

This was

continued until the best combination of tests was obtained* This is referred to as the optimal cutting score method* The third method was the Wherry-Poolittle procedure which selects tests on the basis of maximum multiple corre­ lation between the battery selected and the criterion, after correction has been made for the chance error added by the inclusion of each test to the battery*

7

Prom the standpoint

of statistics the use of any correlational approach to the data of this study was questionable, since the obtained safety quotients were distributed neither normally nor with homoscedasticity, and since it was assumed that accident data conform to a modified Poissonian curve. been applied effectively before, however*

This procedure has Furthermore, the

practical problem presented in studies of accident-proneness requires the evaluation of all methods which offer the possi­ bility of working.

For these reasons the use of this pro­

cedure seemed to be justified in this study. The biserial correlation technique was used in apply­ ing the Wherry-Doolittle procedure.

The accident data easily

permitted division of the officers into top and bottom halves, as was done by the process discussed in section III of this chapter, but because of the ’’infinite rates’’--number of months ? W. H. Stead and C. L. Shartle, Occupational Counsel­ ing Techniques (New York: American Book Company, 1940), Appendix V.

33 divided by zero accidents— a Pearson correlation could not be computed.

Of the correlation techniques applicable to an

artificially dichotomized variable the biserial appeared to be most practical since the sample was too small to justify the use of a tetrachoric correlation and the biserial gives a finer statistic than the phi coefficient* used

The formula

was

rh 1 - Mt OX where

pjp

x £z

= mean test score of the top half Mrjt = mean test score of the total group

(T = standard deviation of the total group p

= proportion in the top half

z

= ordinate corresponding to p

Derivation of the correlation coefficients was facili­ tated by the use of tables prepared for this purpose by Waits.^ The steps outlined by Stead and Shartle"^ were followed in selecting test batteries and in obtaining beta weights. The fourth method of selecting tests for the batteries was a modified application of multiple cutting score ® J. P* Guilford, Fundamental Statistics in Psychology and Education (New York: McGraw-Hill Book Company, 1942), p. 239. 9 J. V. Waits, ’’Table of Biserial Coefficients, ” unpublished manuscript. 10 Stead and Shartle, ££♦ eft., Appendixes V and VI.

34 procedure,

11

the specific steps for the application of which

are given by Grimsley.

12

Like the Wherry-Doolittle method,

this is a procedure for selecting tests to form a battery which has maximum prediction efficiency.

Unlike that method,

however, it requires less time and lower skill in application, it does not assume rectilinearity of regression of test scores on the criterion or of test scores on each other, and it does not assume that a high score on one test will com­ pensate for a low one on another.

Moreover, it puts a

premium on consistency of test scores. The fifth selection procedure, called the successive cutting score procedure, was applied as follows!

The officers

of the criterion group were sorted into top and bottom halves on the basis of one of the tests and the median test score for the total group located. of the best half was found. each test.

Next the median safety quotient This was done separately for

The tests were then ranked according to the size

of the median safety quotient of the officers placed in the top half on the basis of the test in question--that is, 11 Floyd L. Ruch, ”A Comparative Study of the Pre­ dictive Efficiency of Batteries of Tests Selected by the Wherry-Doolittle and a Multiple-Cutting Score Method,” American Psychologist, 3:291, July, 1948 (abstract). 12 Glen Grimsley, f,A Comparative Study of the WherryDoolittle and the Multiple Cutting-Score Method of Test Selection,” (unpublished Doctor's dissertation, The University of Southern California, Los Angeles, 1947).

35 according to the effectiveness of the test in separating good from poorer operators.

The proportion of the total

group who passed the median score on the best test was determined and the median safety quotient of the passing group was computed.

To the passing group the next best test

was applied in the same manner, the proportion of officers passing the second test and their median safety quotient again being computed.

The remaining tests were applied

successively in this manner until the best combinations of tests were determined, the usefulness of the combinations being evaluated in terms of proportions of officers selected and the median safety quotients of those selected.

This

selection method has the same advantages as the multiple cutting score procedure and it requires even less time to perform the necessary calculations. Testing the hypotheses.

The first step.in testing the

hypotheses was to apply each of the five predictive batteries to the scores of the officers in the experimental group. From the battery selected by the multiple regression method this was done by applying the obtained regression equation to the appropriate scores of the officers of the experimental group.

From the point score battery the officers were ranked

into groups or ’’selection levels” according to the number of tests which they passed.

Predictions from the three cutting

36 score batteries also were made by grouping officers of the experimental group into "selection levels" each level con­ sisting of the officers who pass the tests at that level, (This procedure is described in the first section of the following chapter,) The usefulness of the tests in distinguishing between good and poor motorcycle officers and the relative effective­ ness of the battery from each of the selection methods was evaluated in terms of the differences between the mean safety quotients, the proportions of operators who were accident free, and the proportions of officers selected at the various selection levels.

This provided a basis for testing the

hypothesis concerning the relative effectiveness of the various methods of selecting test batteries, and also that concerning the predictive value of the tests.

To test the

hypothesis concerning the generality of factors in accidentproneness, Wilson’s point scores obtained in the transit line study were applied to the experimental group in the same manner as for the other predictive batteries described above. The extent to which the same tests were included in the various batteries, including the transit line battery, was also considered in evaluating the hypothesis.

CHAPTER IV ANALYSIS OF THE DATA AND PRESENTATION OF RESULTS In this chapter the applications of each of the five techniques for selecting tests and test batteries are first given separately, after which the results of all the pre­ dictions are presented together to simplify comparisons. The hypotheses of the study are then analyzed in terms of these data. I.

THE t-RATIO PROCEDURES

The t-ratio method of selecting test scores and points at which the scores cut most effectively utilizes the fact that in a given sample the significance of the differences between the proportions of two sub-groups passing a particular score is indicated by the ratio of the differences to the standard errors of the differences,^

The standard errors of

the differences between proportions were computed from the formula

Since the sub-groups of the criterion group were matched for experience the influence of variable experience was removed*

^ J. P. Guilford, Fundamental Statistics in Psychology and Education (New Yorkt McGraw-Hill Book Company, 1942), pp. 135-43.

38 The t-ratios for these differences and the percentages of the criterion group selected by each cutting score on each test are given in Table V.

Those significant at the 5 and 1

per cent levels of confidence

2

are starred.

The point score

and optimal cutting score procedures were then applied on the basis of the data in this table, test scores significant at the 5 per cent level being used. For the point score battery only one typeof score

was

used from any one test, this being the one that yielded the highest jb-ratio.

The scores used are indicated in Table V.

As indicated previously, application of the cutting score procedures involved an evaluation of the contribution of each test added to the battery in terms of the change it makes on both the proportion of officers selected and on the median criterion score.

Table VI presents the work sheet for

applying the optimal cutting score procedure.

As has been

mentioned, the sequence for evaluating tests was determined by the size of the t-ratios.

Of the sixteen combinations

tried five were considered to be useful.

These combinations,

called "selection levels," are indicated in the last column of the table and are numbered from highest to lowest.

For

example, Level I selected only 8 per cent of the criterion group, but those selected had the highest median safety quotient of any group.

This is the type

^ Ihid., Appendix, Table D.

of grouping referred

39 TABLE V t-RATIOS OF DIFFERENCES BETWEEN PROPORTIONS OF TOP AND BOTTOM HALVES OF THE CRITERION GROUP

Code

Name

T e s t## Type of Score

Score

Per Cent Selected

tRatio

Per Cent

100$ > 85

59 96

1.59 2.38*

2

Raw Score

>13 >11

48 71

2.14* 2.5*

3

No. Att.

> 13

4

No. Right

>12# >11

1

5

R-W 1

R-W 2

Per Cent No. Right

67 75

2.41* 2.34*

7.2

1.78

^ 45

9.70

1.22 2.35* 1.33

23.4 8.06

8

Raw Score

>103 > 95

55.65 69.34

1.24 1.08

9

No. Att.

>119

49.99

1.34

Per Cent

> 98

25.80

1.24

11

No. Att.

> 89 > 84

70.16 79.00

1.40 1.39

12'

No. Right

- >135

4.03

1.36

7

10

13

R-W 3

R-W 4

R-W 5

Per Cent

Spurs

O CD

o a>

6

< 100

■SH*

127

6. 45

1.43

16

Per Cent

>95

32.26

1.74

40 TABLE V (cont inue d ) t-RATIOS OP DIFFERENCES BETWEEN PROPORTIONS OF TOP AND BOTTOM HALVES OF THE CRITERION GROUP

Code 17

Name

Test^ Type of Score

Score

Raw Score

< 300^

43.55

1.72

No. Att. * No. Wrong

< 13# < 21 59

20.16

1.23

25

G-Z VII

Regular

> 25

93.55

1.46

## The types of scores used In this and subsequent tables are defined In Table XVII, Appendix A, where the test code is the same as that used in this table. The abbreviation R-W refers to Ruch-Wilson tests, S.T. to Standard Tasks, and G-Z to the Guilford-Zimmerman test.

# Test scores used in point score battery. f Significant at 1 per cent level (t « 2 •616)..^

■SHfr Not computed because, of obvious lack of significance.

41 TABLE VI WORK SHEET FOR OPTIMAL CUTTING SCORE METHOD

Test Group Group Tests Number Included None 1 2 3 4 5

6

7

8

9

10

Median Number Per Cent Safety Having 0 Selection Selected Quotient Accidents Levels 100

12.75

8

R-W 6: No. Att. £13

16.13

14

2

R-W 6t No. Att. < 21

90.32

12.75

8

R-W 1: R. Sc. >13

48.38

14

4

R-W 1: R. S c . >11

70.96

14

6

R-W 1: R. Sc. >13, R-W 6: No. A t t . < 21

42.74

15

4

R-W 1; R . Sc. 11, R-W 6: No. Att. < 21

71.77

14

6

Battery 5 plus R-W 1: No. Right >12

42.74

15

4

Battery 5 plus R-W 3: Per Cent >98

13.71

20

1

II

Battery 5 plus R-W 5: Spur < 8

37.90

17.75

4

III

Battery 5 plus R-W 5: Spur £ 8 plus R-W 3: Per Cent >98

12.09

20

1

IV

42

WORK SHEET FOR

Test Group Tests Group Included Number 11

12 13 14

15

16

t"*

o hj 1—1

TABLE VI (continued) CUTTING SCORE METHOD

Median Number Per Gent Safety Having 0 Selection Selected Quotient Accidents Levels

Battery 9 plus R-W 7 s >39

33.87

17.75

4

R-W 5: Spur 8

88.71

13.3

8

81.45

13.6

8

41.93

15.25

4

1

R-W 6: R-W 5 1

Att. *21 Spur 13 Battery 14 plus R-W 3 s Per Cent >98

8.06

23.0

Battery 14 plus R-W 6: Att. ^13

4 •83

20.7

V

I

43 to in the previous chapter and is discussed in detail here since the application of the other cutting score procedures was similar.

A summary of the selection levels, tests used

by each, and the proportion of officers of the criterion group selected at each level for each cutting score method is given in Table XIV.

Similar data for the point score

method are also included in that table. II.

THE MULTIPLE REGRESSION PROCEDURE

The biserial correlations between test scores and criterion scores for the criterion group, calculated as indicated in the previous chapter, are given in Table VII. Only the tests yielding correlations significant at the 5 per cent level of confidence were used in selecting the test batteries.

The table of intercorrelations and the work

sheets showing the application of the Wherry-Doolittle selection procedure are given in Appendix B*

The multiple

correlation between the safety quotients for the criterion group and the three tests selected was found to be +.39.

The

tests selected and the proportion of variance of the criterion accounted for by each test are given in Table XVI. The mean and standard deviation of the criterion data enter into the formula for calculating b coefficients. However, in this study the fact that several officers with varying periods of experience had zero accidents made this a

44 TABLE VII CORRELATIONS BETWEEN TESTS AND THE CRITERION N * 124

Type of Score

Top Half

R-W 1

No. Right Raw Score % Right

14.693 14.56 97.258

14.226 13.806 96.024

3.641 3.94 6.15

R-W 2

No. Right % Right

31.097 87.74

31.459 87.42

9.129 12.84

-.049 .0249

R-W 3

Raw Score % Right No. Att.

110.084 ’96.13 87.08

108.276 95.51 87.404

19.76 4.67 18.68

+.1147 +.1663 -.021

R-W 4

No. Right % Right Raw Score

97.20 96.568 93.45

96.97 96.802 93.35

18.70 2.58 17.3

+.0154 -.065 #

R-W 5

Raw Score % Right No. Att* Spurs

79.916 95.42 91.31 3.677

81.756 95.88 90.41 3.919

14.64 7.54 20.56 2.598

-.1575 -.0765 +• 0548 -.1168

R-W 6

Raw Score 13.302 No. Att.+Wrong 16.597

13.536 17.766

3.08 6.492

-.095 -.2256/

Test

Mean Total Group

r

*p bi +.1606 +.2397* +.2582*

R-W 7 No. Completed St.Task5 Raw Score St.TasklSNew Standard

46.82 16.564 4.242

46.74 16.903 4.274

7.04 4.617 3.168

+• 014 — .092 + .012

Peg Bd.

Out

21.613

21.754

2.151

-.082

Cards

Av. of 3 Tries 29.808

29.363

4.344

+.128

Hand Dy- Strongest Hand 65.671 namometer 38.79 G-Z VI

65.283

7.83

+.0621

38.34

8.72

+.0647

Tests significant at 1 per cent level (r —

7.228).

# Tests significant at 5 per cent level (r ~ ^.174).

/ Not computed. significant .

Determined by observation to be non­

T

i'

45 truncated distribution and so it was impossible to calculate 'K

these statistics.

From an examination of the formulas

con­

cerned it appears that the actual size of the mean and standard deviation has no bearing upon the relative sizes of the predicted scores, since they effect each relatively the same.

It thus appears that arbitrary values could be used.

In this study, however, these statistics were estimated as follows.

In a normal distribution the numerical value of the

median approximates that of the mean and the sigma is the distance above and below the mean within which range 68.26 per cent of the cases fall.4

The median value in this calcu­

lation was used as the mean.

It was found that 68.26 per

cent of the cases was 84.64 cases.

Since half of these would

lie on each side of the mean there were 42.32 on either side of 12.75 (the median).

The point on the distribution marking

one sigma above the mean was 28.

The interval was thus 21.6

and the sigma 10.8. III.

THE MODIFIED MULTIPLE CUTTING SCORE PROCEDURE

In applying the multiple cutting score procedure medians were used instead of means, for reasons indicated above.

As Table VIII shows, there were very small differences

between the sets of medians where the sub-groups were either 5 Ibid., pp. 259-60. 4 Ibid., p. 53.

46 TABLE VIII WORK SHEET FOR MULTIPLE CUTTING SCORE PROCEDURE: MEDIAN SCORE OF EACH CRITERION GROUP

Test ^ Code'* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

A

B

100 14 15 14 92 31 98 113 119 98 96 91 3 80 91 98 13.2 15 47 18 3 29.0 21 9 39

100 14 14 15 90 31 97 105 119 97 98 95 3 83 88 99 12.2 15 47 15 5 31 22 9 39

Criterion Groups^ D C 100 12 13 12 91 31 97 109 117 98 95 93 4 87 88 98 13.6 16 47 18 3 28 22 10 39

* For Key to Code see Table V # Key - A B C D E F

-

Top quarter Second quarter Third quarter Fourth quarter Top half Bottom half

97 13 14 14 90 31 97 103 121 97 97 93 3 82 85 98 14 16 48 18 3 28 22 9 39

E

F

100 14 15 15 91 31 97 112 119 97 97 95 3 82.5 88 99 13 15 47 17 4 29 21 9 39

100 13 15 13 90 31 97 104 120.5 97 95.5 93 3 83.5 87.5 98 14 16 47.5 18 3.5 28 22 9 39

47 quarters or halves of the total group.

However, the second

step (Table IX) of the procedure was applied to those scores where there were some differences between medians.

Because

of the small differences and the frequent reversals from one standard to another for each test, the data were not plotted as in the usual application of the multiple cutting score procedure but the order of usefulness of types of scores was determined by inspection of the table. were 22, 5, 17, and 20 in that order.

The ones indicated These test scores were

applied successively, in that order, to the data of the criterion group with results as given in Table X.

Prom this

table it is clear that only test score 5 contributed to the value of score 22.

The best combinations of cutting points

on these two scores were determined by observation of Table XI.

The order of usefulness of the scores, in terms of median

safety quotients and per cent selected, is indicated by Homan numerals in this table and Table X. IV.

THE SUCCESSIVE CUTTING SCORE PROCEDURE

The application of this procedure to the criterion group is summarized in Tables XII and XIII.

The tests used

at various selection levels are summarized in Table XIV.

TABLE IX WORK SHEET FOR MULTIPLE CUTTING SCORE PROCEDURE: MEDIAN CRITERION SCORE AND PER CENT SELECTED* BY SINGLE TEST SCORE STANDARDS

Test Scorg (Code")

Md

Median Criterion Score and Per Cent Selected by Each Standard D E F C B A Md ' Md Md Md Md % % % % % %

2

14

50

14

50

14

71

14

63

14

50

14

63

4

14

56

14

46

14

75

14

56

14

46

14

66

5

13.6

44

12.5

57

12.6

50

12.5

57

12.6

50 ,

12.5

57

8

13.2

46

14

54

13.3

49.2

13

56

13.7

46

13.7

55

15

14

44

14

51

14

51

14

60

14

51

14

51

17

14

48

14

34

14

50

13.3

58

14

46

13.3

58

20

12.5

49

11.6

70

12.5

49

12.5

29

11.6

59

12.5

49

21

12

50

13.3

72

12

50

12

50

12

59

12

50

22

14

37

16.3

28

13.3

44

13.3

46

14

36

13.3

46

# Only those test scores on which there are differences between Median Scores are given here. § For Key refer to Table V.

TABLE X WORK SHEET FOR MULTIPLE CUTTING SCORE PROCEDURE: MEDIAN CRITERION SCORES AND PER CENT SELECTED BY MULTIPLE SCORE STANDARDS

Test Battery Code

Standard A Md $

Median Criterion Score and Per Cent Selected Standard B Standard C Standard D Standard E Md % Md % Md % Md %

(IV)

(III)

Standard F Md %

22

14

37

16.3

28

13.3

44

13.3

46

14

37

22+5

19.9

16

14

23

15.1

21

14

23

15.1

21

22+5*17

15

5

11.8

4

14

6

16.2

8

13

5

16.2

8

22+5+20

16.3

9

14

4

16.3

9

16.3

9

16.3

6

16.3

9

13.3 '14

46 23

to

TABLE XI WORK SHEET FOR MULTIPLE CUTTING SCORE PROCEDURE: COMBINATION OF BEST MULTIPLE CUTTINGS

■n wr c * Per Cent (Codes)

"

Card Sort (Test Code 22) ~ -- 1 1 1 —-yr ' 1 Standard B Standard C Standard D"'r Standard W Standard F* "lid ~Md ltd Id jT I d ' ~^

Standard A ~Md jT

'

(I)

(II)

Standard A

21.6

13

21.3

9

B

16.3

19

18.5

C

18.5

17

D

16.3

E F

19.9

16

14

14

23

20.0

13

15.1

21

19

18.5

14

14

23

18.5

17

20

13

15.1

21

16.3

19

18.5

14

14

23

All entries same as for Standard C. # All entries same as for Standard A.

Oi

o

51

TABLE XII WORK SHEET FOR SUCCESSIVE CUTTING SCORE PROCEDURE: MEDIAN SCORES FOR TOP HALF OF CRITERION GROUP ON EACH TEST

Median Test Score

Md Safety Quotient of Top Half

1. R-W #1: Raw Score

13.8

14

2. R-W #1: No. Right

14

14

100

14

4* Card Sorting

28

14

5* R-W #5: Per Cent

99

13.8

6* R-W #6: Raw Score

13.4***

13.6

7* R-W #6: No. Attempted

15 “"

13.3

8. R-W #5: Per Cent

97

13

9. R-W #4: No. Right

93

13

Type of Test Score'"*

5. R-W #1: Per Cent

10. R-W #6: No. Attempted+W 16 "

13

* This tahle includes only test scores where the Median Safety Quotient of the Top Half exceeds the median of the total group* ':HJThe top half had scores at or below this score*

52

TABLE XIII WORK SHEET FOR SUCCESSIVE CUTTING SCORE PROCEDURE; MEDIAN SAFETY QUOTIENTS AND PERCENTAGES SELECTED BY COMBINATIONS OF TEST SCORES*

Type of Test Score*"*

Median Safety Quotient of Cases Selected

Per Cent Selected

14

1 + 3

H

CO

32

1 + 4

18.5

27

1 + 5

14

25

1 + 4 + 5

18.5

16

1 + 4

21

+ 6

50 •

1

9

1 + 4 + 7

18.5

11

1 + 4 + 8

17.5

14

1 + 4 + 9

14.5

12

Selection Levels III

II

I

Only combinations yielding Median Safety Quotients which exceeded the Median of those selected by the best single type of score are included. The numbers refer to the identifying numbers from the first column in Table X.

53 TABLE XIV SUMMARY OF METHODS OF SELECTION AS APPLIED TO THE CRITERION GROUP

Point Score Method

A*

Selection Level

B*

Officers Selected Median Per Cent Safety Quotient

Tests Passed

A

5 (all)

B

4

24

14.75

C

3

71

14

D

2

96

13.3

E

1

99 +

12.75

2

15.5

Optimal Cutting Score Method

Selection Level

Tests Used

Cutting Score

Officers Selected Median Per Cent Safety Quotient

I

R-W 1: Raw Score >13 R-W 5: Spur 98

8

23

II

R-W 1: Raw Score >13 R-W 3: Per Cent >98 R-W 6: No. Att* 13 R-W 5: Spur < 8 R-W 6: No. Att* 13 R-W 6: No. Att. < 21

43 '

15

R-W 5: Spur

89

13.3

V

8

54 TABLE XIV (continued) SUMMARY OP METHODS OF SELECTION AS APPLIED TO THE CRITERION GROUP C,

Modified Multiple Cutting Score Method Officers Selected Cutting Median Selection Per Cent Safety Quotient Tests Used Score Level I

R-W 2: Per Cent Card Sort

>91 > 28

13

21.6

II

R-W 2: Per Cent Card Sort

>91 >27

16

20

III

Card Sort

>30

28

16.3

IV

Card Sort

> 28

37

14

D.

Successive Cutting Score Method

Selection Level I

Tests Used

Cutting Score

Officers Selected Median Per Cent Safety Quotient

R-W 1: Raw Score >13 R-W 6: Raw Score 14 Card Sort 7 27

9

21

II

R-W Is Raw Score Card Sort

>13 > 27

27

18.5

III

R-W Is Raw Score

>13

50

14

55 V.

RESULTS OF THE APPLICATION OF THESE PROCEDURES The next step was to predict the relative standing of

the officers of the experimental group from their scores on the tests included in each of the selection batteries.

The

basic information concerning these predictions is given in Table XV.

In predicting from the point score method Class A

included officers who passed all the tests of the battery, Class B those who missed no more than one, and Class C those who missed no more than two tests.

No distinction was made

between groups failing more than two tests, since only 8 per cent of the officers did so.

In applying the multiple

regression method the officers were ranked according to t h e . predicted safety quotient, then were grouped in such manner as to give a breakdown as comparable to the groupings from the other methods, in terms of number of groups and proportions selected, as the data permitted. The results of applying to the experimental group the tests and cutting scores which were found in Wil s o n ’s transit line study by a point score method similar to that used here are also included in Table XV.

This table provides the basis

for comparing the results of the various selection methods in terms of the percentages of officers who were accident free at each selection level.

The significance of the differences

56 TABLE XV EFFECTIVENESS OF THE PREDICTIONS FROM TEST BATTERIES SELECTED BY EACH METHOD

A.

By Point Score Method

Class

Frequency No. %

Accidents No. Mean

No.

A B C Total

18 64 78 85

31 106 131 150

5 11 12 13

B.

21 75 92 100

1.72 1.66 1.68 1.76

Accident Free t^ratio between % class & total gr 28 17 15 15

1.375 w •\

By Optimal Cutting Score Method

Selection Level

Frequency No. %

I II III IV V Total

12 10 36 36 80 85

Accidents No. Mean

16 14 12 12 51 42 51 42 94 s 137 100 150

1.33 1.20 1.42 1.42 1.71 1.76

No. 4 3 6 6 13 13

Accident Free jb-ratio between % class & total gr 33 30 17 17 16 15

1.333

By Modified Multipl e Cutting Score Method Accident Free Selection Frequency Accidents t-ratio between Level No. % No. Mean No. % class & total gr C.

I II III IV V Total

10 12 24 31 41 85

12 14 28 36 48 100

15 18 36 56 75 150

1.5 1.5 1.5 1.81 1.83 1.76

1 2 4 4 5 13

10 17 17 13 12 15

57 TABLE XV (continued) EFFECTIVENESS OF THE PREDICTIONS FROM TEST BATTERIES SELECTED BY EACH METHOD D.

By Successive Cutting Score Procedure

Selection Level

Frequency Mo. %

Accidents No* Mean

No.

I II III Total

6 26 54 85

8 44 87 150

2 3 9 13

E.

1.33 1.69 1.61 1.76

33 12 17 15

.927

M ethod By Multiple Regression '

Predicted Rate Greater than 16 Greater than 15 Greater than 13 Greater than 8 Total F.

7 31 63 100

Accident Free t-ratio between % class & total gr

Frequency No. %

Accidents No. Mean

No.

Accident Free t_-ratio between % class & total gr

10

12

9

.90

3

30

18

21

30

1.66

3

17

38

45

59

1.53

5

13

71 85

84 100

121 150

1.70 1.76

11 13

15 15

1.10

By Transit Line Point Scores

Class

Frequency No.

AA A B Total

8 17 40 85

9 20 47 100

Accidents No. Mean

No.

11 23 59 150

3 4 9 13

1.36 1.35 1.47 1.76

Accident Free jt-ratio between % class & total gr 37.5 23 22.5 15

1.3213 .87 1.293 1.56

Where no entry is given, the statistic was obviously not significant so was not computed* # These levels are non-cumulative.

58 between these percentages

5

is also shown.

The mean number

of accidents at each selection level is given in the table to indicate trends, but the significance of differences between means was not computed since the differences between percentages of accident free officers seemed to be as appropriate a criterion as the differences between mean safety quotients, and the computation of the significance of differences of percentages is much shorter#

In Table XVI

the types of tests selected by the various procedures are shown to provide additional basis for evaluating the relative effectiveness of the various tests# VI.

FACTORS AFFECTING THE INTERPRETATION OF RESULTS Limitations of the criteria.

In both the criterion

and the experimental groups the criterion of accidentproneness included all the accidents in which each officer was involved#

For reasons considered in Chapter II, Section

II, it appears that this complicates the activity of the various liability groups which together with non-systematic or chance factors are assumed to determine the variance in the criterion data.

This may account for the fact that by

C o b b fs procedure it was found that only 1,7 per cent of the ^ » PP* 142 ff. The correlations between successive sets of data for use in this formula were computed from the formula given by J. P. Guilford in Psychometric Methods (New York: McGraw-Hill Book Company, 1936), p# 365.

TABLE XVI TEST SCORES SELECTED BY THE VARIOUS SELECTION PROCEDURES, INCLUDING SPECIFIC CUTTING SCORES

Test

Type of Score

R-W 1

Per Cent No. Right

Point Score

Method of Selection Cutting Score Multiple Transit line Optimal Successive Multiple Regression point score .033*"' .06*

>12

Raw Score

>89/

>13

>13 >91

R-W 2

Per Cent

R-W 3

Per Cent

R-W 4

No. Right

>68/

R-W 5

Per Cent

>90

Spurs R-W 6

No. Attempted

>98

>98

*8

*8

*13

*21

> 98

* 4

.06*

No. Att. + Wrong Raw Score R-W 7

No. Completed

>87

*14 >39

*13 >39/

*3l / Cards >27 X# # Product of Beta weights and rxc* # There were several different cutting points on this test; / These test scores were used only in forming class AA.

60

variance of the experimental criterion could be attributed to differences in accident-proneness and thus be predicted. Because of the small number of accidents (the mean was 1.76, and 34 per cent of the officers had no accidents) it was obvious that the obtained measure of the reliability of the experimental criterion could not be very high, whether estimated by correlating the number of accidents that occurred on odd calendar days with those that occurred on even days, or the number that occurred during the first and last halves of the experience periods.

These estimates of reliability

were found to be -.03 between odd and even days, and +.15 between first and last experience periods.

This indicates

that the criterion for the experimental group provided an inadequate basis for evaluating the hypotheses. Applicability of test selection procedures.

It was

pointed out in the previous chapter that the data in this study were assumed to be distributed according to a Poissonian curve, and that if this assumption were valid it violated the conditions necessary for the application of a biserial corre­ lation.

Furthermore, the multiple cutting score procedure

had to be modified to be applied here, and was also used on a much smaller sample than that for which it has been found to be most useful.

Since the sub-groups to which it was

applied were not selected randomly, the use of the t-ratio as

61

a test of significance was questionable.

This should be

remembered in evaluating the point score and the optimal cutting score procedures.

In selecting tests by the succes­

sive cutting score method the matching for experience could not be utilized as it had been in applying all other methods. Thus the influence of experience on safety quotient was not controlled in the application of this method and presumably this lessened the effectiveness of tests selected by this method.

Thus it appears that none of the five selection

methods met all of the conditions required for a rigid com­ parison of the results. Restriction of range in the test scores.

It was

obvious from the standards the officers in the experimental group were required to meet before being assigned to motor­ cycle duty that they were very highly selected.

That is, the

tests in this study were used to predict a trait on which the officers were already highly homogeneous, and presumably this restricted the range of their scores on the tests.

Insuffi­

cient information was available to correct for this restriction by statistical methods.

However, for a few of the tests the

mean and variability of a group of newly-hired transit line operators were available.

The mean scores of the motorcycle

officers on these tests were about one standard deviation above that of the transit line operators and their standard

62

deviations correspondingly smaller*

This meant that the

predictive efficiency of the tests as determined in this study was underestimated to an undetermined extent, a con­ sideration very important in the interpretation of the results• VII.

EVALUATION OP THE HYPOTHESES

General considerations.

None of the t-ratios of

differences between proportions of accident-free officers in the total group and those selected at each level, as shown in Table XV, were significant.

The largest ratio found

(1.56) was from the transit line battery.

This battery also

yielded the most consistent differences between both percen­ tages of accident-free officers and between mean numbers of accidents at the different selection levels.

There were no

reversals in the percentages of accident-free, and only a very slight reversal (a mean of 1.36 as compared with one of 1.35) between mean safety quotients for the successive selection levels.

Because of these consistent differences,

and to provide a better basis for comparing the results with those obtained from other studies, a biserial coefficient of correlation was computed between the'number of tests passed in the transit line battery and the obtained safety quotient. This was done by dividing the officers into top and bottom groups, on the basis of tests passed, at the point where the

63 groups formed were nearest to being halves*

The top group

consisted of those passing at the B level (see Table XV) and it included 47 per cent of the officers*

The obtained

coefficient was +.25, which is significant at the 5 per cent level of confidence* The point score procedure showed the next best differ­ ences in terms of percentages of accident-free officers, yielding a t^-ratio of 1*375 at the highest selection level which included 21 per cent of the officers*

However, this

method yielded a very small decrease in the mean safety quotient for the top selection groups, thus weakening the predictive value of this battery.

The optimal cutting score

method showed the next highest ratio between percentages of accident-free officers (1.333) and at the same time yielded large differences between mean safety quotients.

The

multiple regression method showed highest selection efficiency at the very top level, where it selected 12 per cent of the total group.

At this point the mean safety quotient was

reduced nearly to half that of the whole group*

However, the

per cent of accident-free at this level was less than at similar prediction levels for three of the other methods, and the differences between mean safety quotients were not con­ sistent*

The results from the successive cutting score pro­

cedure were somewhat similar although the efficiency at the top level was not as great.

64 For the transit line battery to be more predictive of the safety quotients of the experimental group than the batteries selected in the present study was not expected. Perhaps the explanation lies in the inadequacy of the measure of accident-proneness for the criterion group which provided the basis for the selection of the batteries in this study. This explanation is supported by the fact that the transit line battery was applied to the data of the criterion group and was found to be much less discriminatory than when applied to the experimental group. With respect to the comparison of the results obtained from this study with that of other similar studies it must be remembered that these results are from the application of the cutting scores to a vehicle operator group other than that on which the cutting scores or weights were determined, while most studies show only the results applied back to the original sample.

For example, in their recent study of sixty-

seven taxicab drivers Ghiselli and Brown

report a validity

coefficient of +,69 for their battery of five pencil and paper tests when it was applied to the standardization group. However, they say "Undoubtedly this coefficient is fortui­ t o u s l y high and would not be obtained with another similar sample.

For the motormen the validity of this battery was of

6 E. E. Ghiselli and C. W. Brown, "The Prediction of Accidents of Taxicab Drivers," Journal of Applied Psychology. 33:540-46, December, 1949,

7 the order of .35.11

The comparable coefficient in the

present study (the multiple regression coefficient between scores from the tests showing highest zero order correlations and the criterion) was +.39.

This correlation compares

favorably with that found by Cobb using a battery of nine tests, most of which were biographical.

(See Chapter II.)

Furthermore, the biserial coefficient between the predicted and the obtained quotients for the experimental group in this 8 study using the multiple regression method was +.18. While 9 this is significant only at the 10 per cent level of con­ fidence, the per cent of the total variance accounted for (about 3 per cent) is high in view of the amount estimated by the Cobb procedure to be predictable. Evaluation of the first hypothesis.

The results of

this study did not provide an affirmative answer to the hypothesis concerning the effectiveness of paper and pencil tests, since none of the obtained differences between the successive selection levels were significant at the level 7 Ibid., p. 544. 8 This coefficient was found by dividing the officers as nearly as possible into halves on the basis of the pre­ dicted rates. The cutting point giving the nearest to halfand-half split was a score o f ^ 13 which placed 44.7 per cent in the top group. 9 This was computed by a formula given by E. F. Lindquist, Statistical Analysis in Educational Research (Boston: Houghton Mifflin Company, 1940), p. 211.

66 ordinarily considered to be an effective level of confidence (5 per cent).

However, the results were in the direction

required by the hypothesis. Evaluation of the second hypothesis.

The present

study provided a positive answer to the hypothesis concerning the applicability to motorcycle officers of tests and cutting points established on transit line operators, as indicated above.

While none of the obtained differences were signifi­

cant statistically, the consistency of the results, as well as the magnitude of the differences in proportions of accidentfree operators of the various sub-groups, seemed surprising in view of the proportion of the variance of the criterion estimated to be predictable and in view of the differences between the types of vehicles in question--trolleys and large busses on the one hand and motorcycles on the other. more, the obtained r ^

Further­

of +.25, significant at the 5 per cent

level of confidence, indicates a definite relationship between test battery and safety quotients. Evaluation of the third hypothesis.

For reasons

already discussed, the present study provided little basis for evaluating the relative effectiveness of the cutting score as compared with the regression methods of test selection.

To the extent that the various methods were

applicable in this study, however, the following conclusions

67 were drawn.

In terms of percentage of accident-free officers

the regression method was less efficient than the optimal cutting score method but superior to the other two cutting score procedures.

It also was conspicuously superior at the

upper level of selection to any of the other methods.

Where

a larger proportion of officers was to be selected, however, the optimal cutting score method was superior, both in terms of mean safety quotient and of per cent of accident-free officers.

This conclusion does not agree with the findings

of previous studies concerning the effectiveness of the two types of methods at the upper level of selection.^ VIII.

RELATIVE EFFECTIVENESS OF SPECIFIC TESTS

The first and sixth of the Ruch-Wilson tests appeared to be the most effective measures used in this study.

Both

were selected by all the methods except the modified multiple cutting score procedure which was the least effective selection method.

Of the two tests, R-W #1 had the highest

zero-order correlation with the criterion, contributed more to the total variance of the criterion,

since two types of

scores from it made independent contribution, and yielded consistently higher ratios of differences between proportions

10 Floyd L. Ruch, "A Comparative Study of the Pre­ dictive Efficiency of Batteries of Tests Selected by the Wherry-Doolittle and a Multiple-Cutting Score Method,” American Psychologist, 3:291, July, 1948, (abstract).

68 as shown in Table V.

Thus it appears to be the best test,

although R-W #6 was the only one of the two which yielded a jb-ratio of differences which was significant at the 1 per cent level of confidence. Of the Ruch-Wilson published battery #4 showed the least predictive value, appearing in none of the selected batteries and consistently yielding the lowest correlations and t-ratios of any of the tests used.

R-W #2 and #7 also

showed little indication of predictive value. R-W #1, called "Visual Attention," appears to measure the ability to "keep your eyes on one object as you sweep through a field of many similar objects," or perhaps the ability to anticipate visually a change of direction.

From

Tables V and VII it appears that accuracy is more important than speed on this test, as indicated by a slightly higher relationship between the criterion and the per cent right, and the criterion and the number right, than between the criterion and the number attempted.

This suggests, then,

that accuracy of visual attention is a desirable character­ istic of motorcycle operators. In all cases Test #6 was found to have a negative relation to the criterion.

This test requires the examinee

to put pencil dots in the center of small circles, as rapidly as he can, for five minutes.

In administering the test some

officers were observed to work a short time and then display

the attitude of flW h a t fs the use?”, while others worked methodically for the full five minutes.

Perhaps this test

measures something akin to compulsiveness or rigidity which interferes with the operation of a vehicle. The activity involved in Test #6 is very similar to the dotting test of Ghiselli and Brown for which they found a high positive relation with the criterion.

It appears

likely that the difference between the results obtained on the two tests is due to the time factor, Ghiselli and Brown using a limit of one half minute.

This would imply that

further study of this test should be made using various time limits.

It appears that different factors are measured when

different time limits are used, and optimum limits for each factor should be determined.

CHAPTER ¥ SUMMARY AND' CONCLUSIONS I.

SUMMARY

This study was des-igned to provide an evaluation of three hypotheses concerning the prediction of accidentproneness*

These were that pencil and paper tests could be

used as effective predictive measures, that tests found to be effective in predicting accident-proneness of bus and streetcar operators also predict proneness among motorcycle operators, and that tests selected by cutting score pro­ cedures are as effective as those selected by multiple regression methods*

The samples of motorcycle operators

were two separate groups of officers of the Traffic Enforce' ment Division of the Los Angeles Police Department*

The

group on which the test scores were analyzed and from which the prediction or regression weights were derived had been on duty for periods varying from one to seven years during the time in which accident records were available and in a few cases for an unspecified period prior to that.

This

meant that the periods during which they had been operating motorcycles were not comparable from the standpoint of uni­ formity of conditions of operation.

These situations

weakened the confidence which could be put in the accident

71 records as a criterion of accident-proneness and also compli­ cated the application of statistical techniques to the criterion data. The predictive measures used were the Ruch-Wilson Safe Driver Selection System, Form A, two other short tests developed by Ruch, a card-sorting test, a peg-board test, a hand dynamometer, and one of the Guilford-Zimmerman tests. In the second chapter the literature was reviewed especially from the standpoint of the methodology of research on accident-proneness.

From this review conclusions were

drawn concerning the distribution of accident data.

Accident-

proneness was defined in terms of the likelihood of a driver's having accidents because of factors or characteristics of the driver himself.

It was concluded that a procedure suggested

by Cobb could be used to evaluate the variability in accident data which can be attributed to differences in the accidentproneness of the drivers in question.

Evidence for the

hypothesis that persons having high proneness for one kind of accident also have high proneness for other types was evaluated, and it was concluded that information available was inconclusive.

The importance in the design of research

of considering the extent to which a high score on one measure will compensate for a low one on another was emphasized. Evidence was found for the value of using pencil and paper tests as possible predictive measures, and for evaluating the

effectiveness of measures by applying them to a criterion sample other than to that on which the prediction technique was worked out. Consideration was given to distinguishing between ♦

accidents in which the officer concerned was judged to be Mat fault*1 and those in which he was not so judged.

Con­

siderable work was done for this purpose but the plan had to be given up because the distinction could not be made in the accidents of the experimental sample.

Neither was it possible

to classify the accidents according to types.

As a result,

the criterion used for each officer was the actual number of accidents in which he was involved.

The extent to which this

weakened the usefulness of the criteria is indicated at least in part by the fact that only 1,7 per cent of the variance in the experimental criterion could be attributed to differences in accident-proneness of the officers concerned. Tests were evaluated and formed into batteries which were predictive of accident-proneness by means of five different test selection procedures--three cutting score pro­ cedures, a point score method, and a multiple regression method.

The safety quotients of the officers of the experi-

/

mentyi group were predicted from their scores on the tests included in each selection battery.

The effectiveness of the

prediction in each case was evaluated in terms of the differ­ ences between the percentages of officers who had no accidents

73 and the differences between the mean safety quotients for each of the sub-groups or classes formed according to the predictions.

The critical scores on pertinent tests as

determined in a study of accident-proneness of bus and streetcar operators were applied to the experimental group in this study in order to determine whether factors important in one type of vehicle operation are also important in another* II.

CONCLUSIONS

The present study provided an opportunity to evaluate the hypotheses by applying the tests to an operator sample other than that on which the tests were initially selected and the cutting points determined.

However, the study was

limited in general by the fact that (1) the obtained safety quotients proved to be unreliable criteria of accidentproneness,

(2) the data did not meet all the conditions

required by the test selection procedures, and (3) the operators whose accident-proneness was predicted were already highly sele.cted in terms of accident-proneness and their test scores were restricted in range, yet no correction could be made for this condition. The following conclusions were drawn from the study; 1.

In general agreement between predicted and obtained

safety quotients was not statistically significant, but the

74 differences between percentages of accident free officers and the mean safety quotients from successive selection levels were in the predicted direction*

There is reason to believe

that if the tests were applied to a typical group of appli­ cants for motorcycle duty, prior to their being selected by other means, or if statistical correction could be made

for

the above-mentioned restriction of range on the test scores, the predictions made from the test batteries selected in this study would be statistically significant, 2.

The different selection methods showed high agree­

ment as to the effectiveness of specific tests, including those used in the transit line 3.

battery,

The prediction from the transit line battery

was

statistically significant, yielding a correlation of +.25. This strongly suggests that accident-proneness for various types of vehicle operators can be predicted from the same battery of tests, and thus that accident-free operators of busses, trolleys, and motorcycles have characteristics in common. 4.

Of the specific tests having best predictive

value, one appeared to measure "visual attention" or the ability to anticipate visually a change of direction, the other to measure compulsiveness, or th^e willingness to persist at a disagreeable task without consideration of the usefulness or appropriateness of the task.

75 5.

While the results were not statistically signifi­

cant, at the upper level of selection, the multiple regression procedure selected the most effective test battery.

When a

larger proportion of employees was to be selected the optimal cutting score procedure was superior to all other methods.

BIBLIOGRAPHY

77 BIBLIOGRAPHY Brown, G. W . , and E. E. Ghiselli, ’’Accident Proneness Among Street Car Motormen and Motor Coach Operators,” Journal of Applied Psychology, 32:20-23, February, 1948, Cobb, Percy W., ’’The Limit of Usefulness of Accident Rate as a Measure of Accident-Proneness,” Journal of Applied Psychology, 24:154-59, 1940, Collins, James H., ”Accidents--A Broader Approach,” Public Utilities Fortnightly, 40:693-700, November 20, 1947* Dunbar, Flanders, Psychosomatic Diagnosis. Harper and Bros., 1943*

New York:

_______ , ’’Medical Aspects of Accidents and Mistakes in the Industrial Army and in the Armed Forces,” Yifar Medicine, 4:161-75, 1943. Ghiselli, E. E., and C. W. Brown, ’’The Prediction of Accidents of Taxicab Drivers,” Journal of Applied Psychology, 33:540-46, December, 1949. Grimsley, Glen, ”A Comparative Study of the Wherry-Doolittle and the Multiple Cutting-Score Method of Test Selection.” Unpublished Doctor's dissertation, The University of Southern California, Los Angeles, January, 1947. Guilford, J. P., Psychometric Methods. Book Company, 1936.

New York: McGraw-Hill

_______ , Fundamental Statistics in Psychology and Education. New York: McGraw-Hill Book Company, 1942. Guilford, J. P., and Wayne L. Zimmerman, ’’The GuilfordZimmemian Aptitude Survey,” Journal of Applied Psychology, 32:24-34, February, 1948. Lindquist, E. F., Statistical Analysis in Educational Research. Boston: Houghton Mifflin Company, 1940. Mintz, A., and M. L. Blum, ”A Re-examination of the Accident Proneness Concept,” Journal of Applied Psychology, 33:195-211, June, 1949. Miller, D. R., ’’Reliability of Apparatus Tests,” Informal Memorandum No. 2, Selection of L.C.V.P. Coxwains, Applied Psychology Panel, NDRC, Project N-117b.

78 Ruch, Floyd L., ,fA Comparative Study Efficiency of Batteries of Tests Doolittle and a Multiple-Cutting Psychologist, 3:291, July, 1948,

of the Predictive Selected by the WherryScore Method,11 American (abstract), **

Ruch-Wilson Safe Driver Selection System, Form A, by Psychological Research Center, 981 West Jefferson Blvd., Los Angeles 7, California* 2

+. 2653

c2

-.26547

a3

1

-C

Check Sum

-.0254

-.2582

+1.2134

+.0254

+.2582

-1.2134

+.2256

1.4529

+.21902

1.48367

3

Test Number

al

b3 c3

.68255 -1

-.0254

1 .999355

•

+. 4