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2004 31: 649Criminal Justice and BehaviorJorge G. Varela, Marcus T. Boccaccini, Forrest Scogin, Jamie Stump and Alicia Caputo

Personality Testing in Law Enforcement Employment Settings: A Metaanalytic Review

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10.1177/0093854804268746CRIMINAL JUSTICE AND BEHAVIOR

Varela et al. / PERSONALITY TESTING IN LAW ENFORCEMENT

PERSONALITY TESTING IN

LAW ENFORCEMENT

EMPLOYMENT SETTINGS

A Meta-Analytic Review

JORGE G. VARELA

Wilford Hall USAF Medical Center

MARCUS T. BOCCACCINI

FORREST SCOGIN

JAMIE STUMP

ALICIA CAPUTO

The University of Alabama

Meta-analysis was usedto (a) assess theoverall validity of personality measures as predictors of

law enforcement officer job performance, (b) investigate the moderating effects of study design

characteristics on this relation, and (c) compare effects for commonly used instruments in this

setting. Results revealed a modest but statistically significant relation between personality test

scoresand officerperformance.Prediction wasstrongestfor theCalifornia PsychologicalInven-

tory and weaker for the Minnesota Multiphasic Personality Inventory and Inwald Personality

Inventory. Effect sizeswere larger for studies examiningcurrent jobperformance, as opposed to

future job performance. Implications for using personality tests in the law enforcement officer

hiring process are discussed, and recommendations for future research are provided.

Keywords: meta-analysis; police; law enforcement; personality assessment

It is standard practice in most major law enforcement agencies toemploy the services of mental health professionals to screen jobcandidates. Thegeneral approach followed in these evaluations is one

of screening out unfit candidates, rather than selecting in preferred

candidates. The typical psychological evaluation involves screening

for both major mental illness and personality traits that may interfere

649

CRIMINAL JUSTICE AND BEHAVIOR, Vol. 31 No. 6, December 2004 649-675

DOI: 10.1177/0093854804268746

2004 American Association for Correctional Psychology

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with law enforcement officer job performance. Such traits may

include an inability to deal with stressful situations, being prone to

violent outbursts, and potential for substance abuse. Screening out

unfit job candidates is especially important in law enforcement set-

tings because law enforcement officers are entrusted with the respon-

sibility of protecting the public from harm. Often, this work is done in

anenvironment inwhichpublicopinion of thepolicesubculture is low

and where the demands and stress of police work may be unappreci-

ated. Psychological screening is one mechanism for identifying offi-

cer candidates that may be unable to uphold their responsibilities in

this environment.

Many law enforcement agencies use personality measures as partof their employee selection procedures. Ash, Slora, and Britton

(1990) surveyed 99 major metropolitan and U.S. state police depart-

ments. Of the 99 surveys distributed, 62 were returned. The research-

ers found that 42 (67.7%) of the departments that responded reported

using personality tests as part of their employee selection procedures.

Twenty-five (40.3%) of the departments reported using two or more

personality tests foreach candidate. Although the frequent useof per-

sonality testing in officer selection procedures suggests that those

involved in the officer selection process believe these tests contribute

useful information, theextent to which personality tests arepredictive

of officer performance is unclear. Numerous published and unpub-

lished studies have examined the relation between personality mea-sures and law enforcement officer performance. Most frequent in the

empirical literature are studies attempting to identify specific person-

ality test scales or groups of scales that arepredictive of objective per-

formance criteria, suchas termination, absenteeism, tardiness, citizen

complaints, and commendations, or subjective performance criteria,

650 CRIMINAL JUSTICE AND BEHAVIOR

AUTHORNOTE:Jorge G. Varelais with the U.S.Air Forceat WilfordHall Medical

Center in San Antonio, Texas. This research is based on Jorge G. Varelas doctoral

dissertation at The University of Alabama.Forrest Scogin and Jamie Stump, Depart-

mentof Psychology, TheUniversity of Alabama.Marcus T. Boccaccini, Ph.D., is now

at Sam Houston State University, Texas.Alicia Caputo is now with theDepartment of

Human Services in Alexandria, Virginia. Correspondence concerning this manu-script should be addressed to Forrest Scogin, Ph.D., Psychology Department, Box

870348, The University of Alabama, Tuscaloosa, AL 35487; telephone: 205-348-

1924; fax: 205-348-8648; e-mail: [email protected].



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such as supervisor andpeer ratings of performance.Despite thegrow-

ing number of studies in this area, there is a lack of quantitative inte-

gration in this literature. Because researchers in this field have used

different personality assessment instruments, different outcome mea-

sures, and different study designs, there is no clear consensus about

what can be predicted from law enforcement officerspersonality test

scores. The present study uses meta-analysis to provide a clearer

picture of the validity of personality measures in law enforcement

settings.

VALIDITY OF PERSONALITY MEASURES IN EMPLOYMENT SETTINGS

Severalpublishedmeta-analyseshave examinedthevalidity of per-

sonality testing in employment settings. Findings from these meta-

analyses are reviewedhere for twopurposes. First, the research meth-

odologiesused in thesemeta-analyses provide a basis for thedesign of

this study. Second, three of the meta-analyses included some data

from law enforcementsettings,andtheir results provide anestimateof

theeffect sizes that might be expectedfrom a meta-analysis basedon a

more comprehensive review of the existing law enforcement officer

performance literature.

Schmitt, Gooding, Noe, and Kirsch (1984) conducted a meta-

analysis of 99 employee selection studies published in the Journal of

Applied Psychology or Personnel Psychology between 1964 and1982.1 These researchersexamined the effectiveness of several differ-

ent types of predictors (personality measures, aptitude assessments,

physical ability measures) across several occupational groups (pro-

fessional, managerial, clerical, sales, skilled, and unskilled). Perfor-

mance criteria included both subjective (performance ratings) and

objective (turnover, achievement/grades, status changes, and wages)

measures. Using only personality measures as predictors, an overall

mean correlation of .149 was observed. A mean correlation of .206

was observed for subjective performance criteria, and mean correla-

tions ranged from .121 (turnover) to .152 (achievement/grades) for

objective performance criteria. Schmitt et al. also found that effect

sizes varied depending on study design characteristics. Studies usinga concurrent design (data from incumbents) or purely predictive

design (recruit data not used for hiring decisions) produced larger

Varelaet al./ PERSONALITYTESTINGIN LAWENFORCEMENT 651



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mean correlations than studies using an employee selection design

(recruit data used for hiring decisions). However, the extent to which

study design characteristics influenced the predictive validity of per-

sonality measures was not reported. In their analyses of study design

characteristics, the researchers combined correlation coefficients

across all available predictors (aptitude assessments, physical ability

measures, personality measures).

Tett, Jackson, Rothstein, and Reddon (1994) conducted a meta-

analysis of 86 studies examining the relation between personality

measures and job performance (see also Tett, Jackson, & Rothstein,

1991). Eleven of these studies were of law enforcement officers or

recruits. The overall corrected sample-weighted mean correlationbetween personality measures and job performance was .174. The

researchers compared correlations for studies that predicted specific

relations between personality measures and performance (confirma-

tory design) andstudies without a clear rationaleforexpecting signifi-

cant results (exploratory design) and found that prediction wassignif-

icantly stronger for confirmatory studies (.238) than exploratory

studies (.035). Mean correlations were also significantly larger for

studies using job recruits (.267) compared to studies using only job

incumbents (.120) and for published studies (.215) compared to dis-

sertations (.049). Although correlations were higher for subjective

performance measures (.186) than objective performance measures

(.103), this difference was not large enough to achieve statisticalsignificance.

Barrick and Mount (1991) conducted a meta-analysis to examine

thepredictivevalidityof theBigFivepersonality traitsacross fiveoccu-

pational groups (professionals, police, managers, sales, andskilled or

semiskilled) for three job performance criteria (job proficiency, train-

ing proficiency, and personnel data). Findings were reported for each

of the Big Five personality dimensions for police officers, despite the

fact this occupational group accounted for only 13% of the 162 sam-

ples included in the meta-analysis. Mean correlations for police offi-

cers, corrected for range restriction and measurement error in crite-

rion and predictor variables, were .09 (extraversion), .10 (emotional

stability), .10 (agreeableness), .22 (conscientiousness), and .00 (open-

ness to experience). Salgado (1997) conducted a similar meta-

analysis using samples from European countries and also reported




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separate effects for law enforcement samples (k= 3 for most effects).

Mean correlations for police officers, corrected for range restriction

and measurement error in criterion and predictor variables, were .13

(extraversion), .15 (emotional stability), .09 (agreeableness), .24

(conscientiousness), and .12 (openness to experience).

In sum, meta-analyses of the validity of personality tests in employ-

ment settings have found that personality test scores have a consistent

but modest relation to job performance indices. Barrick and Mounts

(1991) and Salgados (1997) results suggest that similar effect sizes

might be observed in meta-analyses using only law enforcement

officer samples, but the small number of law enforcement studies

included in these meta-analyses precludes strong predictions. Themeta-analyses reviewed above also suggest that effect sizes can vary

as a result of study design characteristics. Tett et al. (1994) found that

prediction was better for confirmatory studies compared to explor-

atory studies and for job recruits compared to job incumbents. Find-

ings from the Schmitt et al. (1984) meta-analysis suggest that predic-

tion isstrongestforstudies usinga concurrent orpredictivedesignand

weakest for studies using an employee selection design. The question

remains as to whether these same relations exist in studies using per-

sonality measures to predict law enforcement officer performance.

UNPUBLISHED META-ANALYSIS OF PERSONALITY TESTS

IN LAW ENFORCEMENT SETTINGS

To our knowledge, there is at least one existing, but unpublished,

meta-analysis of thevalidityof personality testing in law enforcement

settings. OBrien (1996) conducted a meta-analysis of 29 published

studies (N= 4,466) examining the relation between personality mea-

sures and law enforcement officer performance. In this unpublished

review, an overall validity coefficient of .25 was reported. OBrien

examined several moderator variables, including the personality

instrument (Minnesota Multiphasic Personality Inventory [MMPI]

vs. California Psychological Inventory [CPI]) and type of perfor-

mance criteria that were used (subjective vs. objective; training vs.

incumbent). When comparing the overall predictive validity of theMMPI and CPI, OBrien used two different strategies. First, she

examined effect sizes from studies in which a clinicians interpreta-




7/2

tion ofpersonality test data (sometimes accompaniedbyclinical inter-

view data) were used to predict performance. Under these circum-

stances (k= 10), she found similar effect sizes for the MMPI (.46) and

theCPI (.32).Second, sheexaminedthepredictivevalidityof individ-

ual test scales. Based on the pattern of her results, she concluded that

prediction was stronger for CPI scales (17 of 22 mean validity coeffi-

cients were significantly different from 0) compared to MMPI scales

(1 of 13 mean validitycoefficients was significantly different from 0).

With respect to performance criteria, subjective and objective criteria

were predicted equally well (.20 and .25, respectively), as were train-

ing criteria and actual job performance criteria (.19 and .27,

respectively).Despite the existence of the OBrien (1996) meta-analysis, there

are several reasons whyfurther integrationof this literature is needed.

First, OBrien included only published findings in her meta-analysis.

As a result, it is likely that her effect sizes are inflated, because jour-

nals tend topublish studies with significant findings. Indeed, Tettet al.

(1994) found that correlations between personality measures and job

performance indices were significantly larger in published studies

compared to unpublished studies. Second, OBriens comparison

of personality tests did not include the Inwald Personality Inven-

tory (IPI; Inwald, Knatz, & Shushman, 1982), a measure specifically

designed for screening law enforcement applicants. According to the

publisher of the IPI, Hilson Research, Inc. (2000-2001), their instru-ment is used by more than 30% of the nations state police depart-

ments. Finally, OBriens meta-analysis has not been published and,

to our knowledge, has not been subjected to peer review.

PURPOSE OF THE CURRENT META-ANALYSIS

This article reports the results of a meta-analytic review of the

validity of personality testing in law enforcement settings. Although

previous meta-analyseshave included lawenforcement samples (e.g.,

Barrick & Mount, 1991; Salgado, 1997; Tett et al., 1994), the number

of samples included were small given the size of the law enforcement

officer performance research literature. The current study is designedto extend what is known about the relation between personality test

scores and law enforcement officer job performance by examining




8/28

effects from a substantially larger number of law enforcement sam-

ples (both published and unpublished) compared to previous meta-

analyses. We report effects for the overall validity of personality tests

in this setting and examine the impact of several moderator variables,

including predictor type (MMPI, CPI, IPI), study design characteris-

tics, sample characteristics, and publication status (published vs.

unpublished findings).

METHOD

CASE SELECTION

Data for this study were retrieved from scholarly journals, books,

conference presentations, dissertations, theses, and unpublished reports

from practitioners and test publishers. Several methods were used to

identify relevant studies. First, searches of PsycInfo, Dissertation

Abstracts International, and the National Criminal Justice Research

Service were conducted to identify all references to personality

assessment in law enforcement settings. Second, all volumes of the

following journals were hand searched:Journal of Applied Psychol-

ogy,Personnel Psychology,Professional Psychology: Research and

Practice, Journal of Police Science and Administration, Law and

Human Behavior,Behavioral Sciences and the Law,Criminal Justiceand Behavior,Journal of Police and Criminal Psychology, andJour-

nal of Personality Assessment. Third, reference lists of already identi-

fied sources were reviewed. Fourth, a request for data was placed in

the American Psychological Association Division 18 (Psychologists

in Public Service) newsletter, and requests for data were submitted to

the Internet discussion forums of the International Association of

Chiefs of PolicePsychological Services Section and Division 41 of

the American Psychological Association (American Psychology

Law Society). Fifth, leading researchers in the field were contacted,

including Robin Inwald (Hilson Research), Robert Hogan (Hogan

Assessment Systems), George Hargrave, Dierdre Hiatt, Curt Bartol,

Larry Beutler, Stanley Azen, and Mark Axelberd. Using these meth-ods, approximately 175 studies containing personality data were

identified.




9/2

Inclusionary and exclusionary criteria were established to identify

data that were appropriate for the current meta-analysis. The follow-

ing inclusionary criteria were used: (a) studies using police, correc-

tional, government security, or other law enforcement personnel, (b)

studies linking personality test data with job performance, (c) studies

using either training performance data or job performance data, and

(d) studies using either objective (e.g., absenteeism, tardiness, citizen

complaints) or subjective (e.g., supervisor ratings, peer ratings) indi-

ces of performance. In addition, the following exclusionary criteria

were used: (a) studies reporting only the results of multiple-predictor

analyses (e.g., multiple regression), and (b) studies failing to report

data necessary for the computation of effect sizes. Of the initial 175data sets that were identified, 78 met the study criteria and were

included in the meta-analysis. Many of the originally identified data

sets were excluded because they did not compare personality data to

job performance (e.g., reported personality data only). Others were

excluded because theyonly reported data for multiple-predictor anal-

yses or because thepersonality tests used were actually measures of

cognitive ability or vocational interest.

Almost half of the usable data sets came from journal articles (k=

36)2, whereas others came from theses or dissertations (k= 27), gov-

ernment reports (k= 4), conference presentations (k= 4), researchers

(k= 3), books or bookchapters (k= 2), and test manuals (k= 2). These

studies ranged in date from 1950 to 1999.

DATA CODING

Coders were used to extract relevant data and study design infor-

mation from the identified studies. Coders were asked to identify data

that were appropriate for inclusion in the meta-analysis and to report

thenumberand type or types of statistical analyses used in each study.

Data were coded as being published if they were obtained from jour-

nal articles, books or book chapters, test manuals, or government

reports, and unpublished if they were obtained from theses, disserta-

tions, conference presentations, or researchers. Officer characteris-

tics, such as the average age and educational level of the sample, werecoded. Job performance predictors were identified, including the

names of personality tests andscales that were used. Measures of offi-




10/

cer performance were identified and coded as either objective (repri-

mands, complaints, suspensions, days of work missed) or subjective

(supervisor or peer ratings). Performance criteria were also identified

as reflecting either training performance or on-the-job performance.

The amount of time that elapsed between personality testing and col-

lection of performance data (measurement interval) was recorded.

Finally, the design of each study was classified on each of the follow-

ing characteristics: (a) Were the personality tests administered and

used as part of the hiring process (screening) or as part of a research

project only (analogue)? (b) Were the personality tests used to predict

future job performance (predictive design) or were they administered

to incumbent officers to examine the relation between personalitydimensions and current performance (concurrent design)? (c) Did the

authors select tests and/or test subscales based on a priori hypotheses

about the relations between specific personality measures and perfor-

mance indices (confirmatory design), or did they select personality

measures without a clear rationale for expecting significant results

(exploratory design)?

The quality of each study was rated using a modified version of the

Instrument for Evaluating Experimental Research Reports (IEERR)

(Suydam, 1968). The IEERR was developed for rating the quality of

studies comparing the effectiveness of different educational pro-

grams. For this study, modifications to the IEERR were made to

accommodate the nature of the literature under review.3 Items on theIEERR are summed to provide a single study quality rating for each

study.

Coders initially received 2 hours of training and instruction con-

cerning theproper useof thecodingmanualanddata collection forms.

After the initial training was completed, each of the five coders was

given a practice set of studies to code. A second training session was

then conducted to clarify questions about the coding manual and data

collection forms. After the second training session, the coders col-

lected the data used in the meta-analysis.

CODER AGREEMENT

Each study was reviewed and coded by two coders. Discrepancies

about the proper coding of study design characteristics were resolved




11/

through critical discussion. Thecodersratings of study quality on the

IEERR were found to be consistent (r= .89), andsubsequent analyses

examining study quality were completed using the average rating for

each study.

STATISTICAL CONVERSIONS AND CORRECTIONS

Testsof significance wereconverted to rfor use as a commoneffect

size using computational formulas provided by Wolf (1986) and

Rosenthal (1994). In cases where only means andstandard deviations

were reported,rvalues were estimated by first generating test statis-

tics (e.g.,t,F) and then converting these values tor.

An important consideration in theaggregation of correlational data

is the sign of the reported coefficients. Because the purpose of this

study was to determine the magnitude of the effect sizes associated

with prediction of job performance, irrespective of directionality,

absolute values of correlation coefficients were used. Tett, Jackson,

Rothstein, andReddon (1999) have noted that theuse of absolute val-

ues leads to an upward bias in mean validity coefficients. However,

these authors have also reported that the amount of upward bias is

minimal when samplesizes and the value of rho are large (e.g., bias of

.01 when = .15 andN 100). Given such a small estimated bias in

findings, we used absolute values of correlations without correcting

for upward bias.For each variable examined in this meta-analytic review, observed

(uncorrected) and corrected validity coefficients were calculated.

Correlations were corrected for three types of study artifacts: attenua-

tion due to unreliability of measures, attenuation or enhancement due

to range restriction, and attenuation due to dichotomization of out-

come variables (discontinuity). Although these corrections typically

increase the size of correlation coefficients, they are undertaken to

provide estimates of outcome values if thestudies hadbeen conducted

without methodological flaws (Hunter & Schmidt, 1994).

The first correction made to the observed correlations was for

attenuation due to unreliability of predictor variables (Hunter &

Schmidt, 1994). Reliability estimates for predictor variables wereobtained from test manuals, published reliability studies, and study

authors. When more than one reliability estimate was located for a




12/

particular test scale, the largest estimate was used because it led to the

smallest amountofcorrection. Testscalesforwhich no reliability data

were available were left uncorrected.

Thesecondcorrection made to observed correlationswasforatten-

uation or enhancement due to range departure of predictor variables.

When the standard deviation of a sample differs from the population

standard deviation, the observed correlation is distorted. When the

standard deviation of the sample is smaller than the population stan-

dard deviation, the observed correlation is attenuated, and, con-

versely, when the samplestandard deviation is larger than the popula-

tion standard deviation, the observed correlation is inflated. Range

restriction is common inpersonnel selectionsettings because thesam-ple under investigation has typically passed an initial screening and

represents a small proportion of all applicants. In the current meta-

analysis, observed correlations were corrected for range departure

using the procedures recommended by Hunter and Schmidt (1994).

Thecrucial determinant of themagnitudeof this correction is theratio

of thesamplestandard deviation to thepopulation standard deviation.

Correction for range departure could only be completed for studies

that reported sample standard deviations. Estimates of population

standard deviation were found in test manuals, handbooks, and pub-

lished research.

The last correction made to observed single predictor correlations

was for attenuation due to dichotomization of performance variables.The correction formula recommended by Hunter and Schmidt (1994)

was applied to studies reporting dichotomous variables.

According to the Hunter and Schmidt (1994) modelof meta-analysis,

each of the three attenuating or enhancing factors is independent.

Thus, observed correlations were corrected for more than one factor

when applicable using the procedures recommended by Hunter and

Schmidt.

WITHIN-STUDY AGGREGATION

The number of validity coefficients reported in individual studies

ranged from 1 to 1,222. Prior to within-study aggregation, the meta-analytic data set consisted of 3,954 correlations. Validity coefficients

were aggregated within studies so that findings would not be unduly




13/

influenced by studies reporting numerous findings. Specifically, valid-

ity coefficients were aggregated according to categorical moderator

variables of interest. This aggregation procedure was used so that

the influence of moderator variables could be examined in the meta-

analysis. If we had simply computed a single mean across all of the

findings in each study, the influence of moderator variables would

have been lost in the aggregation. For example, if a study used both

turnover and supervisor ratings as performance criteria, aggregating

across these criteria would make it impossible to examine differences

in the prediction of subjective and objective performance criteria.

The effects of the following categorical moderator variables were

examined (see Data Coding section above for variable descriptions):predictor type (MMPI vs. CPI vs. IPI), nature of outcome measure

(subjective vs. objective, training vs. incumbent), study design (con-

firmatoryvs. exploratory, concurrentvs.predictive, screeningvs. ana-

logue), and data source (published vs. unpublished).

Data aggregationswithinstudies were made using sample-weighted

means so that coefficients based on larger samples were accorded

greater weight in the aggregation. Sample-weighted means were also

used to compute the average sampling error estimates (Hunter &

Schmidt, 1990).

TESTING THE SITUATIONAL SPECIFICITY HYPOTHESIS

The variance of a population correlation is used to test the situa-

tional specificity hypothesis. This test is conducted to determineif the

observedmeancorrelationgeneralizes across samples.If theobserved

mean correlation generalizes across samples, it is not situationally

specific. A common way of evaluating the situational specificity

hypothesis is to use thethree-fourths rule. This rule states that when

less than 75% of the population correlation variance is accounted for

by sampling error variance, the role of moderator variables should be

examined (Hunter & Schmidt, 1990). In these situations, the variance

not accounted for by sampling error maybe attributable to systematic

sources, such as study design characteristics or differences in the

characteristics of the samples being studied.

The amount of variance explained by sampling error is calculated

by dividing the sampling error of the population correlation by the




14/

variance of the observed sample-weighted mean correlation. This

value can exceed 100% because sampling error is estimated algebra-

ically, whereas the variance associated with each mean correlation is

determined from the observed effect sizes (Hunter & Schmidt, 1990).

Sampling error was computed using the formula provided by Hunter

and Schmidt (1990). Sampling error estimates were not corrected for

variance due to study artifacts. This approach has been referred toas a

bare-bones meta-analysis (Hunter & Schmidt, 1990, p. 156).

DETECTION OF MODERATOR VARIABLES

The influence of moderator variables was examined using Pearsoncorrelations for continuous moderator variables andz-tests for cate-

gorical moderator variables (see Hunter & Schmidt, 1990, p. 437-

438). Uncorrected validitycoefficients were used inallof themodera-

tor analyses.

FINAL DATA SET CHARACTERISTICS

The meta-analyses reported in this article were conducted using

validity coefficients from 78 studies with a combined total of 11,725

participants. After within-study aggregation, the meta-analytic data

set contained 168 validity coefficients.

RESULTS

PREDICTIVE VALIDITY ACROSS ALL PREDICTOR AND

OUTCOME VARIABLES

Meta-analysis results for all samples and all categorical moderator

subgroups are provided in Table 1. Themean sample-weighted corre-

lationacrossall predictors andoutcomes was.134, which increasedto

.218 when corrected for study artifacts (see Table 1, row 1). The lower

bound of the 95% confidence interval for the uncorrected coefficient

was greater than 0, indicating a statistically significant relation

between personality tests scores and performance criteria. However,

only 64% of the variance in the overall validity coefficient (uncor-




15/

662

TABLE1:

Meta-AnalysisResultsforAllSamples

andCategoricalModerators

O

bs.

Samp.

%Var.(r)Due

LowerCI

UpperCI

Corr.N-

z-Score

Moderator

ka

Nb

N-Wtd.r

c

Va

r.(r)d

ErrorVar.e

toSamp.Error

f

(9

5%)g

(95%)h

Wtd.r

i

Diff.j

Allsample

s

78

11,725

.134

.

011

.007

64

.122

.157

.218

CPI

13

2,049

.155

.

006

.008

100

.141

.169

.251

IPI

11

2,537

.100

.

001

.004

100

.090

.110

.196

MMPI

41

6,940

.108

.

010

.005

52

.096

.120

.206

k

Training

18

3,820

.112

.

006

.006

100

.100

.124

.188

Performan

ce

72

9,747

.143

.

013

.007

59

.129

.157

.231

1.37

Subjective

53

5,962

.134

.

009

.008

89

.118

.150

.192

Objective

41

7,115

.138

.

012

.006

49

.128

.148

.239

.18

Predictive

59

10,185

.125

.

009

.006

68

.115

.135

.216

Concurren

t

21

1,661

.199

.

020

.013

66

.181

.217

.228

2.23*

Screening

49

8,168

.118

.

009

.006

65

.106

.130

.213

Analogue

30

3,616

.162

.

013

.009

67

.148

.176

.226

1.77



16/

663

Explorator

y

64

10,373

.133

.

010

.007

63

.121

.145

.224

Confirmatory

15

1,447

.132

.

013

.009

68

.116

.148

.161

.03

Unpublished

34

5,203

.112

.

012

.007

59

.075

.149

.182

Published

44

6,522

.149

.

009

.007

75

.121

.177

.245

1.57

Note.CPI

=CaliforniaPsychologicalInventory;IPI=I

nwaldPersonalityInventory;MMPI=Minne

sotaMultiphasicPersonalityInventory.

a.Numberofstudiesprovidingdatatothegivenaggregation.

b.Number

ofindividualparticipantscontributingdatatothegivenaggregation.

c.Observe

dsample-weightedmeancorrelation.

d.Varianceintheobservedcorrelations.

e.Samplin

gerrorvariance.

f.Proportionofvarianceintheobservedcorrelationsduetosamplingerror.

g.Lowerlimitof95%confidenceinterval(CI)aroundtheobservedsample-weightedmeancorrelation.

h.Upperlimitof95%confidenceintervalaroundtheo

bservedsample-weightedmeancorrelation

.

i.Correcte

dsample-weightedmeancorrelation(meas

urementerror,rangerestriction,discontinuity).

j.Significa

ncetestofthedifferencebetweenuncorrec

tedvaliditycoefficientforthevariableinthisrowandtherowdirectlyaboveitusingaz

score.

k.SeeTab

le2forsignificancetestscomparingtheMM

PI,CPI,andIPI.

*p .05 for all correlations.



19/

unpublished meta-analysis, in which it was found that 17 of 22 CPI

scales were predictive of officer performance compared to only 1 of

13 MMPI scales. Onepossible reason for thesuperior performance of

the CPI in this setting is that the CPI is designed to be a measure of

normal personality traits (Gough, 1995), whereas the MMPI and IPI

are, for themost part,measures of psychopathology, deviant personal-

ity traits, and maladaptive behavior. In many law enforcement set-

tings, applicants must survive a rigorous initial screening process

before they are asked to complete a personality measure. The screen-

ingprocess often includes civil service testing, background investiga-

tions, criminal history investigations, and evaluative interviews. Most

pathological job candidates are eliminated during this process. Per-sonality measures designed to detect pathological personality traits

may be redundant when they are administered after the initial screen-

ing process. In contrast, personality measures that are designed to

assess normal personality traits, such as the CPI, may be more useful

in this context because they provide information that is not obtained

during the initial screening process. For instance, the CPI was

designed to provide information about consistent styles of interper-

sonal behavior. Because being a successful police officer requires

effective interpersonal skills (e.g., interacting with community

members, other officers, and supervisors), the CPI may be a useful

measure for predicting this important aspect of officer performance.

Second, mean correlations were larger for studies using a concur-rent design compared to those using a predictive design. This finding

suggests that personality measures are somewhat better at predicting

current job performance than future job performance. Studies using a

concurrent design examine the relation between personality test data

and officer performance at the same point in time, whereas studies

using a predictive design attempt to link personality test data with

future performance. If an officer is experiencing noticeable psycho-

logical problems, it makes sense that these problems would affect his

or hercurrent performance. However,current psychological problems

may not always affect future performance, which reduces the likeli-

hood that measures of psychological functioning can be used to

predict future job performance.

Finally, there was no significant difference in mean effect sizes

from published and unpublished studies. One reason for conducting




20/

this meta-analysis was that previous meta-analyses of personality

testing in law enforcement settings have focused on data from pub-

lished studies (e.g., Barrick & Mount, 1991; OBrien, 1996; Salgado,

1997). The current meta-analysis included data from both published

and unpublished studies. Because scholarly journals tend to publish

studies with significant findings, it is often expected that effects from

unpublished studies will besmaller than those from publishedstudies.

Indeed, Tettet al. (1994) found in their meta-analysis that correlations

between personality measures and job performance indices were sig-

nificantly largerinpublishedstudies. Onepossibleexplanation forthe

lack of a significant difference in the current meta-analysis is that

many of the unpublished studies may have gone unpublished for rea-sons other than significance of their findings. Indeed, many of the 34

unpublished studies included in the meta-analysis did contain signifi-

cant effects. Because many of the unpublished studies were masters

theses and doctoral dissertations, it is possible that they were never

publishedbecause graduate studentsconductingthe researchwerenot

interested in pursuing academic careers. It is also possible that they

may have been submitted for publication but were rejected because

they did not provide enough new information to warrant publication.

Irrespective of the reasons why these unpublished studies were never

published, the inclusionof so many unpublished studies in thecurrent

meta-analysis suggests that the effects reported here are not likely to

be inflated due to the inclusion of only published research.

IMPLICATIONS FOR PRACTICE

Although effect sizes observed in this meta-analysis are modest,

there are several reasons why it would be inappropriate at this point to

conclude that personality measures should not be used in the law

enforcement officer hiring process. First, the hiring process in law

enforcement settings is lengthy and complex, and job candidates are

typically evaluated on a number of different psychological and medi-

cal variables. Personality functioning is just one of these variables.

Each variable is intended to provide a unique piece of information

about a job candidate that can be combined with other pieces of infor-mation to provide an estimate of the candidates suitability for hire.

Support for using this type of multifaceted approach comes from




21/

research studies showing that predictive validity can be enhanced

when different types of psychological and medical variables are used

in combination to predict officer performance (e.g., Scogin,

Schumacher, Gardner, & Chaplin, 1995).

Second, it should not be expected that every personality test scale

can predict officer performance. It is likely that many personality test

scales are not predictive of officer performance and that practitioners

should only interpret the few scales that have a meaningful relation to

officer performance. This argument is supported by findings from

studies in which researchers have been able to use multiple-predictor

analyses (e.g., multiple regression, discriminant function analysis) to

identify optimally weighted combinations of personality test scalesthat can be used to successfully predict officer performance. We have

identified 41 studies (published andunpublished) that have used mul-

tiple predictor analyses to predict law enforcement officer perfor-

mance from personality test data. It would be inappropriate to make

conclusions about the predictive validity of personality tests in law

enforcement settings without considering the findings from these 41

studies. Table 4 contains a stem and leaf plot of the effect sizes from

the 128multiple predictoranalyses reported in these studies. Although

caution should be used when interpreting the values in Table 4

because they have not been corrected for capitalization on chance,

they clearly show that prediction of officer performance can be quite

good when anoptimallyweightedsubsetof personality scalesis used.Finally, the purpose of using personality measures in the law

enforcement officer hiring process is to reduce the likelihood that a

dangerous officer will be hired. Dangerous officers can be harmful to

members of the public, to fellow officers, and to the publics trust in

law enforcement agencies. Given the risks associated with allowing a

dangerous officer to be hired, the small overall predictive effects of

personality measures may be salient in some specific cases.

FUTURE RESEARCH

Findings from the current meta-analyses have implications for

future research, both at the meta-analytic and individual study levels.At the meta-analytic level, the existing research literature can be used




22/

to examine the validity of individual personality tests scales.Although we found that the overall validity coefficients associated

with personality measures were modest, there may be individual test

scales that are more strongly associated with officer performance.

Moreover, the existing research literature can be used to examine the

predictive validity of specific personality traits, such as those of the

five-factor model, in predicting specific components of law enforce-

ment office performance (e.g., Barrick & Mount, 1991; Salgado,

1997).

Finally, given therelatively superior performanceof theCPI, future

individual studies should examine the predictive validity of other

existing measures of normative personality traits, such as the 16 Per-

sonalityFactor Questionnaire,Personality Assessment Inventory, and

NEO Personality Inventory. There currently are an insufficient num-


TABLE 4: Stemand LeafDiagram of Effect Sizes FromMultiple-Predictor Analy-

ses Examining the Relation Between Personality Measures and Offi-

cer Performance

5 .0 : 02344

12 .0 : 555677778999

13 .1 : 0000012344444

18 .1 : 555566666677777888

9 .2 : 122224444

15 .2 : 556677778889999

10 .3 : 0000013334

12 .3 : 566667778899

8 .4 : 00112334

5 .4 : 55559

6 .5 : 111122

5 .5 : 556674 .6 : 0001

1 .6 : 6

2 .7 : 02

1 .7 : 7

0 .8 :

0 .8 :

1 .9 : 3

0 .9 :

1 1.0 : 0

Note. Diagram includes 128 coefficients from 41 studies (published and unpublished).Coefficients are multiple correlations (R) from regression analyses and phi coefficientscalculated from discriminant function analysis classification tables. A list of these stud-ies is available from the first author (JGV).



23/

ber of existing studies to provide reliable estimates of the predictive

validityof theseinstruments in lawenforcementemployment settings.

NOTES

1. Schmitt,Gooding,Noe,& Kirsch (1984)didnot reporthowmany, if any, ofthe 99 studies

included in their meta-analysis examined employee selection in law enforcement settings.

2. Data from some samples were reported inmultiplejournalarticles. Forthe purpose of this

study, effects from different articles reporting on the same sample were grouped together as

being from the same sample.

3. The version of the revised Instrument for Evaluating Experimental Research Reports

(IEERR) that was used in this study is available from the first author.

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