PERSONNEL PSYCHOLOGY2007, 60, 995–1027

IN SUPPORT OF PERSONALITY ASSESSMENT
IN ORGANIZATIONAL SETTINGSDENIZ S. ONES AND STEPHAN DILCHERT
Department of Psychology
University of Minnesota
CHOCKALINGAM VISWESVARAN
Department of Psychology
Florida International University
TIMOTHY A. JUDGE
Department of Management
University of Florida

Personality constructs have been demonstrated to be useful for explaining and predicting attitudes, behaviors, performance, and outcomes inorganizational settings. Many professionally developed measures of personality constructs display useful levels of criterion-related validity forjob performance and its facets. In this response to Morgeson et al. (2007),
we comprehensively summarize previously published meta-analyses on(a) the optimal and unit-weighted multiple correlations between the Big
Five personality dimensions and behaviors in organizations, including
job performance; (b) generalizable bivariate relationships of Conscientiousness and its facets (e.g., achievement orientation, dependability,
cautiousness) with job performance constructs; (c) the validity of compound personality measures; and (d) the incremental validity of personality measures over cognitive ability. Hundreds of primary studies anddozens of meta-analyses conducted and published since the mid 1980s
indicate strong support for using personality measures in staffing decisions. Moreover, there is little evidence that response distortion among
job applicants ruins the psychometric properties, including criterionrelated validity, of personality measures. We also provide a brief evaluation of the merits of alternatives that have been offered in place oftraditional self-report personality measures for organizational decision
making. Given the cumulative data, writing off the whole domain ofindividual differences in personality or all self-report measures of personality from personnel selection and organizational decision making iscounterproductive for the science and practice of I-O psychology.

Personnel Psychology recently published the revised and enhanced
transcripts from a panel discussion held during the 2004 annual conference
None of the authors is affiliated with any commercial tests, including personality, integrity, and ability tests.
Correspondence and requests for reprints should be addressed to Deniz S. Ones, Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN
55455-0344; deniz.s.ones-1@tc.umn.edu.
C 2007 BLACKWELL PUBLISHING, INC.
COPYRIGHT 

995

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PERSONNEL PSYCHOLOGY

of the Society for Industrial and Organizational Psychology. The panelists
included M. A. Campion, R. L. Dipboye, J. R. Hollenbeck, K. Murphy,
and N. Schmitt who, among them, served as editors or associate editors of
Personnel Psychology and Journal of Applied Psychology between 1989
and 2002. The panelists, and the article generated (hereafter referred to asMorgeson et al. [2007]) questioned the usefulness of personality measuresin personnel selection primarily on the grounds of purportedly low validity
and secondarily because of perceived problems with response distortion.
In this article, we first scrutinize and summarize the meta-analytic research documenting the usefulness of personality variables for a broad
spectrum of organizational attitudes, behaviors, and outcomes. Second,
we consider the impact of potential response distortion on the usefulnessof personality measures. Third, we briefly evaluate the desirability, feasibility, and usefulness of alternatives that have been offered in place ofself-report personality measures for organizational decision making.
What Are the Validities of Personality Measures?

Central to Morgeson et al.’s critique was the argument that the validity of personality measures is low and has changed little since Guion and
Gottier’s (1965) negative review. However, qualitative reviews often reacherroneous conclusions (Cooper & Rosenthal, 1980), and vote counting approaches to literature review, such as that employed by Campion in thetarget article (as well, of course, by Guion and Gottier’s review), are similarly defective (e.g., Hedges & Olkin, 1980; Hunter & Schmidt, 2004).1
In responding to claims by Morgeson et al., we rely on meta-analytic,
quantitative summaries of the literature.2

1
Later in this article, using an example from Campion’s table, we illustrate how suchsummaries lead to erroneous conclusions about the state of the research literature on personality and social desirability.
2
Campion asserted that “gathering all of these low-quality unpublished articles andconducting a meta-analysis does not erase their limitations. . . . [T]he findings of the metaanalyses cannot be believed uncritically. I think they overestimate the criterion-related
validity due to methodological weaknesses in the source studies” (p 707).
Appropriately conducted meta-analyses are not necessarily adversely affected by lowquality studies; in fact, meta-analysis is a quantitative way of testing the effect such lowquality studies have on conclusions drawn from a body of literature. Moderator analyses
related to the quality of the sources, such as those now frequently conducted in metaanalyses, will reveal systematic shortcomings of low-quality studies (whether they inflateor deflate the magnitude, or simply increase the variability in observed effect sizes). Votecounting approaches, however, lack such systematic and objective mechanisms to safeguard
us against low-quality studies unduly influencing conclusions drawn from literature reviews.

DENIZ S. ONES ET AL.

997

The Evidence: Multiple Correlations Based on Quantitative Reviews

We sought and located the most comprehensive meta-analyses thathave examined the relationships between the Big Five and the following
variables: (a) performance criteria (e.g., overall job performance, objective
and task performance, contextual performance, and avoidance of counterproductive behaviors), (b) leadership criteria (emergence, effectiveness,and transformational leadership), (c) other criteria such as team performance and entrepreneurship, and (d) work motivation and attitudes.
In our summary we report both uncorrected, observed relationships,
as well as corrected, operational validity estimates. Observed correlations
are biased due to the influence of statistical artifacts, especially unreliability in the criteria and range restriction (see Standards for Educationaland Psychological Testing; AERA, APA, & NCME, 1999). Thus, it isour position that conclusions should be based on operational (corrected)
validities. This has been the scientifically sound practice in over a dozen
meta-analyses of personality measures and in over 60 meta-analyses ofcognitive and noncognitive personnel selection tools (e.g., interviews, assessment centers, biodata, situational judgment tests).
Morgeson et al. (2007) criticized some previous meta-analyses of personality variables on the grounds that corrections were applied for either predictor unreliability or imperfect (incomplete) construct measurement (or both), a procedure that “overestimate[s] the operational validity”(p. 706). For the summary that follows, we employed only values corrected for attenuation due to criterion unreliability (and range restriction
where appropriate).3 We do wish to note that if one carefully scrutinizes the personnel selection literature, the meta-analyses on personality
generally have made more conservative corrections—using higher estimates for the reliability of performance (Barrick & Mount, 1991) and not

3
Operational validity estimates should only be corrected for attenuation due to unreliability in the criterion (and, where appropriate, range restriction), but not for unreliability
in the predictor measures, in order to provide realistic estimates of the utility of tests asthey are used in applied settings, including in selection (Hunter & Schmidt, 1990, 2004;
Morgeson et al., 2007).
In cases where estimates presented in meta-analyses were corrected for predictor unreliability or imperfect construct measurement, we attenuated these estimates using the original
artifact distributions reported in the meta-analyses. If no predictor reliabilities were reported, we employed meta-analytically derived estimates of personality scale reliabilities
provided by Viswesvaran and Ones (2000). In meta-analyses where relationships were only
corrected for sampling error (i.e., sample-size weighted mean rs), we applied corrections
for attenuation due to unreliability in the criterion, using reliability estimates reported in theoriginal meta-analyses where possible. In the few cases where criterion reliabilities were
not available to make these corrections, we used appropriate reliability estimates from theliterature (see notes to Tables 1, 2, and 3 for details). This strategy enabled us to summarize only psychometrically comparable validities that represent appropriate estimates of theapplied usefulness of personality measures.

998

PERSONNEL PSYCHOLOGY

correcting for range restriction—unlike meta-analyses of many other kindsof predictors.
Some previous meta-analyses of personality have only reported bivariate relationships with criteria (e.g., Barrick & Mount, 1991; Hurtz& Donovan, 2000). Morgeson et al. (2007) focused on such observed bivariate values. Other studies have used meta-analytic estimates of bivariate
relationships to compute multiple correlations documenting the usefulnessof the Big Five personality dimensions as a set (e.g., Judge, Bono, Ilies,& Gerhardt, 2002). In direct response to Morgeson et al.’s general assertion that personality is not useful, we focus on multiple correlations (both
optimally weighted and unit-weighted) for the Big Five as a set. For metaanalyses that did not report a multiple correlation of the Big Five predicting
the criterion of interest, operational validities reported for individual Big
Five traits were used to compute multiple Rs.4 At the typical primary study
level, multiple regression weights are often problematic. However, basing
multiple regression on meta-analytic intercorrelation matrices to a large
extent circumvents capitalization on chance. Nevertheless, we computed
and report unit-weighted composite correlations for each criterion as well.
Table 1 summarizes the operational usefulness of the Big Five as a
set in predicting job performance and its facets, leadership, and othervariables in organizational settings. The Rs provided represent the best,meta-analytically derived estimates of the applied usefulness of Big Five
personality measures in the prediction of work-related criteria.
For job performance criteria, optimally weighted Rs, computed using
meta-analytic intercorrelations among variables, range between .1 and .45.
Individual overall job performance is predicted with R = .27. Objective(nonrating) measures of performance are predicted with R = .23. Note that
personality variables measured at the Big Five level are even more predictive of performance facets such as counterproductive work behaviors(Rs .44 and .45 for avoiding interpersonal and organizational deviance,
respectively), organizational citizenship behaviors (R = .31), and interpersonal behaviors of getting along (R = .33) and individual teamwork(R = .37).
In addition to performance and facets of performance, the Big Five
personality variables are far from trivial predictors of leadership and othercriteria such as training performance. Rs from optimally weighted composites range between .30 and .49 for leadership criteria. There are sizeable

4
To estimate the operational validity of the Big Five as a set, operational validities
were obtained from the most comprehensive meta-analyses available and used to compute
optimally and unit-weighted multiple correlations between measures of the Big Five andeach of the criteria. Meta-analytic intercorrelations among the Big Five (corrected only forsampling error) were obtained from Ones (1993; also available in Ones, Viswesvaran, &Reiss 1996).

DENIZ S. ONES ET AL.

999

TABLE 1
Meta-Analytic Validities of the Big Five for Predicting Work-Related Criteria
Observed
Criterion

Meta-analytic source

Job performance
Overall job performance Barrick, Mount, & Judge(2001)
Objective performance
Barrick, Mount, & Judge(2001)
Getting ahead
Hogan & Holland (2003)
Getting along
Hogan & Holland (2003)
Expatriate performance Mol, Born, Willemsen,
Van Der Molen (2005)
Individual teamwork
Barrick, Mount, & Judge(2001)
Contextual: Citizenship Borman, Penner, Allen,& Motowidlo (2001)
Contextual: Altruism
Organ & Ryan (1995)
Contextual: Generalized Organ & Ryan (1995)
compliance
CWB: Interpersonal
Berry, Ones, & Sackett
deviance(2007)CWB: Organizational
Berry, Ones, & Sackett
deviance(2007)
Leadership criteria
Leadership
Judge, Bono, Ilies, &Gerhardt (2002)
Leader emergence
Judge, Bono, Ilies, &Gerhardt (2002)
Leader effectiveness
Judge, Bono, Ilies, &Gerhardt (2002)
Transformational
Bono & Judge (2004)
leadership
Other criteria
Training performance
Entrepreneurship
Team performance
Motivational criteria
Goal setting
Expectancy
Self-efficacy
Procrastination

Operational

R
unit

R
optimalR
unit

R
optimal

.12a

.14a

.23a

.27a

.11b

.13b

.20b

.23b

.16c
.17c
.25e

.18c
.20c
.26e

.24cd
.26cd
.35e

.27cd
.33cd
.37e

.23b

.24b

.35b

.37b

.21efg.23efg.29efg.31efg

.11ef
.17ef

.12ef
.19ef

.11efh.20efh.13efh.23efh

.29i

.41

.32ij

.44j

.32

.40

.36j

.45j

.30

.34

.39k

.45k

.31l

.38l

.40k

.49k

.28l

.28l

.36k

.37k

.24

.25

.28j

.30j

Barrick, Mount, & Judge(2001)
Zhao & Seibert (2006)
Peeters, Van Tuijl, Rutte,& Reymen (2006)

.19b

.23b

.33b

.40b

.20m
.36n

.31m
.44n

–
.49dkn–.60dknJudge & Ilies (2002)
Judge & Ilies (2002)
Judge & Ilies (2002)
Steel (2007)

.18i
.17
.35
.42i

.48
.26
.38
.64

.21ik
.23k
.40k
.45dhi

.54k
.33k
.45k
.70dh

(Continued)

1000

PERSONNEL PSYCHOLOGYTABLE 1 (Continued)
Observed
Criterion
Work attitudes
Job satisfaction
Career satisfaction

Meta-analytic source
Judge, Heller, & Mount(2002)
Ng, Eby, Sorensen, &Feldman (2005)

Operational

R
unit

R
optimalR
unit

R
optimal

.28

.33

.31k

.36k

.31o

.36o

.33h

.39h

Note. All values are based on meta-analytic data. Observed multiple correlations (observedR) are based on observed intercorrelations among the Big Five personality dimensions anduncorrected mean predictor–criterion correlations. Operational multiple correlations (operationalR) are based on observed intercorrelations among the Big Five personality dimensions andoperational validity estimates (personality–criterion correlations corrected for unreliability inthe criterion only, and range restriction where indicated). Meta-analytic intercorrelations for
the Big Five were obtained from Ones (1993) and subsequently attenuated using reliability
estimates from Viswesvaran and Ones (2000) in order to reflect sample size weighted mean
correlations that do not take unreliability in the different predictor measures into account.
Personality–criterion correlations were obtained from the meta-analytic sources listed above. If
operational validity estimates were not available, mean observed values or population values
were corrected appropriately using the reliability estimates as listed below. Unit-weighted
composites were computed using composite theory formulas (see Hunter & Schmidt, 2004;
Nunnally, 1978); optimally weighted composites were computed using multiple regression.
a Second-order meta-analytic validity estimates based on independent samples.
b Second-order meta-analytic validity estimates.
c Hogan Personality Inventory scale intercorrelations were obtained from the test manual.
d Operational validity estimates in meta-analysis were also corrected for range restriction.
e Validity estimates based on other-ratings of criteria only.
f Validity for Emotional Stability, Extraversion, Agreeableness, and Conscientiousness only.
Meta-analytic estimate for the validity of Openness was not available.
g Validity estimates based on studies not included in Organ and Ryan (1995).
h Personality–criterion correlations on the population level, attenuated using predictor
reliability estimates from Viswesvaran and Ones (2000).
i Informing computation of the unit-weighted composite, negative predictor–criterion
relationships were taken into account (disagreeableness for goal setting, introversion forinterpersonal deviance, and lack of openness for procrastination).
j Mean sample-size weighted personality–criterion correlations, disattenuated using means ofcriterion reliability artifact distributions used in original meta-analysis.
k Personality–criterion correlations on the population level, attenuated using means ofpredictor reliability artifact distributions reported in original meta-analysis.
l Personality–criterion correlations on the population level, attenuated using means of predictor
and criterion reliability artifact distributions reported in original meta-analysis.
m Mean sample-size weighted d-value on personality scales between regular managers andentrepreneurs, subsequently converted to a correlation coefficient (positive values indicating
entrepreneurs scored higher on the personality scales).
n Estimate for professional teams only. Based on correlations between team elevation on
personality scales and team performance.
o Personality–criterion correlations on the population level, attenuated using predictor
reliability estimates from Viswesvaran and Ones (2000) and criterion reliability estimate from
Judge, Cable, Boudreau, and Bretz (1995).

DENIZ S. ONES ET AL.

1001

Rs for training performance (.40) and entrepreneurship (.31) as well. Furthermore, work teams’ elevations of the Big Five predict team level performance (R = .60).
Unit-weighted composite correlations tell a consistent and very similar story. The Big Five personality variables are predictive of job performance and its facets, leadership, and other work-related criteria. The
unit-weighted Rs range between .11 and .49.
The summary provided in Table 1 certainly belies the view that the Big
Five personality variables have little value for understanding and predicting important behaviors at work. Note that the meta-analyses summarized
have relied on scores from traditional self-report personality measures ofthe sort criticized by the authors of the target article. Moreover, these values are not produced by magical or fanciful corrections. Indeed, they mostcertainly involve lower corrections than meta-analyses of other selection
measures such as interviews, for example. Note that the observed Rs listed
in Table 1 are also in the useful ranges. Observed Rs are on average only .08
correlational points smaller than operational Rs (SD = .04). In short, thetable shows that self-report personality scale scores assessing the Big Fiveare useful for a broad spectrum of criteria and variables in organizationalsettings.
In Table 1, we also list relationships between the Big Five and otherimportant work-related variables such as motivation and attitudes. Thesevariables are listed for completeness sake and to provide a comprehensive
overview of relevant variables that personality traits relate to. Althoughmotivation and work attitudes are qualitatively different from performance
criteria, they are related to important behaviors and outcomes such as theeffort dimension of job performance and turnover (Campbell, Gasser, &Oswald, 1996). We realize that summarizing relationships between personality traits and these variables is not in keeping with some traditionalviews in industrial and organizational psychology, which consider solely
performance measures as appropriate validation criteria. However, we believe that providing this summary is important in light of contemporary
research that has expanded the traditional criterion domain. Readers who
are merely interested in the validity of personality measures for predicting task or overall job performance may confine their evaluation to therespective analyses discussed above. Those readers who are interested ina more comprehensive list of outcomes related to personality may find the
expanded list informative. As scientists, we cannot dictate what the goalsof organizations should be and the types of workforces they want to create
through their personnel selection practices. However, we can provide organizations with valuable information on the effectiveness of the selection
tools that they have at their disposal, as well as the likely outcomes of theiruse in operational settings.

1002

PERSONNEL PSYCHOLOGY

Validity of Conscientiousness and Its Facets

Because evidence suggests that Conscientiousness is the single best,
generalizable Big Five predictor of job performance (Barrick & Mount,
1991; Barrick, Mount, & Judge, 2001; Hough & Ones, 2001; Schmidt &Hunter, 1998), Table 2 presents our summary of meta-analytic operational
validities (bivariate relationships) for Conscientiousness and its facets inpredicting dimensions of job performance. We summarized operational
validity estimates for each predictor–criterion relationship from the mostcomprehensive meta-analyses to date.5 Operational validities for Conscientiousness range from ρ = .04 for the altruism facet of organizational
citizenship behaviors to .38 for avoiding organizational deviance. Conscientiousness scales predict overall job performance (ρ = .23), objective
performance indices (ρ = .19), and task performance (ρ = .15). Operational validities for interpersonal behaviors and aspects of contextual
performance are in the .20s (e.g., getting along, organizational citizenshipbehaviors, individual teamwork).
The achievement facet of Conscientiousness predicts overall job performance (ρ = .18), task performance (ρ = .22), and job dedication aspect of contextual performance (ρ = .34) well. The dependability facet ofConscientiousness appears to be even more valuable for many criteria. Operational validities for dependability are .22 for overall job performance,.15 for task performance, .18 for interpersonal facilitation, .40 for job
dedication, and .30 for avoiding counterproductive work behaviors.
The validities we have summarized for the Big Five (Table 1) andfor Conscientiousness and its facets (Table 2) are on par with othervaluable predictors frequently used in personnel selection and assessment.
For example, meta-analytic operational validities for job performance
have been reported as .36 for assessment centers (Gaugler, Rosenthal,
Thornton, & Bentson, 1987), .44 for structured interviews (McDaniel,
Whetzel, Schmidt, & Maurer, 1994), .35 for biodata (Rothstein, Schmidt,
Erwin, Owens, & Sparks, 1990, as cited in Schmidt & Hunter, 1998),
and .26 and .34 for situational judgment tests (McDaniel, Hartman,
Whetzel, & Grubb, 2007; McDaniel, Morgeson, Finnegan, Campion, &Braverman, 2001). Most researchers and practitioners in I-O psychology
regard the magnitude of these validities, which are comparable to those of
personality variables (see Tables 1 and 2), as “useful” in applied settings(see McDaniel et al., 2001; Schmitt, Rogers, Chan, Sheppard, & Jennings,

5
Again, validities listed are the values that have not been corrected for unreliability in thepredictor or for imperfect construct measurement. If operational validity estimates were notreported, the appropriate corrections were applied to attenuate or disattenuate the reported
values (details on each correction and the reliability estimates used are provided in the note
to Table 2).

DENIZ S. ONES ET AL.

1003

TABLE 2
Operational Validity of Conscientiousness and Its Facets for
Performance-Related Criteria
Criterion/predictor
Job Performance
Overall job performance
Conscientiousness
Achievement
Dependability
Order
Cautiousness
Objective performance
Conscientiousness
Getting ahead
Conscientiousness
Getting along
Conscientiousness
Task performance
Conscientiousness
Achievement
Dependability
Order
Cautiousness
Expatriate performance
Conscientiousness
Individual team work
Conscientiousness
Contextual: OCB
Conscientiousness
Contextual: Citizenship
Conscientiousness

Meta-analytic source

Barrick, Mount, and Judge (2001)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)r̄ρ.12a .23a
.10
.18b
.13
.22b
.05
.09b
−.01 −.01b

Barrick, Mount, and Judge (2001)

.10c

.19c

Hogan and Holland (2003)

.12

.17d

Hogan and Holland (2003)

.14

.21d

Hurtz and Donovan (2000)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)

.10
.13
.09
.08
.06

.15
.22b
.15b
.14b
.10b

Mol, Born, Willemsen, Van Der Molen(2005).17e.24e

Barrick, Mount, and Judge (2001)

.15c

.23c

LePine, Erez, and Johnson (2002)

.19

.21f

Borman, Penner, Allen, and Motowidlo(2001)

.19eg

.26eg

.04e

.04e

.17e

.21e

Contextual: Altruism
Conscientiousness
Organ and Ryan (1995)
Contextual: Generalized compliance
Conscientiousness
Organ and Ryan (1995)
Contextual: Interpersonal facilitation
Conscientiousness
Hurtz and Donovan (2000)
Achievement
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dependability
Dudley, Orvis, Lebiecki, and Cortina (2006)
Order
Dudley, Orvis, Lebiecki, and Cortina (2006)
Cautiousness
Dudley, Orvis, Lebiecki, and Cortina (2006)
Contextual: Job dedication
Conscientiousness
Hurtz and Donovan (2000)
Achievement
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dependability
Dudley, Orvis, Lebiecki, and Cortina (2006)
Order
Dudley, Orvis, Lebiecki, and Cortina (2006)
Cautiousness
Dudley, Orvis, Lebiecki, and Cortina (2006)

.11
.16
.06
.10b
.11
.18b
−.01 −.02b
.00
.00b
.12
.20
.23
.05
.04

.18
.34b
.40b
.09b
.07b

(Continued)

1004

PERSONNEL PSYCHOLOGYTABLE 2 (Continued)
Criterion /predictor

Meta-analytic source

CWB: Overall counterproductivity
Conscientiousness
Berry, Ones, and Sackett (2007)
Achievement
Dudley, Orvis, Lebiecki, and Cortina(2006)
Dependability
Dudley, Orvis, Lebiecki, and Cortina(2006)
Order
Dudley, Orvis, Lebiecki, and Cortina(2006)
Cautiousness
Dudley, Orvis, Lebiecki, and Cortina(2006)CWB: Interpersonal deviance
Conscientiousness
Berry, Ones, and Sackett (2007)
CWB: Organizational Deviance
Conscientiousness
Berry, Ones, and Sackett (2007)

Motivation criteria
Goal setting
Conscientiousness
Expectancy
Conscientiousness
Self-efficacy
Conscientiousness
Procrastination
Conscientiousness
Work attitudes
Job satisfaction
Conscientiousness
Career satisfaction
Conscientiousnessρ−.28h −.31b
.00
.00b
−.21

−.30b

−.04

−.06b

−.06

−.10b

−.19

−.21b

−.34

−.38b

.20

.26b

.23h

.30b

.11h

.15b

.10

.12b

Barrick, Mount, and Judge (2001)

.13c

.23c

Zhao and Seibert (2006)

.19i

–

Peeters, Van Tuijl, Rutte, and Reymen(2006).29j.40bdjJudge and Ilies (2002)

.22

.26b

Judge and Ilies (2002)

.16

.21b

Judge and Ilies (2002)

.17

.20b

−.62

−.68dfJudge, Heller, and Mount (2002)

.20

.22b

Ng, Eby, Sorensen, and Feldman (2005)

.12k

.13f

Leadership criteria
Leadership
Conscientiousness
Judge, Bono, Ilies, and Gerhardt (2002)
Leader emergence
Conscientiousness
Judge, Bono, Ilies, and Gerhardt (2002)
Leader effectivenessConscientiousness
Judge, Bono, Ilies, and Gerhardt (2002)
Transformational Leadership
Conscientiousness
Judge, Bono, Ilies, and Gerhardt (2002)
Other criteria
Training performance
Conscientiousness
Entrepreneurship
Conscientiousness
Team’s Performance
Conscientiousnessr̄Steel (2007)(Continued)

DENIZ S. ONES ET AL.

1005

TABLE 2 (Continued)
Criterion /predictor
Learning criteria
Motivation to learn
Conscientiousness
Achievement motivation
Skill acquisition
Conscientiousness
Achievement motivationr̄ρColquitt, LePine, and Noe (2000)
Colquitt, LePine, and Noe (2000)

.31
.27

.35f
.31l

Colquitt, LePine, and Noe (2000)
Colquitt, LePine, and Noe (2000)

−.04
.13

−.05f
.15l

Meta-analytic source

Note. All values are based on meta-analytic data. Personality–criterion correlations wereobtained from the meta-analytic sources listed above. If operational validity estimates were
not available, mean observed values or population values were corrected appropriately
using the reliability estimates listed below.
r̄ = observed validity (uncorrected, sample-size weighted mean correlation). ρ =operational validity (corrected for unreliability in the criterion only, and range restriction
where indicated).
aSecond-order meta-analytic validity estimates based on independent samples.
b
Personality–criterion correlation on the population level, attenuated using mean ofpredictor reliability artifact distribution reported in original meta-analysis.
cSecond-order meta-analytic validity estimates.
dOperational validity estimates in meta-analysis were also corrected for range restriction.
e
Operational validity estimate based on other-ratings of criteria only.
f
Personality–criterion correlations on the population level, attenuated using predictor
reliability estimates from Viswesvaran and Ones (2000).
g
Operational validity estimate based on studies not included in Organ and Ryan (1995).
hPersonality–criterion correlations on the population level, attenuated using means ofpredictor and criterion reliability artifact distributions reported in original meta-analysis.
i
Mean sample-size weighted d-value on personality scales between regular managers
and entrepreneurs, subsequently converted to a correlation coefficient (positive values
indicating entrepreneurs scored higher on the personality scales.
j
Estimate for professional teams only. Based on correlations between team elevation on
personality scales and team performance.
kPersonality–criterion correlation on the population level, attenuated using predictor
reliability estimates from Viswesvaran and Ones (2000) and criterion reliability estimate
from Judge, Cable, Boudreau, and Bretz (1995).
lPersonality–criterion correlations on the population level, attenuated using predictor
reliability estimates from Connolly and Ones (2007a).

1997). If the criticism that the amount of variance explained by personality is supposedly trivial was to be considered seriously, then costly andtime-consuming assessment tools such as situational judgment tests, assessment centers, structured interviews, and biodata do not fare better than
traditional self-report measures of personality.
Different Sets of Personality Variables Are Useful for Different
Occupational Groups

There is a strong tradition of using ability-based predictors in personnel selection (see Ones, Viswesvaran, & Dilchert, 2004, for thosemeta-analyses not listed in Campion’s Table 2). A key finding in the1006

PERSONNEL PSYCHOLOGY

cognitive ability literature is that regardless of the occupation or job underconsideration, ability tests predict overall job performance (Ones, Viswesvaran, & Dilchert, 2005a), and there appears to be little predictive gainfrom specific abilities beyond general cognitive ability (Brown, Le, &Schmidt, 2006; Ree, Earles, & Teachout, 1994). This is one aspect inwhich the domains of ability and personality differ. Apart from Conscientiousness, there seem to be no other personality traits that predict overall
job performance with similarly consistent validities across different jobs.
Instead, for different occupations, different combinations of the Big Fiveyield the best levels of validity.
Table 3 presents a summary of meta-analytic personality validity estimates in predicting job performance among various occupational groups.6
Operational validities that exceed .10 have been highlighted in the Table.
For professionals, only Conscientiousness scales appear to be predictiveof overall job performance. Similarly, for sales jobs, only Conscientiousness and its facets of achievement, dependability, and order predict overall
performance well. For skilled and semi-skilled jobs, in addition to Conscientiousness, Emotional Stability appears to predict performance. For
police and law enforcement jobs, Conscientiousness, Emotional Stability,
and Agreeableness are useful personal characteristics. In customer service
jobs, all the Big Five dimensions predict overall job performance. Finally,
for managers, the Extraversion facets dominance and energy, and the Conscientiousness facets achievement and dependability, are predictive. Thus,
different sets of personality variables are useful in predicting job performance for different occupational groups. The magnitudes of operational
validities of single valuable Big Five dimensions for specific occupations
range between .11 and .25.
Compound Personality and Other Traits

We should note that the Big Five do not exhaust the set of usefulpersonality measures. Judge, Locke, and colleagues’ concept of core selfevaluations has shown itself a useful predictor of performance, controllingfor Conscientiousness (Erez & Judge, 2001) or the entire set of Big Five
traits (Judge, Erez, Bono, & Thoresen, 2003). Crant and colleagues’ measure of proactivity has similarly shown itself to be useful, even whencontrolling for the entire set of Big Five traits (Crant, 1995). It may be thatthese measures pick up more complex personality traits.
Personality scales that assess more than one dimension of the Big Fiveare referred to as compound personality scales (cf. Hough & Ones, 2001;

6

Footnote 5 applies here as well.

DENIZ S. ONES ET AL.

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TABLE 3
Personality Traits Predictive of Job Performance for Various
Occupational Groups
Meta-analytic sourcer̄ρBarrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)

.03a
.07a
−.01a
.05a
.11a
.15
.13
.10
−.02

.05a
.09a
−.02a
.01a
.21a
.26b
.23b
.17b
−.04b

.06c

.11c

Extraversion
Openness
AgreeablenessConscientiousness
Achievement
Dependability
Order
Cautiousness

Barrick and Mount (1991); Hurtz andDonovan (2000); Salgado (1997)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)

.03a
.03a
.05a
.12a
.11
.14
.11
−.11

.05a
.04a
.08a
.19a
.18b
.24b
.19b
−.18b

Customer service
Emotional Stability
Extraversion
Openness
AgreeablenessConscientiousness
Achievement
Order

Hurtz and Donovan (2000)
Hurtz and Donovan (2000)
Hurtz and Donovan (2000)
Hurtz and Donovan (2000)
Hurtz and Donovan (2000)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)

.08
.07
.10
.11
.17
.12
.07

.12
.11
.15
.17
.25
.20b
.11b

Professional
Emotional Stability
Extraversion
Openness
AgreeablenessConscientiousness

Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)

.04a
−.05a
−.05a
.03a
.11a

.06a
−.05a
−.08a
.05a
.20a

PoliceEmotional Stability
Extraversion
Openness
AgreeablenessConscientiousness

Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)

.07a
.06a
.02a
.06a
.13a

.11a
.06a
.02a
.10a
.22a

Occupation
Sales
Emotional Stability
Extraversion
Openness
AgreeablenessConscientiousness
Achievement
Dependability
Order
Cautiousness
Skilled or semi-skilled
Emotional Stability(Continued)

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PERSONNEL PSYCHOLOGYTABLE 3 (Continued)
Occupation
Managerial
Emotional Stability
Extraversion
Dominance
Sociability
EnergyOpenness
Flexibility
IntellectanceAgreeablenessConscientiousness
Achievement
Achievement
Dependability
Dependability
Order
Cautiousness

Meta-analytic sourcer̄ρBarrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Hough, Ones, and Viswesvaran (1998)
Hough, Ones, and Viswesvaran (1998)
Hough, Ones, and Viswesvaran (1998)
Barrick, Mount, and Judge (2001)
Hough, Ones, and Viswesvaran (1998)
Hough, Ones, and Viswesvaran (1998)
Barrick, Mount, and Judge (2001)
Barrick, Mount, and Judge (2001)
Hough, Ones, and Viswesvaran (1998)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Hough, Ones, and Viswesvaran (1998)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)
Dudley, Orvis, Lebiecki, and Cortina (2006)

.05a
.10a
.16
−.01
.12
.05a
.05
.07
.04a
.12a
.09
.07
.02
.10
−.06
.01

.08a
.17a
.27
−.02
.20
.07a
.11
.06
.08a
.21a
.17
.12b
.03
.17b
−.11b
.01b

Note. All values are based on meta-analytic data. Personality-criterion correlations wereobtained from the meta-analytic sources listed above. If operational validity estimates were
not available, mean observed values or population values were corrected appropriately
using the reliability estimates listed below.
r̄ = observed validity (uncorrected, sample-size weighted mean correlation). ρ =operational validity (corrected for unreliability in the criterion only and range restriction
where indicated).
aSecond-order meta-analytic validity estimate.
b
Personality–criterion correlation on the population level, attenuated using mean ofpredictor reliability artifact distribution reported in original meta-analysis.
cSecond-order meta-analytic validity estimate. Because an estimate of this personality–
criterion relationship was not available in Barrick et al. (2001), validity estimates from thesesources were sample-size weighted and then averaged. For the operational validity estimate,
the population level correlation reported in Barrick and Mount (1991) was attenuated using the Emotional Stability reliability estimate obtained from Viswesvaran and Ones (2000).

Hough & Oswald, 2000). Compound personality scales include integrity
tests (see Ones & Viswesvaran, 2001a; Ones, Viswesvaran, & Schmidt,
1993), customer service scales (see Frei & McDaniel, 1998), drug andalcohol, stress tolerance, and violence scales (see Ones & Viswesvaran,
2001b), as well as managerial potential scales (e.g., Gough, 1984; see also
Hough, Ones, & Viswesvaran, 1998; Ones, Hough, & Viswesvaran, 1998).
Perhaps the most studied of all compound personality traits is integrity. Regarding the predictive power and applied value of integrity tests,
we wholeheartedly agree with N. Schmitt (see p. 695) that one should
not “mislead clients with ‘inflated’ estimates of predictive validity using
studies that are based on self-report criteria or concurrent studies.” In fact,
one should not mislead anyone (practitioners or researchers) with any

DENIZ S. ONES ET AL.

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type of validity estimate. That is why we will take the opportunity to clarify the actual operational validity of integrity tests. In the meta-analysis
conducted by Ones et al. (1993), the authors very carefully sorted out studies that were conducted using external criteria and predictive validation
designs. They found enough studies to evaluate the validity of such measures against non-self-report broad counterproductive behaviors, overall
job performance, and objective production records of performance. The
operational validity of integrity tests for productivity (from production
records) was .28 (see Ones et al.’s table 5). In predictive studies conducted on job applicants, the operational validity for externally detected
counterproductive behaviors was .39 for overt integrity tests and .29 forpersonality-based integrity tests (see Ones et al.’s Table 11). Externallydetected broad counterproductive behaviors for these two meta-analyses
included violence on the job, absenteeism, tardiness, and disruptive behaviors but excluded theft (see footnote to Ones et al.’s Table 11). Finally,in predictive studies conducted on job applicants, the operational validity for supervisory ratings of overall job performance was .41 (see Oneset al.’s Table 8). These are the operational validities that organizations
should keep in mind when integrity tests are considered for inclusion inpersonnel selection systems.
The criterion-related validities of other compound personality scales
for predicting overall job performance have recently been summarized byOnes, Viswesvaran, and Dilchert (2005b). Ones and colleagues based theirreview on meta-analyses and focused on operational validities that were
not corrected for predictor unreliability or imperfect construct measurement. The operational validities for compound scales other than integrity
ranged between .19 and .42 for supervisory ratings of overall job performance. In fact, Ones et al., in their summary of the available meta-analyses,
concluded that “not all personality traits are created equal in terms of theirpredictive and explanatory value. Highest validities for predicting overall
job performance using predictors from the personality domain are foundfor compound personality variables. . . . The criterion-related validities of
the compound personality variables presented . . . are among the highest
for individual differences traits predicting overall job performance. In fact,
only general mental ability has superior criterion-related validities for thesame criterion.” (p. 396).
Incremental Validity of Personality Variables

A contribution acknowledged by Murphy and Campion is the potential for personality inventories to provide incremental validity overcognitive variables. Nonetheless, Campion has suggested that previous
reviews (specifically, Schmidt & Hunter, 1998) “overestimated” (p. 707)

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the incremental validity for personality scales, on the grounds thatcriterion-related validities informing incremental validity calculations for
Conscientiousness were inappropriately corrected for imperfect predictor
construct measurement. Even if we put aside incremental validity computations for Conscientiousness, there is still evidence for substantial incremental validity of compound personality measures.
For integrity tests, the multiple correlations for predicting overall job
performance using a measure of cognitive ability and integrity are reported in Table 12 of Ones et al. (1993). The values were .64 for thelowest complexity jobs, .67 for medium-complexity jobs, and .71 for thehighest complexity jobs. Schmidt and Hunter (1998) updated the Oneset al. (1993) integrity test incremental validities for job performance byusing a more precise estimate of the relationship between integrity test
and cognitive ability test scores from Ones’ (1993) meta-analysis. In theprediction of overall job performance, integrity tests yield .14 incremental
validity points over tests of general mental ability.
Incremental validities of other compound personality scales have beenreported in Ones and Viswesvaran (2001a; 2001b; in press). Incremental
validities over cognitive ability in predicting overall job performance range
between .07 and .16. It is important to note that none of these incremental
validity computations involved corrections for incomplete construct measurement or unreliability in the predictor. There is incremental validity to
be gained from using personality measures in predicting overall job performance, even when cognitive variables are already included in a given
selection system.

Conclusions on the Usefulness of Personality Measures in Organizational
Decision Making

The criterion-related validities of self-report personality measures aresubstantial. The Big Five personality variables as a set predict important organizational behaviors (e.g., job performance, leadership, and evenwork attitudes and motivation). The effect sizes for most of these criteria are moderate to strong (.20 – .50). Conscientiousness and its facets
are predictive of performance constructs with useful levels of validity.
Compound personality measures (e.g., integrity tests, customer service
scales) are strong predictors of job performance (e.g., most operational
validities for supervisory ratings of overall job performance are around.40), and they provide substantial incremental validity over tests of cognitive ability. The accumulated evidence supports the use of self-reportpersonality scales in organizational decision making, including personnel
selection.

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Does Response Distortion Among Job Applicants Render Personality
Measures Ineffective?

To determine whether response distortion influences psychometricproperties of self-report personality measures, a critical evaluation of the
faking, social desirability, and impression management literatures is necessary. There are two empirical approaches that have been used to examine
effects on scale scores: lab studies of faking and studies aiming to examine
response distortion among actual job applicants. It is crucial to distinguishbetween these two types of studies in reaching correct conclusions about
the effect of response distortion on the psychometric properties of noncognitive measures. Lab studies of directed faking, where participants (mostly
students) are either directed to fake good or respond as though they were
job applicants, create direct experimental demands on participants. A key
question is whether naturally occurring response distortion is adequately
modeled in lab studies of directed faking (cf. Dilchert, Ones, Viswesvaran,& Deller, 2006).
Lab studies of directed faking typically find that criterion-related validity and construct validity of self-report personality scales are affected byinstructions to fake. Directed faking studies can, by design, only document
maximal limits on direct, outright lying by modeling response distortion
upon strong experimental instructions. However, as Ruch and Ruch (1967,
p. 201) noted, “the real-life situation is not the same as an all-out attempt
to fake when subjects are instructed to do so as part of an experiment‘that doesn’t count’ (Dunnette McCartney, Carlson, & Kirchner 1962).”
There is emerging consensus among those who study faking and response
distortion that faking demand effects in lab studies document a different
phenomenon than response distortion among job applicants. Smith andEllingson (2002, p. 211) pointed out that “most research demonstrating
the adverse consequences of faking for construct validity uses a fake-good
instruction set. The consequence of such a manipulation is to exacerbate
the effects of response distortion beyond what would be expected underrealistic circumstances (e.g., an applicant setting).”
Much of the misunderstandings in the literature stem from combining and/or confusing directed studies of faking and naturally occurring
applicant response distortion (see, for example, Table 1 of Morgeson
et al. [2007], where the problem is further compounded by a vote counting
approach to literature review where large-scale meta-analyses are given
the same weight as investigations conducted in extremely small samples).
Accordingly, in the next sections of this article we examine several commonly voiced concerns regarding faking and social desirability. Given theinterpretational problems with lab studies of directed faking, we confine
our examination only to empirically supported conclusions based on actual
job applicant data.

1012

PERSONNEL PSYCHOLOGY(Non) Effects of Faking and Social Desirability on Criterion-Related Validity

In Morgeson et al. (2007), Campion (Table 1) lists eight studies as providing support for the position that response distortion affects the validityof personality measures. An analysis of these eight studies is presented inAppendix A. Scrutinizing the eight studies listed indicates that five of theeight were studies of directed faking or lab studies where participants wereassigned to experimental response distortion groups. Among these studies, Dunnette et al. (1962) investigated both “fakability” and the degree of
actual faking in a selection context, and were very careful to conclude that
although scores were increased when test-takers were instructed to fake,few applicants actually tend to fake in real employment situations. Anotherstudy offered as supporting the claim that criterion-related validity is affected by faking was conducted on participants in nonmotivating contexts.
Only two of the eight studies were conducted on job applicants and only
one of those two actually included a criterion for which criterion-related
validity could be computed. In that study, criterion-related validities of
personality scales were affected only after corrections for response distortion were applied. Thus, the only study that offered a criterion-related
validity estimate computed among job applicants showed sizeable validity
for the personality measure (but did not support the correction of scoresfor presumed faking).
The best method for estimating operational validities in selection settings is predictive validation based on applicants. In selection settings,
job applicant groups should be the focal interest if response distortion isregarded as a concern. Two large-scale quantitative reviews have reported
substantial criterion-related validities of personality measures in predictive studies, among job applicants. Hough (1998b) reported a summaryof predictive validity studies for personality variables based on the Hough(1992) database (see Hough, 1998b, Table 9.6.). Hough did not correct
the mean observed validities she obtained for range restriction or unreliability in the criterion and predictor. Nevertheless, there were useful andsizeable predictive validities reported. Achievement measures predicted
both job proficiency and training success with a mean observed validityof .19. Predictive studies for educational success yielded mean observed
validities in the .15 to .23 range for potency (dominance facet of Extraversion), the achievement facet of Conscientiousness, and Emotional Stability(adjustment). Finally, predictive validity studies for the counterproductive
work behaviors criterion found substantial mean observed validities for theachievement facet of Conscientiousness (−.33), the dependability facet ofConscientiousness (−.23), Emotional Stability (−.17), and intellectance(.24).

DENIZ S. ONES ET AL.

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As mentioned earlier, Ones et al. (1993) reported the criterion-related
validities for integrity tests based only on predictive validation studies conducted on job applicants. The criterion-related validity of integrity tests issubstantial, even in the potential presence of naturally occurring response
distortion among job applicants (.41 for predicting overall job performance; .39 for overt tests predicting externally detected counterproductive behaviors; and .29 for personality-based integrity tests for externallydetected counterproductive behaviors).
These quantitative reviews summarize predictive studies, where job
applicants are tested and selected on the basis of their scores on personality
measures. They indicate that there is useful criterion-related validity for
self-report personality measures when the stakes involve getting a job.
(Non) Effects of Faking and Social Desirability on Construct Validity

Primary construct validity studies have focused on convergent/
divergent validities of personality scales and/or factor structure under conditions that differ in the degree of response distortion expected. Findings inthis domain show that factor structures of personality inventories hold up
in job applicant samples (e.g., see Marshall, De Fruyt, Rolland, & Bagby,
2005, for the NEO PI-R; Smith & Ellingson, 2002; Smith, Hanges, &Dickson, 2001, for the Hogan Personality Inventory). That is, when thefocus is on actual job applicants, factor structures underlying responses
on personality inventories are in line with those theoretically postulated
for the traits assessed by such inventories.
A recent meta-analytic investigation of divergent validity and factor
structure of two widely used Big Five inventories (NEO PI-R and Hogan
Personality Inventory, Ns ranging from 1,049 to 10,804) also indicatedthat the factor structures of the two inventories held up among job applicants (Bradley & Hauenstein, 2006). Contexts that have been postulated tomotivate job applicants to distort their responses appear to have “a small,practically unimportant, influence on either the intercorrelations among
the Big Five, and no effect on the higher order factor loadings of Big Fivemeasures” (Bradley & Hauenstein, 2006, p. 328).
Investigations employing IRT analyses lead to equivocal conclusions.
Although Stark, Chernyshenko, Chan, Lee, and Drasgow (2001) reported
differential item functioning for applicants versus nonapplicants on a personality test, Robie, Zickar, and Schmit’s (2001) analyses suggest that“personality measures used for selection retain similar psychometric properties to those used in incumbent validation studies.”
A recently published investigation using a within-subjects design (thesame individuals completing a personality measure once under selection,

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PERSONNEL PSYCHOLOGY

and once under development conditions) concluded that there was only
a limited degree of response distortion (Ellingson, Sackett, & Connelly,2007). As both factor structure and convergent/divergent validities holdup in job applicant samples, potential response distortion among job applicants does not appear to destroy the construct validity of personality
measures. There is strong evidence that self-report personality measures“can be successfully used to capture traits in motivating contexts” (Smith& Ellingson, 2002, p. 217).
Presumed Usefulness of Social Desirability Scales in Predicting Various Job
Relevant Criteria

Some authors have suggested (cf. Marcus, 2003, see also Murphy,
p. 712) that impression management may be a useful personal characteristic and even possess predictive validity in occupational settings.
Yet, meta-analyses and large sample primary studies of social desirability
scales report negligibly small correlations for overall job performance inheterogeneous samples (Ones, Viswesvaran, & Reiss, 1996) among managers (Viswesvaran, Ones, & Hough, 2001) and among expatriates (Foldes,
Ones, & Sinangil, 2006). In a meta-analysis that separated self-deceptive
enhancement from impression management aspects of social desirability,
Li and Bagger (2006) also found weak support for the criterion-related
validity of these scales: Neither aspect of social desirability predicted performance well. Social desirability, self-deceptive enhancement, and impression management scales do not appear to be useful in predicting job
performance or its facets.
Conclusion: The Impact of Response Distortion Among Job Applicants
Is Overrated

Understandably, in personnel selection settings, faking and response
distortion are a concern on any self-report, noncognitive measure (be it apersonality test, an interview, a biodata form, or a situational judgment testwith behavioral tendency instructions). For personality measures, there isevidence from job applicant samples documenting their criterion-related
validity and construct validity in motivating contexts, a fact that should
alleviate some of these concerns.
An Evaluation of Alternatives

There are a number of alternatives offered by the authors of the target article that I-O psychologists can pursue with regard to assessing personality

DENIZ S. ONES ET AL.

1015

in organizational settings. In this section, we evaluate the desirability, feasibility, and usefulness of each.
The Use of Faking Indices to Correct Scores

Correcting personality scale scores based on faking or social desirability scale scores has a long history in psychological assessment. Traditionally, social desirability, lie, or impression management scales areused in the corrections (Goffin & Christiansen, 2003). Research to datehas failed to support the efficacy of such corrections in dealing with intentional response distortion (Christiansen, Goffin, Johnston, & Rothstein,
1994; Ellingson, Sackett, & Hough, 1999; Hough, 1998a). The cumulative research evidence shows that “if selection researchers are interested
only in maximizing predicted performance or validity, the use of faking
measures to correct scores or remove applicants from further employment
consideration will produce minimal effects” (Schmitt & Oswald, 2006,
p. 613).
Conditional Reasoning Measures of Personality

Individuals can distort their scores on personality measures when directly instructed to do so. In an effort to construct less fakable measures,
L. James and colleagues have developed conditional reasoning tests ofsome behavioral tendencies, most prominently aggression (James et al.,2005). The premise behind conditional reasoning tests is that individuals use various justification mechanisms to rationalize their behaviors. It
is postulated that test takers’ standing on a given trait is related to thejustification mechanisms they endorse. Conditional reasoning measures
are described as tapping into implicit personality characteristics, thosecharacteristics and tendencies that individuals are not explicitly aware of.
Because of their covert way of assessing noncognitive characteristics,
conditional reasoning measures have been suggested as substitutes (Jameset al., 2005) and supplements (Bing et al., 2007) to traditional self-reportpersonality measures. Hollenbeck (p. 718) states, “I think conditional reasoning might be another approach that is really promising in this regard.
These tests are less direct than traditional measures and focus on how
people solve problems that appear, on the surface at least, to be standard inductive reasoning items. . . . These measures have been found tobe psychometrically sound in terms of internal consistency and test–retest
estimates of reliability, and their predictive validity against behavioral criteria, uncorrected, is over .40.” It is true that James et al. (2005) havepresented evidence that documents the usefulness of conditional reasoning measures. However, it appears that the criterion-related validity was

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PERSONNEL PSYCHOLOGY

drastically overestimated (Berry, Sackett, & Tobares, 2007). In fact, thecumulative evidence to date indicates that validities of the conditional
reasoning test for aggression range between .15 and .24 for counterproductive work behaviors, whereas the mean validity for job performanceis .14 (meta-analytic mean r; Berry et al., 2007). Conditional reasoning
tests have at best comparable validities to self-report personality measures,along with sizeable comparative disadvantages, owing to their difficulty
and cost in development. Furthermore, additional research is needed on theincremental validity of conditional reasoning tests over ability measuresin unrestricted samples as well as on group differences, adverse impact,and differential prediction.
Using Forced Choice Personality Inventories With Ipsative Properties

An approach that has been suggested as having the potential to prevent faking and therefore enhance the usefulness of self-report personality
scales is using forced choice response formats. Forced choice measures
ask test takers to choose among a set of equally desirable options loading on different scales/traits (e.g., Rust, 1999). Unfortunately, the use offorced choice response formats often yields fully or at least partially ipsative scores. Such scores pose severe psychometric problems, including
difficulties in reliability estimation and threats to construct validity (seeDunlap & Cornwell, 1994; Hicks, 1970; Johnson, Wood, & Blinkhorn,
1988; Meade, 2004; Tenopyr, 1988). Furthermore, ipsative data sufferfrom artificially imposed multicollinearity, which renders factor analyses
and validity investigations using such data problematic. Individuals cannot be compared normatively on each scale, rendering ipsative scores useless for most purposes in organizational decision making. In short, forced
choice measures are psychometrically inappropriate for between-subjects
comparison, which of course is the whole point in selection contexts.
Even though recent studies have suggested that some forced choice
formats can yield less score inflation than normative scales under explicit
instructions to fake (Bowen, Martin, & Hunt, 2002; Christiansen, Burns,& Montgomery, 2005), there is also evidence that suggests that a multidimensional forced choice format “is not a viable method to control faking”(Heggestad, Morrison, Reeve, & McCloy, 2006, p. 9). In Heggestad et al.’s
individual-level analyses, the forced choice measure was as affected bydirections to distort responses as when a Likert-type response scale was
employed. Some researchers have reported higher validities for forced
choice measures (e.g., Villanova, Bernardin, Johnson, & Dahmus, 1994).
However, the precise reasons for the higher correlations are unclear. Interestingly, unlike traditional Likert-type personality scale scores, forced
choice personality scores appear to correlate with cognitive ability when

DENIZ S. ONES ET AL.

1017

individuals respond as job applicants (Vasilopoulos, Cucina, Dyomina,
Morewitz, & Reilly, 2006).
Overall, forced choice measures, even if they yield only partially ipsative scores, are not certain to improve criterion-related validity of personality measures (or to do so without compromising the underlying constructs assessed). The challenge for future research will be to find waysof recovering normative scores from traditional forced choice inventories.
Recently, there have been promising IRT-based developments in this area
(McCloy, Heggestad, & Reeve, 2005; Stark, Chernyshenko, & Drasgow,
2005; Stark, Chernyshenko, Drasgow, & Williams, 2006) suggesting that
normative scores could potentially be estimated from ipsative personality
scales (Chernyshenko et al., 2006; Heggestad et al., 2006). However, theusefulness of these approaches in operational selection settings has notbeen tested and their impact on predictive validity is yet unknown.
Constructing One’s Own Personality Measure

To the question “What recommendations would you give about the useof personality tests in selection contexts?” Schmitt responded “first, avoid
published personality measures in most instances. The only time I would
use them is if they are directly linked in a face valid way to some outcome.
Second, I would construct my own measures that are linked directly to jobtasks in a face-valid or relevant fashion” (p. 715).
The primary concern highlighted by Morgeson et al. (2007) is the
purportedly low validity of personality scales. It is unclear to the present
set of authors why homegrown scales should necessarily have superior
validity to professionally constructed, well-validated measures that havebeen shown to possess generalizable predictive power. There is no evidence that we can unearth that suggests that freshly constructed personality scales for each individual application perform better (i.e., have higher
criterion-related validity) than existing, professionally developed and validated off-the-shelf personality scales. On the contrary, there is reason tobelieve that homegrown measures can be deficient, contaminated, or both,and can be lacking in terms of construct and criterion-related validity
unless extraordinary effort and resources are devoted to scale construction, refinement, and validation. Although we are not opposed to the ideaof constructing customized personality measures for particular applications (e.g., organizations, jobs), we are also realistic about what it takes
to develop personality measures to match the psychometric properties
of well developed, off-the-shelf inventories. Harrison Gough, the authorof the California Psychological Inventory, has often reminded us (mostrecently, Gough, 2005) that many items, which, on the surface, suggest
to measure a given personality construct, often do not survive rigorous

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PERSONNEL PSYCHOLOGY

empirical tests of scale construction. For every item that has been included on professionally developed personality scales, dozens of personality items have ended up in item graveyards.
Again, if the issue is poor predictive validity of self-report measures,
which Campion, Murphy, and Schmitt suggest, then constructing a newmeasure for each local application is unlikely to provide much improvement. Such homegrown scales may or may not have construct validity
problems. Their item endorsement rates (i.e., the equivalent of item difficulty for the personality domain) may or may not be well calibrated.
Unlike well developed existing personality measures, they may not haveextensive norms for use in decision making. In short, there are no guarantees that homegrown personality measures would be psychometricallysuperior; in fact, it is reasonable to expect that the opposite may be thecase.
Others’ Ratings of Personality in Personnel Decision Making

An alternative to self-report measures of personality is to seek alternate rating sources. In organizational decision making, peers and previous
supervisors could provide personality ratings, especially for internal candidates. Measuring constructs using multiple measures is always a good
idea, not only in the domain of personality. There are off-the-shelf personality inventories with established psychometric characteristics that havebeen specifically adapted for use with others’ ratings (e.g., NEO-PI-R).
A few field studies have examined the criterion-related validity of peer
ratings of personality for job performance (Mount, Barrick, & Strauss,1994), as well as high school grades (Bratko, Chamorro-Premuzic, & Saks,
2006) and career success (Judge, Higgins, Thoresen, & Barrick, 1999).
Criterion-related validities appear to be somewhat higher than those of
self-reports; other-ratings also have incremental validity over self-reports.
Future research should continue to explore the potential for other-ratings ofpersonality in personnel selection, placement, and promotion decisions.
Strategies to Enhance Prediction From Personality Variables

There is ongoing research into ways to enhance criterion-related validities of personality measures in applied settings. Promising ideas being
pursued include nonlinear effects (e.g., for Emotional Stability, see Le,
Ilies, & Holland, 2007), dark side traits (e.g., Benson & Campbell, 2007),
profile analyses (Dilchert, 2007), and interactions between personality
variables and other individual differences characteristics (see Judge &Erez, 2007, Witt, Burke, Barrick, & Mount, 2002). Another intriguing approach is to use multiple personality measures to assess the same trait. ThisDENIZ S. ONES ET AL.

1019

approach has been shown to yield up to 50% increase in criterion-related
validity for Conscientiousness, depending on the number of scales combined (Connelly & Ones, 2007b), and has been shown to be independentof the effect of merely increasing the reliability of the measures. Future
research should further pursue some of these approaches.
Abandoning the Use of Personality Measures in Personnel Selection
and Decision Making

Morgeson et al. (2007) encourage I-O psychologists to reconsider theuse of published self-report personality tests in personnel selection contexts. The authors state as the primary reason “the very low validity of
personality tests for predicting job performance” (p. 683). We think thatthe quantitative summaries that we have reviewed in the first section of thisarticle refute this assertion. Even validities of .20 translate to substantial
utility gains. As reviewed above, taken as a set, the Big Five personality
variables have multiple correlations in the .40s with many important organizational behaviors. Single compound traits also correlate around .40
with supervisory ratings of performance. Such measures can be expected
to provide incremental validity and, thus, utility in applied settings.
If broad judgments are to be rendered about the validity of personality measures in personnel selection, we advise researchers to consider
the multiple correlations of the measures in use, as well as validities ofcompound personality measures. To evaluate personality measures basedon the Big Five as a set is hardly cheating, as complete inventories of
the Big Five dimensions are, still, among the shortest selection measuresavailable. Multiple correlations between the Big Five traits and many important organizational behaviors and outcomes are generally in the .30s
and .40s, which surely ranks among the more important discoveries inpersonnel selection in the past quarter century. As we have shown in oursummary, these relationships are substantial, as high as or higher than forother noncognitive measures.
In conclusion, “there is considerable evidence that both general cognitive ability and broad personality traits (e.g., Conscientiousness) are relevant to predicting success in a wide array of jobs” (Murphy & Shiarella,
1997, p. 825). The validities of noncognitive predictors, including personality inventories, are “practically useful” (Schmitt et al., 1997, p. 721).
What is remarkable about self-report personality measures, beyond theircriterion-related validity for overall job performance and its facets, is thatthey are also useful in understanding, explaining, and predicting significant
work attitudes (e.g., job satisfaction) and organizational behaviors (e.g.,
leadership emergence and effectiveness, motivation and effort). Any theory of organizational behaviors that ignores personality variables would be1020

PERSONNEL PSYCHOLOGY

incomplete. Any selection decision that does not take the key personality
characteristics of job applicants into account would be deficient.
Conclusions

1. Personality variables, as measured by self-reports, have substantial
validities, which has been established in several quantitative reviewsof hundreds of peer-reviewed research studies.
2. Vote counting and qualitative opinions are scientifically inferior alternatives to quantitative reviews and psychometric meta-analysis.
3. Self-reports of personality, in large applicant samples and actual selection setting (where faking is often purported to distort responses), haveyielded substantial validities even for externally obtained (e.g., supervisory ratings, detected counterproductive behaviors) and/or objective
criteria (e.g., production records).
4. Faking does not ruin the criterion-related or construct validity of personality scores in applied settings.
5. Other noncognitive predictors may derive their validity from capturing
relevant personality trait variance.
6. Customized tests are not necessarily superior to traditional standardized personality tests.
7. When feasible, utilizing both self and observer ratings of personality
likely produces validities that are comparable to the most valid selection measures.
8. Proposed palliatives (e.g., conditional reasoning, forced choice ipsative
measures), when critically reviewed, do not currently offer viable alternatives to traditional self-report personality inventories.

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 Ruch and Ruch (1967) (93) [Results in
opposite direction: Sizeable
criterion-related validity existed, but
dropped when corrected for purported
faking.]high-school and college
students; correcting for
distortion did NOT result inincreases in validity beyond
chance level.](1998) (11) [Did not include any
criterion measures.] Dicken (1963) (98) [Mostly

 Rosse, Stecher, Miller, and Levinsample; did not include any criterion
measures.] Topping and O’Gorman (1997) (12)[Student sample; criterion was
peer-rating of personality.] Ironson and Davis (1979) (64) [Student
sample, criterion was rating ofcreativity.]
 Schmit, Ryan, Stierwalt, and Powell(1995) (19) [Mixed findings: validity
was affected when students were
responding as applicants, but when the
personality measure wascontextualized, validity was strong andhigher than for the nonapplicant
condition.] Dunnette et al. (1962) (101) [Validity
among 45 incumbents instructed to fake
was investigated.]

Studied job applicants/
motivated test takers

 Abbott and Harris (1973) (80) [Student

Studied nonmotivating
contexts

Note. Numbers in parentheses correspond to those listed in the reference list of Morgeson et al. (2007).

Primary study ofcriterion related
validity

Did not study
criterion-related
validity
Did not include
performance criterion

Directed faking/lab study with assignment

APPENDIX
Studies Listed by Campion as Indicating That Response Distortion Affects Criterion-Related Validity of Personality Measures

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