Assessment Center Dimensions: Individual differences correlates and meta-analytic incremental validity
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International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
Assessment Center Dimensions:
Individual differences correlates
and meta-analytic incremental
validity
Stephan Dilchert* and Deniz S. Ones**
*Department of Management, Box B9-240, Zicklin School of Business, Baruch College, One Bernard Baruch
Way, New York, NY 10010, USA. stephan.dilchert@baruch.cuny.edu **Department of Psychology, University
of Minnesota, Minneapolis, MN, USA
This study provides an investigation of the nomological net for the seven primary
assessment center (AC) dimensions identified by Arthur, Day, McNelly, and Eden
(Personnel Psychology, 56, 125–154, 2003). In doing so, the authors provide the first robust
estimates of the relationships between all primary AC dimensions with cognitive ability
and the Big 5 factors of personality. Additionally, intercorrelations between AC dimensions based on sample sizes much larger than those previously available in the metaanalytic literature are presented. Data were obtained from two large managerial samples
(total N ¼ 4985). Primary data on AC dimensions, personality, and cognitive ability
interrelationships were subsequently integrated with meta-analytic data to estimate
incremental validity for optimally and unit-weighted AC dimension composites as well
as overall AC ratings over psychometric tests of personality and cognitive ability. Results
show that unit- and optimally weighted composites of construct-based AC dimensions add
incremental validity over tests of personality and cognitive ability, while overall AC ratings
(including those obtained using subjective methods of data combination) do not.
1. Introduction
A
ssessment centers (ACs) remain a popular tool for
the evaluation of job applicants and employees,
especially for managerial jobs. As an assessment
This article won the 2009 James C. Johnson Student Paper Award
from the International Personnel Assessment Council (IPAC). Portions of this research were contained in an unpublished technical
report [Dilchert, S., & Ones, D. S. (2005, October) Using PDI
Assessment Factors, Competencies, and Exercises. Minneapolis, MN:
Author] and were presented at the annual conference of the Society
for Industrial and Organizational Psychology, New York City, April
2007. The authors would like to thank Robert Lewis and Personnel
Decisions International for making the two primary data samples
available.
method, ACs can be designed to measure a multitude
of individual differences characteristics (e.g., interpersonal skills, communication skills, personality, cognitive
ability). A variety of tools or exercises can be employed
to measure these characteristics as part of an AC (e.g.,
simulations, interviews, in-baskets). Because of the
complexities involved in assessing different personal
characteristics with a variety of measurement techniques, ACs remain a fruitful field for applied research.
The criterion-related and construct validity of AC
ratings has been investigated in many primary studies as
well as several meta-analyses. By now there is little
doubt that ACs are useful tools for performance
prediction. Gaugler, Rosenthal, Thornton, and Bentson
(1987) reported a meta-analytic operational validity of
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AC Dimensions: Nomological Net and Incremental Validity
r ¼ .36 for overall AC ratings predicting job performance (corrected for criterion unreliability and range
restriction). Two meta-analytic updates of the literature
published since 1987 (Hardison & Sackett, 2004; Hermelin, Lievens, & Robertson, 2007) report operational
validity estimates of r ¼ .28 (corrected for unreliability
in the criterion only). These estimates of operational
validity for overall AC ratings are commonly referred to
when ACs are compared with other methods and
predictors to estimate their utility in personnel staffing
(e.g., see Schmidt & Hunter, 1998).
The issue of AC validity, however, is somewhat more
complex. Rather than focusing on validity estimates for
overall scores obtained using the AC method, researchers and practitioners may benefit from examining the
validity of the constructs underlying such scores. A
construct-based approach provides the opportunity
to evaluate the true utility of ACs in applied settings
by estimating their incremental validity over other
commonly used predictors. As this study will show, a
more differentiated validation approach based on AC
dimensions, rather than overall AC ratings, will yield
more promising estimates of operational and especially
incremental validity by taking into account different
predictor combinations.
Recently, Arthur, Day, McNelly, and Edens (2003)
took the first step in this direction by conducting a
meta-analytic investigation of the primary dimensions
underlying AC ratings. Arthur and colleagues argued
that estimates of operational validity of overall AC
ratings cannot be meaningfully compared with validities
of predictors such as personality traits, for example, as
this essentially involves comparing an aggregate of
different constructs (the overall score across several
AC exercises and dimensions) to single constructs (e.g.,
conscientiousness). Furthermore, comparisons of this
sort are comparisons of a method (the AC) with
constructs or traits (e.g., cognitive ability, personality),
rendering investigations of relative predictive value even
less fruitful (Arthur & Villado, 2008). In an attempt to
remedy this problem, Arthur and colleagues established
a taxonomy of seven primary AC dimensions from a list
of 168 lower-order constructs typically measured in
ACs (see Appendix Table A1 for definitions of these
seven dimensions). The authors provided meta-analytic
estimates of the interrelationships among the primary
AC dimensions, and showed that they are reasonably
related to one another (sample size weighted mean
r ¼.52). More importantly, they also investigated the
criterion-related validity for six of these AC dimensions
in predicting job performance. Their results demonstrate that the higher-order dimensions commonly
assessed across various AC exercises are of different
value when predicting overall job performance.
However, in order to determine the usefulness
of AC scores in predicting valued behaviors and
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255
outcomes, we must not only assess their criterionrelated validity but also their potential overlap with
more readily available (and cheaper) assessment tools
such as psychometric tests of personality and cognitive
ability. Such investigations will result in estimates of
incremental validity that AC scores can offer over
other tools in the prediction of performance and other
valued outcomes. Data that speak to this matter are
available for overall AC ratings. Collins et al. (2003)
meta-analytically examined the nomological net of
overall AC ratings that represent aggregates of several
constructs. Collins and colleagues’ meta-analysis provides information on the overlap of overall AC ratings
with tests of cognitive ability (r ¼ :43) and four of the
Big 5 dimensions of personality (agreeableness, extraversion, emotional stability, and openness; r ¼ :12, .36,
.26, and .18, respectively). Unfortunately, the quantitative summary by Collins and colleagues does not speak
to the overlap of construct-based AC dimensions with
these individual differences variables. Similarly, an earlier meta-analytic review published in German (Scholz
& Schuler, 1993) only provided estimates for the
relationships of cognitive ability and the Big 5 with
overall AC ratings. Equivalent investigations for the
construct-based primary dimensions identified by
Arthur et al. (2003) are lacking.
Arthur et al.’s (2003, table 2) definitions of the seven
‘meta-dimensions,’ as well as an examination of the
lower-order dimensions that contributed to each of
these meta-dimensions, provide an indication of which
construct-based AC dimensions can be expected to
overlap at least partially with individual differences
traits. The most obvious associations can be drawn
between AC scores on ‘problem solving’ and cognitive
ability. According to Arthur and colleagues, problem
solving is the ability to gather information, to effectively
analyze data, and to generate viable ideas and solutions
to problems. These skills and abilities are at the heart of
general mental ability as operationalized by standardized tests (Carroll, 1993), and we would thus expect a
moderate to strong relationship between AC problemsolving dimension and cognitive ability test scores.
Based on the definition of the six remaining AC
dimensions we would not expect much overlap with
cognitive ability tests, and thus postulate potential for
these AC scores to increment validity of ability measures. However, we stress that cumulative or largescale evidence regarding these relationships is not
available in the published literature, and thus their
incremental validity is unknown.
We would also expect that many of the non-cognitive
AC dimensions display moderate to strong associations
with personality traits as operationalized by standardized tests. The AC dimensions ‘influencing others’
(conceptually related to leadership skills) and ‘drive’
(which captures aspects of ambition) should both relate
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
256
to extraversion, while the latter should also carry some
conscientiousness variance, incorporating both achievement and persistence (Arthur et al., 2003). Other AC
dimensions that are easily linked to personality traits on
a conceptual level are ‘organizing and planning’ (which
bears conceptual similarity to facets of conscientiousness, especially order) and ‘tolerance for stress’ (which
falls squarely into the domain of emotional stability). Yet
again, large-scale evidence for the overlap between
these primary AC dimensions and personality traits
(whether measured by standardized tests or other
methods such as interviews or observer ratings) is
lacking.
Based on data presented by Schmidt and Hunter
(1998), we know that the potential for incremental
validity of overall AC scores over tests of general mental
ability is small (.02). Similarly, the meta-analytic estimates provided by Collins et al. (2003) can be used to
determine that the incremental validity of overall AC
ratings over psychometric tests of personality and
cognitive ability is negligible. However, the lack of
correlational evidence for AC dimensions hinders parallel investigations for construct-based AC scores. If
correlations between the primary AC dimensions identified by Arthur et al. (2003) and personality and
cognitive ability tests were available, the incremental
validity of optimally as well as unit-weighted AC score
composites over such tests could be established by
multiple regression procedures using the meta-analytic
operational validity estimates for all predictors. Additionally, knowledge of these relationships would allow
organizations to supplement existing predictor batteries with only a few AC dimensions in a targeted
manner, in order to maximize overall validity efficiently.
1.1. The present study
The goal of this study is to further AC research by
examining the nomological net of the seven primary AC
dimensions identified by Arthur et al. (2003). First, we
provide the first large-scale investigation of the overlap
between AC dimensions with individual differences
traits (cognitive ability and personality) in two independent samples (total N ¼ 4985). The sample sizes available in our primary data exceed those of prior
investigations of overall AC ratings, even of metaanalytic ones (e.g., Collins et al., 2003; Scholz and
Schuler, 1993). Second, we integrate the findings from
our primary data with meta-analytic predictive validity
estimates for ACs, personality, cognitive ability, as well
as meta-analytic data on the interrelationships of these
predictors. The resulting matrix of meta-analytic and
large sample primary data provides the best estimates
of the relationships between AC dimensions, overall
AC ratings, the Big 5 personality dimensions, cognitive
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
Stephan Dilchert and Deniz S. Ones
ability, and job performance. Subsequently, this matrix
of intercorrelations is used to estimate the incremental
validity of various combinations of AC scores (overall
AC ratings, single dimensions, as well as unit- and
optimally weighted dimension composites) over tests
of personality and cognitive ability (and vice versa).
Thus, the present research addresses four previously
unanswered questions: (1) What is the relationship
between the seven meaningful primary AC dimensions
identified by Arthur et al. (2003) and the Big 5
personality factors and cognitive ability? (2) What is
the incremental validity that can be expected from AC
ratings when these primary dimensions are combined
(using both optimal and unit-weights) over tests of
cognitive ability and personality? (3) Can useful levels
of incremental validity be obtained by adding only one
AC dimension to tests of personality and cognitive
ability? (4) What is the incremental validity that tests of
personality and cognitive ability in turn add over AC
dimension composites?
2. Method
The analytic approach of this study was twofold: first,
we strove to provide the best estimate of incremental
validity that ACs offer over tests of personality and
cognitive ability, based on meta-analytic estimates of
operational validities and predictor relationships where
possible. In this investigation, only the previously unknown intercorrelations between AC dimensions and
tests of personality and cognitive ability were estimated
from primary data. The second goal was to provide a
parallel investigation using only primary data on predictor interrelationships, in order to provide a check on
the theoretically derived results as they would occur in
a real-world assessment setting.
2.1. Meta-analytic data
In order to estimate the incremental validity of one
predictor over another in predicting a given criterion,
the criterion-related validity of each predictor as well as
the interrelationship between the predictors must be
known. Thus, to inform computations of criterionrelated and incremental validity of ACs, we strove to
obtain the best validity estimates for AC ratings, the Big
5 personality factors, and cognitive ability, as well as the
best estimates of all predictor interrelationships. For
this purpose we consulted the most recent and comprehensive meta-analytic summaries in each research
domain.
Next, we obtained sample size weighted mean
correlations between all predictors (not corrected for
unreliability or range restriction) from these meta-
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AC Dimensions: Nomological Net and Incremental Validity
257
analyses. Where only corrected estimates were available we obtained the respective artifact distributions
and attenuated true validity estimates appropriately.1
We also obtained estimates of each predictor’s criterion-related validity for predicting job performance. For
these estimates, operational validities (corrected for
unreliability in the criterion and range restriction where
appropriate) were obtained where possible. In order to
most accurately estimate the operational validity of a
predictor combination in applied settings, it is necessary
to use uncorrected estimates of predictor intercorrelations, but estimates of criterion-related validity that are
corrected for range restriction and unreliability in the
criterion measures (Schmidt & Hunter, 1998). The
operational validity of predictor combinations with
more than two predictors can then be calculated using
multiple regression analyses on the full correlation
matrix.
In many cases multiple meta-analytic estimates were
available. In selecting those that would contribute to
our analyses, we took care to match predictor and
criterion scales to the current investigation. Since ACs
are mostly employed for medium to high complexity
jobs (e.g., to assess managerial applicants), we chose
estimates that most closely matched the predictor and
criterion relationships of interest. For example, when
meta-analytic estimates were reported by sample type,
we chose those values that were obtained in managerial
samples. Similarly, when values for different criteria
were reported, we chose those estimating the predictive validity for managerial performance or in high
complexity jobs (e.g., data from Hough, Ones, &
Viswesvaran, 1998, for managerial samples, rather
than other meta-analytic values available across jobs;
or data on high-complexity jobs from Hunter, 1980,
rather than results across job complexity levels). Appendix Table A2 provides a detailed description of all
sources, as well as the corrections that were applied
and the respective artifact distributions used.
lytic values, in order to provide a comparison using data
as obtained from an operational AC.
Data were gathered from AC evaluations of mid-level
managers and top-executives conducted for employment purposes2 over the course of 4 years (2000–
2004). Two different ACs were used to evaluate
managers depending on their current managerial level
or that of the job they were being considered for. The
primary dimensions for which scores were extracted
from the two ACs were identical for the two samples
and corresponded to those identified by Arthur et al.
(2003, see Appendix Table A1).
2.2. Primary data
2.2.2. Sample 2: Top-level managers
Managers that underwent the top-level managerial AC
(N ¼ 1923) were primarily from mid-level to top-executive management, had an average age of 44.3 years
(SD ¼ 6.4), and were mostly male (80.4%). Ethnicity
data were available for over 78% of these individuals,
92.6% of whom indicated White, 3.0% Black, 1.9%
Hispanic, 1.7% Asian, .03% American Indian, and .5%
‘Other.’ As expected, individuals in this sample on
average had an even higher level of education than
those in Sample 1, with 42.3% having obtained a
Bachelor’s degree, and 48.7% a Master’s or doctorate/
professional degree. The composition of the sample
was similar to Sample 1 with respect to industries
represented, although individuals in this sample tended
In addition to meta-analytic estimates, primary data
were employed for two purposes. First, as described
earlier, the relationships between Arthur et al.’s (2003)
primary AC dimensions and tests of personality and
cognitive ability have not been investigated to date. This
study constitutes the first such investigation by providing estimates obtained from two large primary samples
for AC dimensions/personality/cognitive ability intercorrelations. Additionally, data on the interrelationships
between personality variables and cognitive ability test
scores as well as among AC dimensions were obtained
from our two samples. These were used to conduct
parallel investigations to those informed by meta-ana-
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2.2.1. Sample 1: Mid-level managers
Managers that underwent the mid-level managerial AC
(N ¼ 3062) mostly came from first-line or mid-level
management, and were of an average age of 41.3 years
(SD ¼ 7.0) and predominantly male (75.6%). Data on
ethnic group membership were available for over 75%
of these individuals, 90.2% of whom indicated White,
3.8% Black, 3.5% Hispanic, 1.8% Asian, .02% American
Indian, and .5% ‘Other’ as their ethnic background.
Individuals in this sample were well educated, with
48.1% having obtained a Bachelor’s degree, and 36.5%
a Master’s or doctorate/professional degree. Assessees
came from over 30 diverse industries (including food
processing, electronics manufacturing, wholesale trade,
banking and finance, health care, agriculture, construction, government, and transportation), and mostly from
large organizations (over 80% from organizations with
more than 1000 members, median ¼ 11,000). Managers’ work experience at the time of assessment was
19.6 years on average (SD ¼ 7.6), in which individuals on
average worked for over three employers and had
managerial responsibility for a duration of 11.8 years
(SD ¼ 7.5). The number of employees managed by the
individuals in this sample was as high as 7500, with a
median of 18 (median number of direct reports ¼ 6).
Individuals received an average annual salary of over
US$147,000.
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
258
to come from larger organizations (median ¼ 20,000
members). The average number of years of work
experience at the time of assessment was 22.5
(SD ¼ 7.1), with an average of three employers and
managerial responsibility of 16.6 years (SD ¼ 7.0). The
number of employees managed by the predominantly
top-level managers in this sample was as high as 35,000,
with a median of 55. Individuals in this sample received
an average annual salary of more than US$309,000, with
over 10% of managers earning US$500,000 or more.
2.3. Measures used in primary data collection
2.3.1. ACs
Mid-level and top-level managers were evaluated in two
different ACs that assessed the same seven higherorder dimensions. The two ACs were designed based
on solid scientific principles and current research
evidence, and conducted by a consulting company
with extensive experience in assessment for selection
and development purposes (serving 75 of the Fortune
100 companies). Both ACs used background interviews,
an in-basket exercise, and three role-play exercises in
the form of a direct report meeting, a strategy presentation, and a task force meeting. Depending on the
exercise, individuals were assessed by one or multiple
trained and experienced assessors. Assessors evaluated
managers’ performance on a number of lower-order
competencies within each exercise. Ratings were provided on very detailed behaviorally anchored rating
scales. Across exercises in each AC, individual managers were rated on several hundred distinct behaviors
that were combined to derive competency scores.
Subsequently, AC primary dimension scores were
obtained by summing relevant competency scores
across AC exercises. The resulting seven primary
dimensions were communication, consideration/awareness of others, drive, influencing others, organizing and
planning, problem solving, and tolerance for stress/
uncertainty.
2.3.2. Cognitive ability
Cognitive ability was assessed using three different
psychometric tests: the Watson–Glaser Critical Thinking Appraisal (Watson & Glaser, 1980), the Wesman
Personnel Classification Test (Wesman, 1965), and the
Raven’s Progressive Matrices (Raven, Raven, & Court,
1998). All three inventories are widely used tests of
cognitive ability that see heavy use in personnel selection settings, and are supported by extensive evidence
demonstrating their construct validity as well as reliability. Individuals’ scores were transformed to standardized scores based on the normative information
available for each measure, and were subsequently
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
Stephan Dilchert and Deniz S. Ones
combined to obtain an overall score as an indicator of
general mental ability.
2.3.3. Personality
Personality was assessed using the Global Personality
Inventory (GPI; see ePredix, 2001), a personality inventory that was developed to assess the Big 5 personality factors emotional stability, extraversion, openness
to experience, agreeableness, and conscientiousness.
Facet scales that assess each of the Big 5 factors include,
among others, Emotional Control, Optimism, and
Stress Tolerance (for emotional stability), Competitiveness, Desire for Achievement, and Sociability (for
extraversion), Independence, Creativity, and Vision
(for openness), Consideration, Empathy, and Trust
(for agreeableness), and Attention to Detail, Dutifulness, and Responsibility (for conscientiousness). The
GPI was developed especially for use in employment
settings. Extensive evidence exists on its reliability and
construct validity across diverse jobs as well as from
countries around the world (Schmit, Kihm, & Robie,
2000).
2.4. Analyses
Three separate analyses were carried out to arrive at
different estimates for operational and incremental
validity. First, the meta-analytic data on predictor
intercorrelations and criterion-related validity were
used in combination with the primary data on AC
dimension–predictor relationships to obtain the best
estimate of operational validity by means of multiple
regression as well as composite correlations. These
analyses involved computations of operational validities
for AC dimensions as a set, AC dimensions when
combined with personality and cognitive ability, and
tests of personality and cognitive ability combined. The
process was repeated for an optimally weighted composite of AC dimensions as well as for a unit-weighted
composite. Unit-weighted estimates were obtained by
weighting all AC dimensions equally using the formula
for computing composite correlation (see Ghiselli,
Campbell, & Zedeck, 1981, chapter 7; also Nunnally,
1978, pp. 166–168), while optimally weighted estimates
were obtained using regression weights. In estimating
the joint validity of unit-weighted AC dimension composites and other predictors, Big 5 and cognitive ability
were optimally weighted based on their meta-analytic
regression weights. This procedure in turn required us
to first compute composite correlations among AC
dimensions with each individual differences predictor,
as well as multiple correlations between predictors and
the performance criteria, and then subject the resulting
correlation matrix to multiple regression analyses.
Results from these analyses were then used to compute
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AC Dimensions: Nomological Net and Incremental Validity
incremental validity of each predictor over all possible
predictor combinations.
Second, the same analyses were carried out for each
single AC dimension in order to evaluate whether
useful levels of incremental validity could be obtained
using only one, rather than all, AC dimensions. Third,
parallel analyses were carried out using primary data on
predictor intercorrelations and meta-analytic data for
operational validity in the sample of 3062 mid-level
managers (criterion information was not available in the
primary data). Finally, the same analyses were repeated
using the data obtained from the sample of 1923 toplevel managers.
Our approach is unique as it yields results that have
so far not been available in the literature: even though
the operational validity of primary AC dimensions has
previously been investigated, their relationships to
other predictors were unknown, and incremental validities for AC dimension score composites were thus
unavailable.
3. Results
3.1. AC dimension overlap with cognitive ability
and personality
The observed intercorrelations among AC dimensions,
Big 5 personality factors, and cognitive ability are
presented in Table 1. Again, the data presented here
are the first estimates of this kind, relating the overarching AC dimensions identified by Arthur et al. (2003)
to individual differences in personality and cognitive
ability. Correlations from the mid-level and top-manager samples are presented separately as well as
combined (sample size weighted). For comparative
purposes, the correlations between cognitive ability
and the Big 5 with overall AC ratings from Collins et
al. (2003) and Scholz and Schuler (1993) are also given
in Table 1.
The correlational pattern was remarkably similar
across the two samples of managers, and hence only
the results for the combined sample are discussed here.
The pattern of relationships between AC dimensions,
cognitive ability, and the Big 5 was very compatible with
the definitions of the seven AC dimensions offered by
Arthur et al. (2003, see Appendix Table A1). As
expected, the AC dimensions ‘problem solving’ displayed the strongest relationship with cognitive ability
(r ¼.32) and the personality trait openness to experience (r ¼.18). This pattern is intuitively appealing, as
problem solving in ACs has been postulated to describe
the ability to analyze and reason with information, as
well as the ability to generate ideas and imaginative
solutions (Arthur et al., 2003). Also as expected,
problem solving was the only one of the seven AC
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259
dimensions that displayed a notable relationship to
cognitive ability. Other AC dimensions displayed sizable
relationships with personality traits, however, and
mostly confirmed our expectations.
‘Organizing and planning’ related most strongly to the
personality domain of conscientiousness (r ¼.24). This
finding is not surprising – orderliness has been postulated as a facet of conscientiousness in many personality
taxonomies and has also been empirically established as
such (Roberts, Chernyshenko, Stark, & Goldberg,
2005). The AC dimension ‘influencing others,’ which
we expected to relate mostly to extraversion, was
found to relate to several of the Big 5 personality
domains. It indeed displayed a sizable relationship with
extraversion (observed r ¼.27), but also related to
agreeableness (r ¼.27) and emotional stability
(r ¼.24). Influencing others describes an individual’s
persuasiveness and surgency. Indeed, most of the
lower-order competencies that Arthur et al. (2003)
categorized into this primary dimension relate to
leadership skills. This was also the case in the present
two samples, where many of the lower-order competencies that were included in this dimension related to
leading and developing others. Thus, the pattern of
relationships with the Big 5 factor of extraversion, but
also emotional stability and agreeableness, is in line with
the broad leadership literature. These three personality
traits relate to leadership effectiveness in general
(Judge, Bono, Ilies, & Gerhardt, 2002) as well as
transformational leadership in particular (Bono & Judge,
2004).
‘Consideration/awareness of others’ mostly reflects
social skills and teamwork-related competencies. Appropriately, this AC dimensions related most strongly
to the Big 5 domain of agreeableness (r ¼.27). However, it also reflected individuals’ emotional stability to
some extent (r ¼.20). ‘Communication’ was the only
AC dimension that displayed virtually no notable relationship with any of the individual differences scales
included in this study. The strongest relationship was
found to exist between this AC dimension and agreeableness; however, the relationship was quite weak
(r ¼.08). Unlike communication, the dimension of
‘drive’ was found to relate to all personality scales.
The multitude of relationships as well as their strengths
were greater than expected; based on the construct
definition this dimension was conceptually only associated with high extraversion and high conscientiousness. While drive related most strongly to extraversion
(r ¼.48), it also correlated notably with the remaining
Big 5 traits (r ¼.31, .32, .26, and .31 with emotional
stability, openness, agreeableness, and conscientiousness, respectively). Post hoc, we can make sense of these
relationships with the Big 5 by examining the lowerorder competencies that define drive as a primary AC
dimension. The taxonomy established by Arthur et al.
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
260
Stephan Dilchert and Deniz S. Ones
Table 1. Correlations primary assessment center dimensions with cognitive ability and personality
Dimension
g
ES
r
N
r
Sample 1: Mid-level managers
Problem solving
.34
2995 .09
Organizing and planning
.02
2995 .16
Influencing others
.02
2995 .22
Consideration of others
.06
2994 .20
Communication
.03
2995 .06
Drive
.09
2995 .34
Tolerance of stress
.02
2994 .49
Unit-weighted composite
.07
2995 .26
Sample 2: Top-level managers
Problem solving
.28
1861 .08
Organizing and planning
.10
1861 .11
Influencing others
.04
1860 .26
Consideration of others
.02
1861 .21
Communication
.09
1859 .02
Drive
.03
1861 .25
Tolerance of stress
.03
1861 .52
Unit-weighted composite
.13
1878 .20
Both samples combined (sample size weighted)
Problem solving
.32
4856 .09
Organizing and planning
.05
4856 .14
Influencing others
.03
4855 .24
Consideration of others
.03
4855 .20
Communication
.05
4854 .04
Drive
.04
4856 .31
Tolerance of stress
.02
4855 .50
Unit-weighted composite
.09
4873 .24
Overall AC ratingsa
.43b
5419
.26b
E
N
O
r
N
r
A
C
N
r
N
r
N
2924
2924
2924
2923
2924
2924
2924
2924
.07
.17
.25
.16
.02
.50
.22
.28
2924
2924
2924
2923
2924
2924
2924
2924
.18
.16
.17
.07
.01
.34
.10
.23
2924
2924
2924
2923
2924
2924
2924
2924
.06
.15
.26
.25
.10
.29
.22
.26
2,924
2924
2924
2923
2924
2924
2924
2924
.07
.27
.09
.08
.05
.35
.11
.16
2924
2924
2924
2923
2924
2924
2924
2924
1854
1854
1853
1854
1852
1854
1854
1854
.17
.17
.29
.16
.02
.44
.50
.24
1854
1854
1853
1854
1852
1854
1854
1854
.19
.11
.21
.05
.03
.29
.29
.17
1854
1854
1853
1854
1852
1854
1854
1854
.04
.07
.28
.29
.06
.20
.27
.18
1854
1854
1853
1854
1852
1854
1854
1854
.03
.20
.11
.06
.06
.25
.25
.13
1854
1854
1853
1854
1852
1854
1854
1,854
4778
4778
4777
4777
4776
4778
4778
4778
.11
.17
.27
.16
.00
.48
.33
.26
4,778
4,778
4777
4777
4776
4778
4778
4778
.18
.14
.19
.06
.01
.32
.17
.21
4778
4,778
4777
4777
4776
4778
4778
4778
.05
.12
.27
.27
.08
.26
.24
.23
4778
4778
4777
4777
4776
4778
4778
4778
.05
.24
.10
.07
.05
.31
.16
.15
4778
4778
4777
4777
4776
4778
4778
4778
1023
.36b
1847
.12b
830
.18b
619
.14c
1107
Note: The unit-weighted composite is a sum of the first six dimensions only, in order to provide data consistent with the computations of
operational validities, which were only available for these six dimensions from Arthur et al. (2003). aMeta-analytic values for overall AC ratings are
presented for comparison purposes. The respective values were obtained from the most recent meta-analytic summaries. For relationships where
estimates were not available in the most recent quantitative review, prior meta-analytic work was consulted. bSample size weighted mean r from
Collins et al. (2003). cSample size weighted mean r for conscientiousness and achievement motivation (weighted by total N) from Scholz and
Schuler (1993). g, general mental ability (overall score on three cognitive ability tests); ES, emotional stability; E, extraversion; O, openness;
A, agreeableness; C, conscientiousness.
(2003) lists a number of diverse characteristics such as
aggressiveness, perseverance, and initiative as belonging
to this category. Thus, it is not surprising that drive
would also be related to several of the Big 5 personality
traits. Lastly, confirming our expectations, ‘tolerance
for stress’ exhibited its strongest relationship with
emotional stability (r ¼.50) but was also related to
extraversion, albeit to a lesser extent (r ¼.33).
These results offer a unique contribution to the
literature and now provide the basis for a better
understanding of the construct underpinnings of AC
dimensions. It is important to note that for the purpose
of this study, observed (uncorrected) estimates of
predictor intercorrelations were of interest. If one
were to correct the values presented in Table 1 for
attenuation due to unreliability in both types of measures using mean reliabilities from meta-analyses and
reliability generalization studies (Connelly, Ones, Ramesh, & Goff, 2008; Hülsheger, Maier, & Stumpp,
2007a, b; Viswesvaran & Ones, 2000), one would obtain
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
estimates of construct-level relationships that would be
higher than those observed on the measure-level. That
is, the relationships between AC dimensions and Big 5
personality traits would on average be 35% larger, while
relationships between AC dimensions and cognitive
ability would be on average 26% larger than observed
values. However, given the applied focus of this study to
understand the relative usefulness of AC dimension visà-vis individual differences measures used in personnel
selection, our focus on observed predictor interrelationships is appropriate.
3.2. Intercorrelations among AC dimensions
Observed intercorrelations among AC dimensions
from our two samples are presented in Table 2. Results
show that the seven dimensions were moderately
related (r ¼ :33 and .34 in the mid-level and top-level
manager samples, respectively). These values are no-
& 2009 Blackwell Publishing Ltd.
AC Dimensions: Nomological Net and Incremental Validity
261
Table 2. Intercorrelations among primary assessment center dimensions in Samples 1 and 2
Dimension
1. Problem solving
n
2. Organizing and planning
n
3. Influencing others
n
4. Consideration/awareness of others
n
5. Communication
n
6. Drive
n
7. Tolerance of stress/uncertainty
n
1
.42
3062
.46
3062
.27
3061
.33
3062
.25
3062
.23
3061
2
3
4
5
6
7
.50
1923
.41
1922
.42
1922
.20
1923
.17
1923
.53
1922
.25
1921
.21
1921
.44
1920
.63
1921
.40
1923
.40
1923
.44
1922
.15
1923
.12
1921
.28
1922
.27
1922
.41
1921
.27
1922
.17
1920
.46
1922
.45
3062
.26
3061
.27
3062
.40
3062
.20
3061
.52
3061
.45
3062
.38
3062
.33
3061
.60
3061
.27
3061
.31
3060
.14
3062
.20
3061
.25
3061
Notes: Values below diagonal obtained from Sample 1 (mid-level manager assessment center); values above diagonal from Sample 2 (top-level
manager assessment center).
ticeably lower than those reported in the quantitative
summary of Arthur et al. (2003), where the average
sample size weighted mean r among the seven primary
AC dimensions was reported as .52. The lower average
correlation between the AC dimensions in the primary
data may lead to higher estimates of AC validity once
dimension composite validities are estimated. One
should note that the sample sizes for many of the AC
dimension intercorrelations obtained from the primary
data in this study are much larger than the cumulated
sample sizes for the meta-analytic values reported in
Arthur et al. (2003). For nine out of the 15 correlations,
the sample sizes in this study exceed those of Arthur
and colleagues; for eight of these nine correlations, the
sample sizes were more than twice as large as the metaanalytic Ns. Although meta-analytic data have advantages over single sample investigations, well-conducted,
large-scale primary data can add significant value,
especially in cases like the one at hand, where the
meta-analytic investigation does not include much of
the unpublished work.
3.3. Validity of AC dimensions
3.3.1. Operational validity
As described in the analyses section, meta-analytic
validities for the six AC dimensions communication,
consideration/awareness of others, drive, influencing
others, organizing and planning, and problem solving
were obtained from Arthur et al. (2003). These estimates, together with observed intercorrelations among
AC dimension scores (both meta-analytic and from
primary data), were used to obtain multiple correlations between AC dimensions and overall job performance. The resulting multiple correlations estimate the
validity that can be expected from composites of these
& 2009 Blackwell Publishing Ltd.
six AC dimensions. To this end, we computed validities
for both unit-weighted and optimally weighted composites of AC dimensions. These validity estimates are
presented in Table 3.
The operational validity estimates computed for AC
dimension composites based on meta-analytic intercorrelations among the primary dimensions were nearly
identical (.44 for unit-weighted and .45 for optimally
weighted composites). The same was the case for
validity estimates computed based on AC dimension
intercorrelations obtained from primary data. However, as expected, these validities were slightly higher
due to the lower intercorrelations of AC dimensions in
the primary data. For the mid-level manager sample, the
values were .49 and .52, and for the top-level manager
sample .50 and .51 (unit- and optimally weighted,
respectively). In Table 3, the validity of overall AC
ratings from a prior meta-analysis is presented for
comparative purposes (r ¼ .36; Gaugler et al., 1987).
Recall that AC dimension composites that we computed were derived entirely mechanically. Thus, we
observe that both unit- and optimally weighted, mechanically combined AC dimension composites yield
validity estimates well above those for overall AC
ratings, which are often obtained, at least in part, using
subjective information combination on the part of
assessors.
3.3.2. Incremental validity of AC dimensions over personality and cognitive ability
Using the information obtained from this study together with meta-analytically derived predictor intercorrelations, we formed predictor composites of AC
dimensions (unit- and optimally weighted), personality
test scores, and cognitive ability. The resulting validity
estimates were substantial. Adding the primary AC
dimensions (unit-weighted) as a set to the Big 5 factors
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
262
Stephan Dilchert and Deniz S. Ones
Table 3. Incremental validity of assessment center dimensions over psychometric tests for predicting job performance
Overall AC ratings
(incl. subjective combination)
AC dimensions
(unit-weighted)
AC dimensions
(optimally weighted)
.36
.44
.45
.12
.02
.00
.17
.12
.09
.27
.15
.12
.49
.52
.26
.16
.11
.31
.19
.13
.50
.51
.27
.14
.10
.30
.15
.12
Meta-analytic estimates
Operational validity
Incremental validity over . . .
Big 5
Cognitive ability
Cognitive ability þ Big 5
Mid-level manager sample (N ¼ 3062)
Operational validity
Incremental validity over . . .
Big 5
Cognitive ability
Cognitive ability þ Big 5
Top-level manager sample (N ¼ 1923)
Operational validity
Incremental validity over . . .
Big 5
Cognitive ability
Cognitive ability þ Big 5
Note: Computations were based on operational validity estimates and observed predictor intercorrelations from sources listed in Appendix
Table A2.
of personality yields criterion-related validity estimates
of .47 for the prediction of job performance (based on
meta-analytic predictor intercorrelations), while adding
them to measures of cognitive ability increases validity
estimates to .68. Combining all three types of predictors available yields an estimate of operational validity
of .71. Optimally weighted AC dimension composites
performed even slightly better. Similar results were
obtained when substituting meta-analytic predictor
intercorrelations with those obtained in the primary
samples. The detailed computations of operational
validities of predictor combinations can be found in
Appendix Tables A3–A5 for the meta-analytic and
primary samples, respectively. Next, we focus our
discussion on incremental validity results.
Incremental validity estimates of AC dimensions over
personality and cognitive ability were computed based
on the multiple correlations reported above; these
results are also reported in Table 3. While the incremental validity that can be expected from overall AC
ratings over personality measures and tests of cognitive
ability has already been reported elsewhere (incremental validity over personality being substantial, see Goffin,
Rothstein, & Johnston, 1996; whereas the incremental
validity over cognitive ability is negligible, see Hardison,
2005; Schmidt & Hunter, 1998), the estimates of
relative incremental value offered by AC dimensions
and different ways of combining them are unique to the
present study. Results show that mechanically (unit- or
optimally) weighted composites of AC dimensions
provide incremental validity over tests of cognitive
ability and personality. Incremental validity estimates
from meta-analytic data were .17 and .27 for unit- and
optimally weighted AC dimension composites over
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
tests of personality. The incremental validity of AC
dimensions over tests of cognitive ability was estimated
at .12/.15 (unit-/optimally weighted). Finally, the incremental validity over both types of individual differences
predictors was found to be .09/.12. This is in contrast
to the findings for overall AC ratings, which provide no
incremental validity over tests of personality and cognitive ability. That is, with regard to operational and
incremental validity, both unit- and optimally weighted
AC dimension composites fare much better than overall AC ratings (which are often derived by subjectively
combining information across AC dimensions or making holistic judgments).
It is encouraging that the estimates obtained from
primary data were very similar. The estimates for
operational validities of AC dimension composites
were only slightly higher due to the lower average
intercorrelations among dimensions, and the overall
conclusions regarding incremental validity remain unchanged. The incremental validity estimates from our
primary data were .11/.13 and .10/.12 for both types of
AC composites in the mid-level and top-manager
samples, respectively. These values are perfectly in
line with the estimate of .09/.12 obtained from the
meta-analytically based data.
3.3.3. Incremental validity of single AC dimensions
The incremental validity that can be expected from six
of the seven primary AC dimensions is high enough to
consider ACs useful tools for evaluating managerial
candidates. However, the question remains whether
similar incremental validity for performance prediction
can be achieved using only one of the seven AC
dimensions. This issue is particularly interesting from
& 2009 Blackwell Publishing Ltd.
AC Dimensions: Nomological Net and Incremental Validity
263
Table 4. Incremental validity of each assessment center dimension over psychometric tests for predicting job performance
AC dimensions
Problem
solving
Operational validity
.39
Meta-analytic estimates
Incremental validity over . . .
Big 5
.19
Cognitive ability
.04
Cognitive ability þ Big 5
.04
Mid-level manager sample (N ¼ 3062)
Incremental validity over . . .
Big 5
.24
Cognitive ability
.04
Cognitive ability þ Big 5
.04
Top-level manager sample (N ¼ 1923)
Incremental validity over . . .
Big 5
.22
Cognitive ability
.05
Cognitive ability þ Big 5
.05
Organizing and
planning
Influencing
others
Consideration/
awareness
Communication
Drive
.37
.38
.25
.33
.31
.12
.10
.07
.14
.11
.09
.07
.06
.04
.16
.08
.07
.05
.09
.07
.15
.11
.06
.19
.11
.08
.09
.07
.05
.17
.08
.08
.09
.11
.05
.15
.08
.05
.19
.10
.09
.10
.05
.04
.17
.07
.07
.10
.07
.03
Note: Computations were based on operational validity estimates and observed predictor intercorrelations from sources listed in Appendix Table A2.
an applied perspective. Organizations that are already
employing a specific combination of predictor measures
may be interested in adding only AC dimensions that
supplement their current tools in the most optimal yet
efficient manner. The results of our analyses that speak
to this issue are presented in Table 4.
Naturally, which specific dimension offers the highest
incremental value depends on the combination of
predictors already being employed for a given criterion.
If one were looking to supplement a test of the Big 5
domains of personality in performance prediction by
assessing only a single AC dimension, the best choice
with regard to incremental validity would be problem
solving. This comes as no surprise when considering that
this AC dimension is by far the most cognitively loaded
of the seven primary dimensions (sample size weighted
mean observed r in the present two samples ¼ .32).
Because of its high operational validity ( ¼ .39; Arthur
et al., 2003) and universally small overlap with the Big 5
domains of personality (mean r in the present two
samples ¼ .08), this AC domain increments validity by
.19 correlational points when added to the Big 5.
Conversely, when looking to increment the validity of
tests of cognitive ability, the best choice is the AC
dimension influencing others. This leadership related
dimension displayed low overlap with cognitive ability
scores in our two samples (mean r ¼.03). Because of its
high operational validity ( ¼ .38), this dimension would
add .11 in incremental validity when added to tests of
cognitive ability. This dimension also remains the best
choice when tests of cognitive ability and personality
are combined with only one AC dimension; the
incremental validity estimate is .09 as obtained from
meta-analytic data and primary samples. This certainly
compares quite favorably with the incremental validity
& 2009 Blackwell Publishing Ltd.
that would be obtained if all AC dimensions were added
to the equation; that meta-analytic estimate was only
slightly higher (.09/.12; see Table 3).
3.3.4. Incremental validity of personality and cognitive
ability over AC scores
While the focus of the present investigation was on the
incremental validity of ACs over tests of personality and
cognitive ability, we provide a parallel investigation for
the value that such tests offer over overall AC ratings
and dimension composites. These results are presented
in Table 5; again, details on the computations can be
found in Appendix Tables A3–A5.
The operational validity of the Big 5 as a set, cognitive
ability, and the Big 5 and cognitive ability combined
were estimated at .29, .56, and .62 based on the
available meta-analytic evidence. Incremental validities
were computed based on the correlations with AC
dimensions obtained from this study and the metaanalytic correlations with overall AC ratings from
Collins et al. (2003) and Scholz and Schuler (1993).
Results show that cognitive ability and the Big 5 as a set
add substantial value over all types of AC ratings. The
combined incremental validity of these two predictors
over unit- and optimally weighted AC dimension composites was .28, respectively. Similar estimates were
obtained from the data based on the two primary
samples; the respective estimates were .28 (mid-level
manager sample) and .26 (top-level manager sample).
4. Discussion
This study provided the first investigation of the
nomological net of the seven primary AC dimensions
International Journal of Selection and Assessment
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264
Stephan Dilchert and Deniz S. Ones
Table 5. Incremental validity of psychometric tests over assessment center dimensions for predicting job performance
Meta-analytic estimates
Operational validity
Incremental validity over . . .
Overall AC ratings
AC dimensions (unit-weighted)
AC dimensions (optimally weighted)
Mid-level manager sample (N ¼ 3062)
Operational validity
Incremental validity over . . .
AC dimensions (unit-weighted)
AC dimensions (optimally weighted)
Top-level manager sample (N ¼ 1923)
Operational validity
Incremental validity over . . .
AC dimensions (unit-weighted)
AC dimensions (optimally weighted)
Big 5
Cognitive
ability
Big 5 and
cognitive ability
.29
.56
.62
.05
.03
.11
.22
.25
.25
.26
.27
.29
.26
.56
.67
.03
.06
.23
.23
.28
.28
.26
.56
.66
.03
.05
.20
.20
.26
.26
Note: Computations were based on operational validity estimates and observed predictor intercorrelations from sources listed in Appendix
Table A2.
established by Arthur et al. (2003) by assessing their
overlap with tests of cognitive ability and personality.
The data on these relationships were used in conjunction with prior meta-analytic data on the validity of
overall AC ratings, AC dimensions, personality, and
cognitive ability, in order to evaluate the relative
incremental value that AC dimension scores add to
tests of personality and cognitive ability.
ACs can increase the applied utility of managerial
staffing systems by offering information about individuals that supplements data already provided by standardized psychometric tests. Previously, investigations
of incremental validity were only available for overall
AC ratings, resulting in low to negligible estimates. As
this study has shown, a composite of dimension scores
offers useful levels of incremental validity when combined with scores of cognitive ability and personality
tests (DR ¼ .12 when optimally weighted). However,
incrementing utility in such a way requires making use of
information on AC dimensions by mechanical combination of primary dimensions rather than using overall AC
ratings. The incremental validity of overall AC ratings
over tests of personality plus cognitive ability was found
to be .00.
A major factor that hampers the criterion-related
validity of overall AC ratings in operational settings is
the fact that most often scores are combined in a nonoptimal fashion across raters, exercises, and dimensions. Such non-optimal approaches include assigning
overall scores based on clinical data combination or
discussion among raters (see Grove & Meehl, 1996;
Grove, Zald, Lebow, Snitz, & Nelson, 2000; Meehl,
1954). Previous research has shown that mechanically
combining AC ratings is superior to judgmentally
combining assessor ratings (see, e.g., Feltham, 1988).
This research shows that this is also the case when it
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
comes to incremental validity. The mode of data
combination does not seem to matter much in relation
to validity, as long as it is mechanical. The operational
and incremental validity estimates established in this
study were not appreciably different for unit- and
optimally weighted AC dimension composites. This
was the case for our investigations based on metaanalytic as well as primary data.
From an applied perspective, this study yields an
interesting conclusion with regard to whether all AC
dimensions need to be assessed in order to achieve
good incremental validity. Given the fact that ACs are
costly tools and also time consuming to administer (cf.
Collins et al., 2003), the question of whether useful
levels of incremental validity can be achieved with fewer
dimensions is worth some consideration. To this end,
we conducted parallel analyses estimating the incremental validity of each AC dimension over the Big 5,
cognitive ability, and both predictor types combined.
The present set of results (from both meta-analytic and
primary data) yield a straightforward answer that may
be of high utility in applied settings: useful levels of
incremental validity can be obtained even when using
only one AC dimension. Practitioners looking to design
and implement slimmed-down ACs concentrating on
only a few dimensions should concentrate on assessing
problem solving and influencing others. These dimensions
offer the largest incremental value when added to tests
of cognitive ability, personality, or both. The challenge in
designing such ACs, however, lies in designing exercises
targeted at only a few dimensions, as well as in training
assessors to capture and rate only assessee behavior
relevant to the specific dimension(s) identified to add
incremental validity. It is conceivable that a slimmeddown AC designed to measure only a single AC
dimension instead results in assessors providing an
& 2009 Blackwell Publishing Ltd.
AC Dimensions: Nomological Net and Incremental Validity
overall evaluation, or at least an AC dimension rating
contaminated by an overall impression of the candidate.
Such scores would likely function similarly to overall
AC ratings that are derived using subjective data
combination across several dimensions. However, in
well-designed and implemented ACs, significant resources are typically invested in training assessors and
providing them with scientifically sound guidelines and
tools for evaluating behaviors observed among candidates. Thus, we are optimistic that such slimmed-down,
one- or two-dimension ACs can be developed and
implemented for organizations looking to supplement
their existing assessment tools. However, because
these ACs would put a strong emphasis on maximizing
overall validity, they may not be suited to provide
developmental feedback. It also remains to be seen
whether such slimmed-down ACs really differ from
other selection tools that include simulation-type assessments, such as work sample tests, for instance.
Nonetheless, validity estimates for single AC dimensions as well as composites of AC primary dimensions
are encouraging. This research has shown that previous
estimates of the operational and incremental validity
estimates were hampered by the fact that only overall
AC ratings were considered, rather than scores on
meaningful AC dimensions. We proposed that overall
AC scores do not add incremental validity due to the
fact that they are often obtained in a non-optimal
fashion (typically using subjective means of data combination). Yet, another major reason is that overall AC
scores are often not aligned with other tools that are
already being administered. Additional measures increase the overall reliability of a predictor battery and
will thus increase overall validity. However, in addition
to increasing overall reliability of an assessment battery,
construct-based AC dimensions can also add construct
coverage. For predictive purposes, the more indicators
of a given construct we can administer, the better. Each
measure will conceptualize the construct domain slightly
differently (after all, this study also showed that AC
dimensions are not correlated perfectly even with tests
of conceptually related individual differences traits). We
now know that the effects of increased construct
coverage can go beyond those of simply increasing
reliability. Connelly and Ones (2007) showed that adding
a test of conscientiousness to another test of conscientiousness can result in large increases in overall validity,
simply because the coverage of the predictor construct
increases (albeit the overall validity asymptotes to a
certain level after a certain number of scales). They
found that this was even the case on the facet level,
where construct definitions were identical. Similarly,
construct-based AC dimensions provide the opportunity to explain variance in job performance with the help
of predictor constructs not yet well represented or
assessed incompletely in a given test battery.
& 2009 Blackwell Publishing Ltd.
265
As discussed above, we can also add AC dimensions
that measure constructs not at all represented in
certain predictor batteries. For example, if an organization only uses ability and experience-related constructs
to identify suitable candidates, an AC that places a
strong emphasis on ‘drive’ has the potential to add
incremental validity. We know from the I/O psychology
literature that this is not the case for overall scores
(Schmidt & Hunter, 1998), possibly due to their high
correlation with cognitive ability (Collins et al., 2003).
Thus, there is a need to employ construct-based
dimensions that clearly tease predictor constructs apart
and show little overlap with exactly those predictors
already in use. Again, an emphasis on constructs assessed
in ACs, rather than on scores for the overall method,
provides a more fruitful avenue for investigations of
incremental validity.
In light of the long-lasting controversy on AC construct validity (for a recent exchange, see Lance, 2008;
Connelly et al., 2008, and other replies published in
Industrial and Organizational Psychology: Perspectives on
Science and Practice, 1, 1), one might ask the question
whether similarly encouraging results of incremental
validity would be obtained for individual AC exercise
scores than those we established here for AC dimensions. While such question has merit, an investigation of
AC exercise score validity over individual differences
traits would again constitute an investigation of methods
(AC exercises regardless of dimension assessed) vs
constructs (e.g., personality or GMA). We are convinced
that the potential for incremental validity of AC dimensions as established in our research is based on additional predictor construct coverage, and the
opportunity to add individual AC dimensions that
assess constructs not yet well assessed by other predictors. It is possible that exercise scores that summarize performance across several predictor constructs
function similarly to overall AC ratings. Nonetheless,
we see some potential for incremental validity of AC
exercise scores when they are combined mechanically
such as in unit-weighted composites (compared with
previous investigations of overall AC ratings that often
include subjective methods of data combination). Unfortunately, investigations of AC exercise validity parallel to the one presented here for AC dimensions are
currently impossible – the literature is lacking good
validity estimates of AC exercises in predicting job
performance. We could only locate a single sample of
359 candidates for first-line supervisor positions (Study
2 in Lance, Newbolt, Gatewood, Foster, French, &
Smith, 2000) that contains correlations between AC
exercise factors and a job performance criterion. Even
investigations of AC exercise validity for distal performance outcomes such as salary are extremely rare (see
Lievens, Dilchert, & Ones, in press). In this study, we
were fortunate enough to be able to rely on meta-
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
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Stephan Dilchert and Deniz S. Ones
analytic evidence on AC dimension criterion-related
validity; our field should take strides to accumulate
similar data on the usefulness of AC exercises as an
additional step toward resolving the AC construct
validity question.
Notes
1.
2.
Predictor intercorrelations should not be corrected for
attenuation due to unreliability, because in estimating
operational validities we are interested in the predictive
efficiency of these measures as they are used in operational settings, recognizing that scores are subject to
measurement error. Estimating operational validity in this
way is standard procedure in personnel selection research, and the same procedure is often applied in metaanalyses of criterion-related validities of popular predictors (Kuncel, Hezlett, & Ones, 2001; Ones, Dilchert,
Viswesvaran, & Judge, 2007; Ones, Viswesvaran, &
Schmidt, 1993; Schmidt & Hunter, 1998).
Assessment centers were conducted both for developmental as well as selection purposes. However, the
predictor intercorrelations contributing to our analyses
were very similar for developmental and selection ACs.
Thus, overall findings are virtually identical across assessment purposes. For reasons of brevity, we present only
results for the combined analysis.
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Appendix
Table A1. Primary assessment center dimensions assessed in
the current study and corresponding definitions from Arthur
et al. (2003)
Dimension and definition
Communication
‘The extent to which an individual conveys oral and written
information and responds to questions and challenges’
Consideration/awareness of others
‘The extent to which an individual’s actions reflect a consideration for the feelings and needs of others as well as an
awareness of the impact and implications of decisions relevant
to other components both inside and outside the organization’
Drive
‘The extent to which an individual originates and maintains a
high activity level, sets high performance standards and
persists in their achievement, and expresses the desire to
advance to higher job levels’
Influencing others
‘The extent to which an individual persuades others to do
something or adopt a point of view in order to produce
desired results and takes action in which the dominant
influence is one’s own convictions rather than the influence
of other’s opinions’
Organizing and planning
‘The extent to which an individual systematically arranges his/
her own work and resources as well as that of others for
efficient task accomplishment; and the extent to which an
individual anticipates and prepares for the future’
Problem solving
‘The extent to which an individual gathers information; understands relevant technical and professional information; effectively analyzes data and information; generates viable options,
ideas, and solutions; selects supportable courses of action for
problems and solutions; uses available resources in new ways;
and generates and recognizes imaginative solutions’
Tolerance of stress/uncertainty
‘The extent to which an individual maintains effectiveness in
diverse situations under varying degrees of pressure, opposition and disappointment’
Note: Adapted from Arthur et al. (2003).
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
268
Stephan Dilchert and Deniz S. Ones
Table A2. Sources of primary and meta-analytic estimates of predictor interrelationships and operational validities
Relationship
Predictor interrelationships
AC dimensions
–intercorrelations
AC dimensions–Big 5
AC dimensions
–cognitive ability
Big 5
–cognitive ability
Source
Type of estimate used
Arthur et al. (2003), as well as
primary data from this study
Primary data from this study,
Samples 1 and 2 combined
(N ¼ 4778)
Primary data from this study,
Samples 1 and 2 combined
(N ¼ 4873)
Ackerman and Heggestad
(1997)
Sample size weighted mean r
Sample size weighted mean r
No meta-analytic estimate
available in the literature
Sample size weighted mean r
No meta-analytic estimate
available in the literature
Values for 4 of the Big 5
were directly available.
The estimate for ‘wellbeing’ was substituted for
emotional stability, which
was unavailable
Sample size weighted
mean of estimates for global conscientiousness and
achievement motivation
Big 5
–intercorrelations
Ones (1993); also reported in
Ones, Viswesvaran, and Reiss
(1996)
Overall AC ratings
–Big 5 (except
conscientiousness)
Overall AC ratings
–conscientiousness
Collins et al. (2003)
True score correlations attenuated using meta-analytic reliability estimates (obtained
from Viswesvaran & Ones,
2000, for Big 5 and Hülsheger
et al., 2007 for cognitive ability)
True score correlations attenuated using meta-analytic reliability estimates (obtained
from Ones and Viswesvaran,
2000)
Sample size weighted mean r
Scholz and Schuler (1993)
Sample size weighted mean r
Overall AC ratings
–cognitive ability
Operational validities
AC dimensions
–job performance
Overall AC ratings
–job performance
Cognitive ability
–job performance
Collins et al. (2003)
Sample size weighted mean r
Arthur et al. (2003)
Operational validity
Gaugler et al. (1987)
Operational validity
Hunter (1980)
Operational validity
Hough et al. (1998)
Operational validity
Big 5
–job performance
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
Comment
Estimate for ‘tolerance for
stress’ unavailable
Validity for performance
criteria
Criterion: Overall job performance in high complexity jobs
Criterion: Managerial performance
& 2009 Blackwell Publishing Ltd.
& 2009 Blackwell Publishing Ltd.
R w/Big 5
.62c
over . . .
g and AC
.03
R w/Big 5
.47i
Big 5
.17
Big 5
.27
g and Big 5
.12
R w/Big 5 R w/g R w/Big 5 and g
.57i
.71j
.74k
Dimensions optimally weighted
g and Big 5 g
.09
.15
R w/g R w/Big 5 and g R
.68j
.71k
.45h
Big 5 g and Big 5 g
.12
.00
.12
R w/Big 5 and g R
.62g
.44h
Overall rating (incl. subjective com- Dimensions unit-weighted
bination)
Assessment center ratings
Big 5 and AC g
.24
.02
R w/Big 5 R w/g
.41e
.58f
Big 5 AC
.33
.25
r
.36d
Cognitive ability
R w/Big 5
.67c
Big 5
.40
r
.56b
g and AC
.05
AC
.23
R
.49d
Cognitive ability
Big 5 and AC
.22
R w/Big 5
.52e
g
.16
R w/g
.72f
Big 5
.26
R w/Big 5 and g
.78g
Dimensions unit-weighted
Assessment center ratings
g and Big 5
.11
R
.52d
g
.19
R w/Big 5
.57e
Big 5
.31
R w/g
.75f
g and Big 5
.13
R w/Big 5 and g
.80g
Dimensions optimally weighted
Notes: All computations based on estimates of predictor interrelationships obtained from the present sample and estimates of operational validity obtained from the meta-analytic literature (see Table A2 for
sources). aBased on Big 5 intercorrelations from this sample and Big 5 operational validities from Hough et al. (1998). bFrom Hunter (1980). cBased on a, b, and Big 5–cognitive ability correlation from this sample.
d
Based on operational validities of AC dimensions from Arthur et al. (2003) and AC dimension intercorrelations from this sample. eBased on d and AC dimension–Big 5 correlations from this sample. fBased on d
and AC dimension–cognitive ability correlations from this sample. gBased on sources for d, e, and f. g, general mental ability (overall score on three cognitive ability tests); Big 5, Big 5 dimensions of personality;
AC, unit-weighted composite of primary assessment center dimensions.
Operational validity
R
.26a
Incremental validity over . . .
g
AC
.11
.03
Personality
Big 5
Table A4. Computation of operational and incremental validity: mid-level manager sample (N ¼ 3062)
Notes: All computations based on meta-analytic estimates of operational validity and predictor interrelationships, except for the correlations of AC dimensions with cognitive ability and the Big 5, which had to be
obtained from the current two samples. aBased on Big 5 intercorrelations from Ones (1993) and Big 5 operational validities from Hough et al. (1998). bFrom Hunter (1980). cBased on a, b, and Big 5–cognitive
ability correlation from Ackerman and Heggestad (1997). dFrom Gaugler et al. (1987). eBased on a, d, and overall AC–Big 5 correlations from Collins et al. (2003) and Scholz and Schuler (1993). fBased on b, d, and
overall AC–cognitive ability correlation from Collins et al. (2003). gBased on sources for a, b, c, d, e, and f. hBased on operational validities and intercorrelations of AC dimensions from Arthur et al. (2003). iBased
on h and AC dimension–Big 5 correlations from the present two samples. jBased on h and AC dimension–cognitive ability correlations from the present two samples. kBased on sources for h, i, and j. g, general
mental ability (overall score on three cognitive ability tests); Big 5, Big 5 dimensions of personality; AC, unit-weighted composite of primary assessment center dimensions.
Operational validity
R
r
.56b
.29a
Incremental validity
g
AC
.06
.03
Personality
Big 5
Table A3. Computation of operational and incremental validity: meta-analytic data
AC Dimensions: Nomological Net and Incremental Validity
269
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
R
.50d
AC
.20
R w/Big 5
.66c
Big 5
.39
Cognitive ability
Big 5 and AC
.23
R w/Big 5
.53e
g
.14
R w/g
.70f
Big 5
.27
R w/Big 5 and g
.76g
Dimensions unit-weighted
Assessment center ratings
g and Big 5
.10
R
.51d
g
.15
R w/Big 5
.57e
Big 5
.30
R w/g
.71f
g and Big 5
.12
R w/Big 5 and g
.77g
Dimensions optimally weighted
Notes: All computations based on estimates of predictor interrelationships obtained from the present sample and estimates of operational validity obtained from the meta-analytic literature (see Table A2 for
sources). aBased on Big 5 intercorrelations from this sample and Big 5 operational validities from Hough et al. (1998). bFrom Hunter (1980). cBased on a, b, and Big 5–cognitive ability correlation from this sample.
d
Based on operational validities of AC dimensions from Arthur et al. (2003) and AC dimension intercorrelations from this sample. eBased on d and AC dimension–Big 5 correlations from this sample. fBased on d
and AC dimension–cognitive ability correlations from this sample. gBased on sources for d, e, and f. g, general mental ability (overall score on three cognitive ability tests); Big 5, Big 5 dimensions of personality;
AC, unit-weighted composite of primary assessment center dimensions.
Operational validity
R
r
.26a
.56b
Incremental validity over . . .
g
AC
g and AC
.10
.03
.05
Personality
Big 5
Table A5. Computation of operational and incremental validity: Top-level manager sample (N ¼ 1921)
270
Stephan Dilchert and Deniz S. Ones
International Journal of Selection and Assessment
Volume 17 Number 3 September 2009
& 2009 Blackwell Publishing Ltd.