Integrity & Personality 1
RUNNING HEAD: Integrity & Personality
Integrity and Personality: A Person-Oriented Investigation
S. Bartholomew Craig
Kaplan DeVries, Inc.
Jeffrey A. Smith
Personnel Decisions International
[In D. Norris (Chair) Patterns, Patterns Everywhere! Application of Person-oriented
Methodology to Problems in Industrial-Organizational Psychology. Symposium presented at the
annual conference of the Society for Industrial-Organizational Psychology in New Orleans, April,
2000.]
Integrity & Personality 2
Abstract
This article presents two studies that applied person-oriented methodology to the
investigation of personality and integrity test scores. In each study, cluster analysis was used to
identify five (in Study 1) or four (in Study 2) primary personality configurations, using the Five
Factor model of personality. Significant differences were found among the personality types with
regard to scores on both overt and personality-based integrity tests. High- and low-integrity
profiles were identified, but they did not correspond to the predictions of past research. Practical
implications for selection applications and directions for future research were discussed.
Integrity & Personality 3
In Webster's New Collegiate Dictionary (Merriam-Webster, 1963, p. 439), one finds the
following under integrity: "adherence to a code of moral, artistic, or other values... see
HONESTY". But the number of different definitions that have been applied by authors is
probably not much less than the number of authors who have written on the topic. The present
discussion is concerned however, not with moral philosophy, but with a class of psychological
tests. According to the Association of Personnel Test Publishers (APTP, 1991), an integrity test
is a psychological inventory designed to predict the likelihood that an applicant will exhibit
counterproductive or delinquent behaviors, such as rule breaking, work-related accidents, and
theft. Instruments of this kind have also been called tests of honesty, dependability,
trustworthiness, and reliability (Sackett & Wanek, 1996), but the present article will use the term
"integrity" because it is the term most commonly used in the psychological literature to describe
such tests. Further, unless explicitly noted otherwise, this article will use the term "integrity" to
mean "scores on integrity tests."
Integrity tests may be categorized as either overt (also called "clear purpose") or
personality-based (also called "disguised purpose" or "covert") (Sackett & Harris, 1984; Sackett
& Wanek, 1996). Overt integrity tests generally ask respondents directly about their attitudes and
beliefs regarding theft, dishonesty, or other wrong-doing, as well as asking for direct admissions
of past misdeeds. This approach to the assessment of integrity is predicated on the hypothesis that
dishonest individuals differ from honest individuals with regard to their perceptions of the degree
of dishonesty inherent in such behaviors and of the frequency of their occurrence (Ryan, Schmit,
Daum, Brutus, McCormick, & Brodke, 1997).
Personality-based integrity tests, rather than being explicitly focused on theft or
dishonesty, assess broader personality constructs that are thought to be related to dishonest or
Integrity & Personality 4
counterproductive behavior. Tests of this type are likely to include items related to dependability,
social conformity, sensation-seeking, trouble with authority, and hostility (Sackett & Wanek,
1996). Both types of integrity tests have been found to be uncorrelated with cognitive ability
(Ones, Schmidt, & Viswesvaran, 1993; Sackett, Burris, & Callahan, 1989) and to not produce
adverse impact in selection applications (Ones, 1993; Ones & Viswesvaran, 1998a).
The Employee Polygraph Protection Act of 1988 virtually eliminated the use of
pre-employment polygraph tests in private industry. As a result, personality-based and overt
integrity tests have become widely used as selection devices in organizations (Sackett & Wanek,
1996). In response to this trend, both the American Psychological Association (APA) (Goldberg,
Grenier, Guion, Sechrest, & Wing, 1991) and the Congressional Office of Technology
Assessment (OTA, 1990) commissioned studies to examine the use of integrity tests as selection
instruments. Subsequently, both reports concluded that the functioning of commercial integrity
tests was not well understood and called for more research on the topic.
The research community has responded to these calls for more research with a flurry of
activity focused on integrity tests. Many of these investigations have examined integrity tests'
criterion-related validity, using both internal criteria, such as admissions of theft or other
misdeeds, and external criteria, such as documented occurrences of theft, counterproductive work
behavior, and job performance (see Ones, Viswesvaran, & Schmidt, 1993 and Sackett & Wanek,
1996 for recent reviews). There now seems to be strong consensus in the literature: integrity
tests have substantial validity as predictors of such criteria and even contribute uniquely, beyond
cognitive ability tests and conventional personality inventories, to their prediction (Mikulay &
Goffin, 1998; Murphy & Lee, 1994a).
Integrity & Personality 5
Other research has focused on the construct validity of integrity tests. Typically, research
of this sort has assessed the relations between integrity tests and personality instruments with
correlational or linear regression techniques. (e.g., Logan & Koettel, 1986; Mikulay & Goffin,
1998; Murphy & Lee, 1994b; Ones, 1993; Woolley & Hakstian, 1992). Unfortunately, this line of
inquiry has produced contradictory findings with regard to which personality variables are
significantly correlated with integrity, as well as to the magnitude, and even the direction, of
those relations.
For example, Woolley and Hakstian (1992) found integrity test scores to be negatively
related to extraversion, positively related to agreeableness, and unrelated to conscientiousness,
emotional stability, or openness, with agreeableness showing the strongest relation to integrity. In
contrast, Murphy and Lee (1994b) found conscientiousness to have the strongest (and a positive)
relation with integrity, of the Big Five personality dimensions, followed by a negative relation
with extraversion, a negative relation with openness, a positive relation with agreeableness, and
no relation with emotional stability. More recently, Neuman and Baydoun (1998) found integrity
scores to be positively related to extraversion and conscientiousness, and unrelated to
agreeableness, emotional stability, or openness. Perhaps the most comprehensive investigation to
date of the relation between integrity and personality is a large scale meta-analysis conducted by
Ones (1993). That investigation concluded that integrity is, in order of decreasing strength,
negatively related to extraversion, and positively related to agreeableness, conscientiousness,
openness, and emotional stability. These examples are not intended to represent an exhaustive, or
even representative, summary of research in the domain of integrity testing but, rather, to
illustrate the diversity of results that previous research has produced.
Integrity & Personality 6
The Present Research
A Person-Oriented Approach
Although previous research has provided hints as to which personality configurations (or
"types" or "patterns" or "profiles") might correspond to high scores on integrity tests, no research
has investigated that question with person-oriented methodology. Person-oriented research, as
distinct from variable-oriented research, has been described as "a holistic, interactionistic view in
which the individual is seen as an organized whole, functioning and developing as a totality"
(Bergman & Magnusson, in press, p. 1). In more practical terms, a person-oriented approach to
psychological research implies a process of identifying individuals with certain characteristics
and then examining outcomes for those individuals. In that sense, a person-oriented approach is a
within-person approach.
In contrast, the variable-oriented approach that is more common in psychological research
seeks to measure relations among variables and across individuals. An important difference
between the two approaches, analytically, is that, in order for a variable-oriented analysis to
discover a relation, that relation must hold, more or less, across all the members of the research
sample. That is, the relation in question must hold more often than not. A person-oriented
approach, rather, seeks to identify disparate, but internally homogenous, subgroups, for whom
different rules apply.
For example, there may be individual differences in how personality translates into
integrity. That is, it may be possible for two individuals to exhibit the same standing on integrity,
but display different profiles on the Big Five dimensions. Traditional correlation-based,
variable-oriented, analyses would be "confused" by such a state of affairs; the individual
differences in the relations between predictors and criterion would appear as "noise," and the
Integrity & Personality 7
magnitude of observed relations would be attenuated. A person-oriented approach, however,
could be used to identify subgroups that are homogenous with regard to such individual
differences, in which case subgroup membership would become a potential moderator for further
investigation.
Despite their differences, however, the two approaches are not at odds, but complement
each other. The two studies described here provide an example of how traditional
variable-oriented research can inform a person-oriented investigation. Specifically, the results of
previous correlational research were used to generate hypotheses about the personality
configurations that correspond to high and low integrity.
This article describes two studies that used the Five Factor Model of personality (FFM) to
identify multiple, distinct, personality configurations, and then examined outcomes for those
configurations in terms of scores on commercial integrity tests. Such a person-oriented approach
allows for the examination of questions about the relative frequency with which different patterns
of Big Five scores occur, and differences among those patterns with regard to integrity test
scores.
The Five Factor Model
The FFM or "Big Five" taxonomy of personality traits has received wide attention and
acceptance as providing a comprehensive model of normal human personality (Barrick & Mount,
1991; Digman, 1990; Goldberg, 1990), although it has not gone uncriticized (e.g., Block, 1995).
However, whether or not the FFM fully encompasses the totality of human personality, it has at
least two characteristics that make it well suited to the current application. First, it provides a
widely known and understood "common metric" for comparing different personality instruments.
Second, the relatively small number of dimensions (i.e., five) facilitates the interpretation of
Integrity & Personality 8
research results, the interpretation of which could be quite daunting, if not impossible, under a
model with many more dimensions. Although they are sometimes called by different names, this
article refers to the Big Five dimensions as extraversion, agreeableness, conscientiousness,
emotional stability, and openness.
Cluster Analysis
This research used the technique of cluster analysis to identify subgroups that were
homogenous with regard to their patterns of scores on the Big Five dimensions of personality.
The interested reader is referred to Aldenderfer and Blashfield (1984) and Borgen and Barnett
(1987) for more information about cluster analysis. But, in brief, cluster analysis begins by
constructing a proximity matrix that describes the profile similarity for all possible pairwise
comparisons of individuals in a data set. The procedure then proceeds in a series of iterations or
"fusions," wherein the two most similar entities (i.e., individuals or previously fused clusters of
individuals, represented by their means) are fused into a cluster on each iteration. The process
continues until all individuals in the data set have been fused into a single cluster. The researcher
then examines the statistics associated with each successive fusion to determine the cluster
solution (i.e., the number of clusters) that best fits the data.
Cluster analysis is frequently compared to exploratory factor analysis, but there are
important differences. Factor analysis is a statistical procedure that partitions variance, usually
that of test items, to different sources, including error. Cluster analysis is, at its core, an
arithmetic procedure that does not partition variance; nor does it make assumptions about the
distributions of variables (Borgen & Barnett, 1987). That said, factor analysis can be used to
group individuals into homogenous groups, much like cluster analysis. Factor analysis used in
this fashion is frequently called "Q-type" or "person" factor analysis, because the data matrix is
Integrity & Personality 9
first transposed so that persons become "variables" and vice versa. Use of factor analysis for
grouping persons has several disadvantages relative to cluster analysis, however, and is not
recommended (Aldenderfer & Blashfield, 1984; Borgen & Barnett, 1987; Everitt, 1980). Among
the advantages of cluster analysis is that a great deal of simulation research has been conducted
with the technique, such that the characteristics of various methods are well understood; the same
is not true of Q-type factor analysis.
Research Questions
The present study used person-oriented methodology to explore the following questions:
1. How many unique personality configurations constitute a parsimonious, yet reasonably
robust typology?
2. What are the defining characteristics of those personality patterns?
3. How frequent is each pattern?
4. Do the patterns differ with regard to scores on commercial integrity tests?
Because no previous research has investigated the relative frequency of personality
configurations using the Big Five, there was no basis for hypotheses regarding the existence or
frequency of specific patterns; the present research serves as an initial exploration of those issues.
On the basis of Ones (1993), it was expected that the highest integrity test scores would be found
in individuals displaying a personality pattern consisting of low extraversion, high agreeableness,
high conscientiousness, high emotional stability, and high openness, if such a pattern exists.
Similarly, the lowest integrity test scores should come from individuals displaying high
extraversion, low agreeableness, low conscientiousness, low emotional stability, and low
openness.
Integrity & Personality 10
Study 1
Method
Sample and Procedure
For Study 1, data previously presented by Murphy and Lee (1994b) were reanalyzed. This
data set contains 180 responses to a personality inventory based on the FFM and to both an overt
and a personality-based integrity test. One hundred and six undergraduate college students (59%)
participated in order to satisfy experimental credit requirements. Additionally, questionnaires
were mailed to 199 nontraditional students who had recently registered for university courses.
Twenty-three questionnaires were returned undelivered, and 74 (41%) were returned completed,
for a response rate of 42%. All responses were provided under anonymous conditions.
Instruments
Hogan Personality Inventory (HPI) (Hogan, 1986). The HPI is a comprehensive measure
of normal personality consisting of 45 "homogenous item clusters" (HICs) of three to seven items
each. The 45 HICs are arranged into six primary scales, representing all of the Big Five
personality factors. Extraversion is measured by the HPI's Sociability scale, agreeableness by
Likability, emotional stability by Adjustment, and openness by Intellectance. In the present study,
conscientiousness was represented by the mean of the HPI's Prudence and Ambition scales. The
average coefficient alpha for the six primary scales was 0.81.
Personnel Decisions International's Employment Inventory (EI) (Paajanen, 1988). The EI
is a personality-based integrity test with two subscales. The Performance scale (EI-P) "is
intended to measure personality characteristics related to employee dependability, which
basically underlies the full range of productive and counterproductive behavior" (Paajanen,
Hansen, & McLellan, 1993, p. 11). The Tenure scale of the EI (EI-T) was designed to predict
Integrity & Personality 11
how long a candidate would voluntarily stay on a job, if hired. Because the EI uses a proprietary
scoring key, it was not possible to estimate its reliability in this sample. In the instrument's
manual, the vendor reports the internal consistency of the Performance scale to be 0.74 and that
of the Tenure scale to be 0.64 (Paajanen et al., 1993).
London House Personnel Selection Inventory (PSI) (London House, Inc., 1980). The PSI
is an overt integrity test. The Honesty scale of the PSI is designed to measure attitudes toward
employee theft, as well as the likelihood of not engaging in theft-related behavior on the job. The
PSI also asks respondents for admissions of past thefts, in terms of their dollar value. Due to its
proprietary scoring protocol, it was not possible to estimate the PSI's reliability from this sample.
However, Ones (1998b) has reported the mean coefficient alpha for overt integrity tests to be
0.82 and their mean test-retest reliability to be 0.87.
Analyses
In order to map the six primary scales of the HPI on to the FFM, a composite index of
Conscientiousness was constructed by averaging the HPI Prudence and Ambition scales. Next,
cases with missing data on any of the Big Five indicators were removed from the data set. This
was necessary because cluster analysis requires complete data from all participants (Bergman &
El-Khouri, 1998). Ten cases were thus removed from the data set, leaving 170 complete cases.
No classification taxonomy can classify every individual and remain parsimonious; there
will always be some cases that do not fit neatly into the categorization scheme (Aldenderfer &
Blashfield, 1984). Cluster analysis can often be facilitated by removing these "residual" cases
before clustering (Bergman & El-Khouri, 1998; Borgen & Barnett, 1987). The criterion for
removal in the present study was that residual cases must not be similar to any other case by less
than an average squared Euclidean distance (see below) of .5, using standard scores (see
Integrity & Personality 12
Aldenderfer & Blashfield for a discussion of different similarity indices). Six such cases were
identified and removed from the data set, resulting in a final sample size of 164. It should be
noted that this was a relatively conservative criterion for outlier removal, because cases were
required to be similar to only one other case in order to be retained for analysis.
The remaining 164 cases were submitted to hierarchical agglomerative cluster analysis
using Ward's (1963) minimum variance method, standardized scores, and squared Euclidean
distance as the similarity index. Ward's minimum variance method was chosen because past
research with Monte Carlo simulations has found it to be equivalent or superior to all other
methods for recovering known cluster structures (Aldenderfer & Blashfield, 1984; Milligan,
1981). Ward's method has an additional advantage in that it provides an objective function, the
error sum of squares (ESS), that can be used to identify an appropriate number of clusters to
retain (Borgen & Barnett, 1987). Scores were standardized before clustering so that scales with
more variance would not be disproportionately highly weighted in the calculation of the
similarity indices. Squared Euclidean distance was chosen as the similarity index because it
encompasses all three components of pattern similarity: shape, level, and scatter (Cronbach &
Gleser, 1953).
After an initial cluster solution was obtained using Ward's method, that solution served as
the basis for a second "iterative partitioning" or "k-means" analysis. This procedure re-evaluates
each case's assignment to its cluster, and relocates cases to different clusters if the total error sum
of squares (ESS) of the cluster solution can be reduced by doing so. This process continues
iteratively, with clusters' means being recalculated on each iteration, until no further ESS
reduction can be achieved. This second stage analysis helps to counter the phenomenon of
"centroid drift." Centroid drift occurs because, during the hierarchical agglomeration of the first
Integrity & Personality 13
stage, cases are permanently assigned to clusters and are not relocated, even if later additions to
clusters shift their means, or centroids, such that earlier additions are no longer appropriate
(Bergman & El-Khouri, 1998; Borgen & Barnett, 1987).
Finally, analysis of variance (ANOVA) was used to test whether members of different
clusters differed significantly on integrity test scores. Duncan's multiple range test (MRT) was
used to test the significance of mean differences. All significance tests used an alpha level of .05.
All cluster analyses were conducted using the SLEIPNER computer program (Bergman &
El-Khouri, 1998). All other analyses and data manipulation were accomplished with the PC-SAS
computer program (SAS Institute, 1994).
Results
Cluster Structure
Because Ward's minimum variance method generates as output the increase in ESS at
each step of the clustering process, it is possible to construct a graph that is analogous to the
scree plot used in factor analysis (Borgen & Barnett, 1987). Much like its analog in factor
analysis, the ESS increment graph allows for visual identification of a "break" in the plotted
values. In this case, such a break signifies a disproportionate increase in ESS as a result of the
fusion of two clusters, indicating that the two clusters were very dissimilar relative to the fusions
that came before. According to this heuristic, the cluster solution that existed just before the
break is chosen as the best compromise between parsimony and within-cluster homogeneity. The
increases in ESS for the final 30 fusions of the cluster analysis are depicted in Figure 1. Note
that, temporally, the process progresses from right to left.
Insert Figure 1 About Here
Integrity & Personality 14
As can be seen in Figure 1, a disproportionate increase in ESS occurs as a result of agglomerating
from five to four clusters. This fact suggests that a five cluster solution is most appropriate for
these data.
Using the initial assignment of cases to those five clusters, the iterative partitioning, or
k-means, procedure described earlier was used to relocate cases that were better suited to a
different cluster. This procedure required four iterations to stabilize, and resulted in the
reassignment of 33 cases to other clusters, increasing the ESS explained by the cluster solution
from 47.96 to 52.69, and the average cluster homogeneity from 1.03 to 1.01. A cluster's
homogeneity coefficient is the average similarity index for all possible pairwise comparisons in
that cluster. In this case, homogeneity is expressed in terms of average squared Euclidean
distance. Lower numbers indicate more homogenous clusters. Descriptive statistics for the final
cluster solution, and for the sample as a whole, are presented in Table 1. The five clusters are
graphically represented in Figure 2.
Insert Table 1 About Here
Insert Figure 2 About Here
Differences among Clusters on the EI Performance Scale
The results of the one-way ANOVAs indicated significant differences among clusters on
all three integrity scales. On the personality-based EI Performance scale (omnibus F = 5.97,
p < .05), Duncan's multiple range test showed members of Clusters 3 (N = 36, 22%) and 4
Integrity & Personality 15
(N = 24, 15%) to have scored significantly higher than those of Clusters 1 (N = 32, 20%) and 5
(N = 23, 14%) (p < .05). Cluster 4 also scored higher than Cluster 2 (N = 49, 30%) (p < .05).
Separate t tests also showed Cluster 4 to have higher scores on the EI-P than the rest of the
sample (N = 137) (t(42) = 4.87, p < .05) and Cluster 1 to have lower EI-P scores than the rest of
the sample (N = 130) (t(159) = -2.41, p < .05).
Differences among Clusters on the EI Tenure Scale
The EI's Tenure scale also showed significant differences among clusters (F = 7.55,
p < .05). Cluster 4 members scored higher, on average, than members of any of the other four
groups (p < .05). Additionally, Cluster 1 showed significantly lower EI-T scores than did
Clusters 3, 4, and 5 (p < .05) and the rest of the sample (N = 130) (t(159) = -3.38, p < .05).
Finally, another t test showed Cluster 4 to have higher EI-T scores than the rest of the sample, as
a whole (N = 137) (t(159) = 4.32, p < .05).
Differences among Clusters on the PSI Honesty Scale
Significant differences were also found on the overt PSI Honesty scale (F = 3.04, p < .05).
Duncan's MRT indicated that members of Clusters 2 and 4 scored significantly higher than any
of the other three clusters (p < .05) and were not significantly different from each other. Once
again, members of Cluster 4 were found to have higher PSI Honesty scores than participants not
in Cluster 4 (N = 128) (t(149) = 2.99, p < .05). The implications of these results will be
considered, along with those of Study 2, in the discussion at the end of this article.
Study 2
Having examined the structure of a student sample with regard to Big Five patterns and
their relations with integrity test scores, it was then desirable to apply the same methodology to a
field sample. In this manner, some evidence for the generalizability of the results from Study 1
Integrity & Personality 16
could be obtained. Additionally, Study 2 made use of a personality instrument that was not
designed to reflect the Five Factor Model. Thus, the utility of the Big Five taxonomy for
providing a common metric with which to compare different personality instruments was also
assessed.
Method
Sample
The data analyzed in Study 2 were the responses of 449 financial industry employees to a
comprehensive personality inventory and a personality-based integrity test. Two hundred twenty
nine participants (51%) were classified as customer service representatives, with the remaining
220 (49%) classified as financial service representatives. Of those reporting their gender
(N = 411, 92%), 321 (78%) were female; 90 (22%) were male. Four hundred thirteen (92%)
individuals provided information about their ethnicity. Of those, 349 (85%) were white, 38 (9%)
were African-American, 18 (4%) were Hispanic, 7 (2%) were Native American, with one
participant marking "other."
Instruments
Personal Performance Inventory (PPI) (Personnel Decisions International, 1995). The PPI
is a personality inventory developed for use in personnel assessments in organizational settings.
It consists of 284 items, arranged into 22 subscales: Planning, Personal Organization, Attention
to Detail/Accuracy, Intellectual Curiosity, Influence, Team Plan, Interpersonal Perceptiveness,
Poise, Sociability, Affiliation, Consideration, Tolerance, Responsibility, Independence,
Adaptability, Emotional Control, Relaxed, Optimism, Need for Achievement/Activity,
Persistance/Results Orientation, Social Desirability, and Random Response. Due to the
Integrity & Personality 17
proprietary nature of the scoring key for the PPI, it was not possible to estimate the reliability of
each subscale separately, but coefficient alpha for all 284 items was 0.95.
Employment Inventory (EI) (Paajanen, 1988). As in Study 1, the Performance and Tenure
scales of the EI were used as an example of a personality-based integrity test (see Study 1 for
more details about the EI). No overt integrity test data were available for Study 2.
Analyses
The analyses for Study 2 proceeded identically to those in Study 1, with one exception.
Because the PPI was not explicitly designed to reflect the Five Factor model of personality, it
was necessary to categorize the 19 personality scales of the PPI into the Big Five taxonomy (the
Social Desirability and Random Response scales were excluded from these analyses). Following
Barrick and Mount (1991), expert judges were used to accomplish this task. Of the eight expert
judges who agreed to assist in this endeavor, seven described their field as
industrial-organizational psychology and one described it as clinical psychology. Four were
private sector researchers, two were practitioners/consultants, one was an academic researcher,
and one was a graduate student. Two of the judges reported having doctoral degrees, with the
remaining six having master's degrees (at least two of these individuals acquired doctoral degrees
within 3 months after serving as expert judges for this study). All judges described themselves as
expert or extremely familiar with regard to the Five Factor model of personality.
Judges responded by completing a standardized survey form that included brief
descriptions of the PPI scales, along with instructions not to assume that all of the Big Five were
represented or were represented with equal frequency. In order for a PPI scale to be included in
the analyses, at least six of the eight judges had to agree as to its categorization into one of the
Integrity & Personality 18
Big Five categories. Fourteen of the 19 PPI scales met this criterion, with eight of those
generating unanimous agreement, and another four having only one dissenter.
Once the 14 PPI scales were categorized, indicators for the Big Five factors were
constructed by averaging the PPI scale scores within each Big Five category. Six cases were then
removed from the data set because they were missing scores for one or more of the Big Five
indicators. Using the same criterion applied in Study 1, five additional cases were identified as
outliers and removed. Thus, 438 cases were submitted to cluster analysis.
Results
Cluster Structure
An examination of the ESS increment graph revealed a disproportionate increase in ESS
accompanying the fusion from four to three clusters (see Figure 3). Such a jump in ESS at that
point suggested that a four cluster solution best fit these data.
Insert Figure 3 About Here
Using the four cluster solution suggested by Ward's method as a starting point, the
iterative partitioning procedure was used to reevaluate each case's cluster assignment. Eighty six
cases were relocated to different clusters during this stage of the analysis, in seven iterations.
This process increased the explained ESS of the cluster solution from 49.30 to 52.42 and the
average within-cluster homogeneity from 1.06 to 1.01. Descriptive statistics for the final cluster
solution, and for the sample as a whole, are presented in Table 2. The four clusters are
graphically represented in Figure 4.
Insert Table 2 About Here
Integrity & Personality 19
Insert Figure 4 About Here
Differences among Clusters on the EI Performance Scale
As in Study 1, there were significant differences among clusters on the Performance scale
of the EI. Although an omnibus F test did not indicate significant differences among clusters on
the EI-P (F = 2.47, p = .06), both Duncan's multiple range test and a separate t test revealed that
Cluster 1 (N = 99, 23%) was significantly higher than Cluster 3 (N = 96, 22%) on the EI-P
(t(193) = 2.80, p < .05). A t test of the difference between Cluster 1 and the rest of the sample
(N = 339) on the EI-P did not reveal a significant difference (t(436) = 1.91, p = .06). But EI-P
scores for Cluster 3 were found to be significantly lower (t(436) = -2.39, p < .05) than those for
the rest of the sample (N = 342).
Differences among Clusters on the EI Tenure Scale
Interestingly, the differences among clusters on the EI's Tenure scale were more marked
than those described above for the Performance scale. The omnibus F test was significant
(F = 3.12, p < .05), as were two of the planned comparisons among clusters. Duncan's MRT
revealed the mean EI-T score for Cluster 3 to be significantly lower than that of either Cluster 1
or Cluster 4 (p < .05). Cluster 3 was also lower (t(436) = -2.22, p < .05) than the rest of the
sample (N = 342) . Finally, another t test showed Cluster 1 to have higher EI-T scores
(t(436) = 2.27, p < .05) than the rest of the sample (N = 339). A summary of mean integrity
scores for all integrity tests and clusters, across both Study 1 and Study 2, is shown in Figure 5.
Insert Figure 5 About Here
Integrity & Personality 20
Discussion
The two studies presented here used cluster analytic techniques and the Five Factor
Model of personality to identify regularly occurring personality patterns and related those
patterns to scores on both overt and personality-based integrity tests. It was expected, based on
previous meta-analytic findings (Ones, 1993), that the highest integrity scores would come from
individuals with low extraversion, high agreeableness, high conscientiousness, high emotional
stability, and high openness. No such pattern emerged in either study. This fact underscores a key
distinction between variable-oriented and person-oriented research; traditional variable-oriented
research can provide hypotheses about outcomes for putative patterns, but person-oriented
research is necessary to determine whether such patterns actually exist with any regularity.
It is noteworthy, however, that one close approximation to the putative high integrity
pattern did emerge in each study. In Study 1, Cluster 4 was characterized by low extraversion,
high agreeableness, high conscientiousness, high emotional stability, but low openness.
Similarly, Cluster 1 in Study 2 matched the hypothetical high integrity pattern, except with
regard to agreeableness, which was low in Cluster 2-1. Further, those two groups displayed the
highest average integrity test scores in their respective samples, for all integrity tests represented
here. This suggests that all five personality dimensions may not be necessary to define the high
integrity pattern. Future research should investigate whether openness and agreeableness are
inherently less relevant to the configuration, or whether consistency with any four of the five
pattern markers is sufficient to define the high integrity profile.
Relatedly, the hypothesized low integrity configuration also did not emerge in either
sample. The lowest integrity scores in Study 1 came from members of Cluster 1, which was
characterized by high extraversion, average agreeableness, low conscientiousness, low emotional
Integrity & Personality 21
stability, and high openness. Similarly, the lowest integrity test scores in Study 2 were found in
Cluster 3, members of which displayed high extraversion, high agreeableness, low
conscientiousness, low emotional stability, and low openness. Together, these results are
consistent with the hypothesis that openness and agreeableness are not crucial to determining the
high integrity personality pattern. This is somewhat at odds with Ones' (1993) meta-analytic
findings, however, wherein extraversion and openness were found to be least important to the
prediction of integrity. In contrast, the present research suggests that (low) extraversion is an
important determinant of the high integrity profile.
The practical implications of the high- and low-integrity profiles become evident when
we consider that, in selection settings, integrity tests are nearly always used with dichotomous cut
scores (Sackett & Wanek, 1996). For example, although the manual for the EI doesn't
recommend a specific cut score, it does give examples of cut scores that range from 52 to 58 for
the Performance scale (Paajanen et al., 1993). Using the median of that range (55) as an example,
we find that members of the two high integrity profiles, 1-4 and 2-1, would usually "pass" the test
(mean Performance scores = 60 and 56, respectively) and members of the two low integrity
profiles, 1-1 and 2-3, would usually "fail" the test (mean Performance scores = 50 and 53,
respectively). Similarly, using the recommended cut score for the PSI (30) (London House, 1980)
would result in only members of the highest integrity profile, 1-4 (mean PSI-Honest score = 38),
passing the test, on average. Additionally, integrity test scores within certain clusters are
considerably less variable than scores in general. For example, EI-P scores in Cluster 1-4 were
32% less variable than in the sample as a whole (see Tables 1 and 2 for more detail).
More research is clearly needed, but it may eventually be possible to select high integrity
employees as effectively with conventional personality instruments as they are now being
Integrity & Personality 22
selected by specialized, commercial, proprietary integrity tests. Such an approach might even
have advantages over selection with integrity tests, in terms of fakability. Because applicants
would be selected on the basis of a multivariate profile, it should be more difficult for them to
guess the "correct" pattern of responses. Using personality inventories to select on integrity
would almost certainly yield a financial benefit to organizations, especially if they currently use
both personality and integrity tests in their selection batteries.
Another important issue concerns the nature and frequency of the personality types
discovered. Perhaps the most obvious discovery in that area was that five configurations were
identified in the student sample, whereas only four emerged in the field sample. Further, the
correspondence between the clusters found in the two samples is somewhat equivocal. One
exception concerns Clusters 1-2 (30%) and 2-2 (36%), which appear to be the same
configuration. Both are characterized by high scores on all of the Big Five dimensions.
Additionally, the two high integrity profiles, 1-4 (15%) and 2-1 (23%) are quite similar to each
other, and differ primarily on agreeableness. Clusters 1-1 (20%) and 2-3 (22%) are also similar,
differing primarily on openness. Another close match is Cluster 1-5 (14%) and Cluster 2-4
(20%), with both displaying scores below the mean on all five dimensions. Cluster 1-3 (22%)
seems to have no analog in Study 2. Although these results fall short of clearly replicating the
same cluster structure in two samples, they provide a foundation on which future research can
build a typology of Big Five profiles. Additionally, the similarities among the clusters in the two
samples support the utility of the Five Factor model as a common metric for comparing different
personality instruments.
The fact that the two studies used two different personality inventories is both a strength
and a limitation. As mentioned above, the similarities among the clusters found in the two
Integrity & Personality 23
samples support the idea that the Big Five taxonomy can be usefully applied as a common metric
with which to compare different instruments. Unfortunately, however, the differences among the
clusters found in the two studies could be an artifact of the use of two different instruments.
Future research that uses identical personality instruments in diverse samples should eventually
resolve this question.
In summary, this research demonstrated how variable-oriented and person-oriented
methodology can complement each other in an investigation of personality and integrity test
scores. Although the exact number of unique personality configurations present in the human
population remains unknown, these results suggest that it may be a small number, perhaps four or
five. Additionally, personality type was found to be related to scores on commercial integrity
tests, but not exactly as predicted by previous research. It is hoped that this work will serve to
stimulate further person-oriented research on human personality.
Integrity & Personality 24
References
Aldenderfer, M.S., & Blashfield, R.K. (1984). Cluster Analysis. Newbury Park,
California: Sage.
Association of Personnel Test Publishers (APTP). (1991). Model Guidelines for
preemployment integrity testing programs. Washington, DC: Author.
Barrick, M.R. & Mount, M.K. (1991). The big five personality dimensions and job
performance: A meta-analysis. Personnel Psychology, 44, 1-26.
Bergman, L.R., & El-Khouri, B.M. (1998). SLEIPNER: A Statistical Package for
Pattern-Oriented Analyses (User's manual, version 2.0). Stockholm: Author.
Bergman, L.R. & Magnusson, D. (In press). A person-oriented approach in research on
developmental psychopathology. Development and Psychopathology.
Block, J. (1995). A contrarian view of the five-factor approach to personality description.
Psychological Bulletin, 117, 187-215.
Borgen, F.H. & Barnett, D.C. (1987). Applying cluster analysis in counseling psychology
research. Journal of Counseling Psychology, 34, 456-468.
Cronbach, L. & Gleser, G. (1953). Assessing similarity between profiles. Psychological
Bulletin, 50, 456-473.
Digman, J.M. (1990). Personality structure: Emergence of the five-factor model. Annual
Review of Psychology, 41, 417-440.
Everitt, B. (1980). Cluster Analysis (2nd ed.). New York: Halsted.
Goldberg, L.R. (1990). An alternative "description of personality:" The big five structure.
Journal of Personality and Social Psychology, 59, 1216-1229.
Integrity & Personality 25
Goldberg, L.R., Grenier, J.R., Guion, R.M., Sechrest, L.B., & Wing, H. (1991).
Questionnaires used in the prediction of trustworthiness in pre-employment selection decisions:
An APA Task Force report, Feb. 1991. Washington, DC: American Psychological Association,
Inc.
Hogan, R.T. (1986). Manual for the Hogan Personality Inventory. Minneapolis: National
Computer Systems.
Logan, T.G. & Koettel, R.C. (1986). Personality correlates of a test of honesty.
Psychological Reports, 59, 1015-1018.
London House, Inc. (1980). Personnel Selection Inventory. Park Ridge, IL: Author.
Merriam-Webster's collegiate dictionary (7th ed.). (1963). Springfield, MA:
Merriam-Webster.
Mikulay, S.M. & Goffin, R.D. (1998). Measuring and predicting counterproductivity in
the laboratory using integrity and personality testing. Educational and Psychological
Measurement, 58, 768-790.
Milligan, G.W. (1981). A review of Monte Carlo tests of cluster analysis. Multivariate
Behavioral Research, 16, 379-407.
Murphy, K.R. & Lee, S.L. (1994a). Does conscientiousness explain the relationship
between integrity and job performance? International Journal of Selection and Assessment, 2(4),
226-233.
Murphy, K.R. & Lee, S.L. (1994b). Personality variables related to integrity test scores:
The role of conscientiousness. Journal of Business and Psychology, 8, 413-424.
Neuman, G.A. & Baydoun R. (1998). An empirical examination of overt and covert
integrity tests. Journal of Business and Psychology, 13(1), 65-79.
Integrity & Personality 26
Office of Technology Assessment. (1990). The use of integrity tests for pre-employment
screening. Congress of the United States, Office of Technology Assessment.
Ones, D.S. (1993). The construct validity of integrity tests. Unpublished doctoral
dissertation, University of Iowa.
Ones, D.S., Schmidt, F.L., & Viswesvaran, C. (1993, May). Integrity and Ability:
Implications for incremental validity and adverse impact. Paper presented at the annual
conference of the Society for Industrial and Organizational Psychology, San Francisco.
Ones, D.S. & Viswesvaran, C. (1998a). Gender, age, and race differences on overt
integrity tests: Results across four large-scale job applicant data sets. Journal of Applied
Psychology, 83, 35-42.
Ones, D.S. & Viswesvaran, C. (1998b). Integrity testing in organizations. In Griffin, R.,
O'Leary-Kelly, A., & Collins, J.M. (Eds.) Dysfunctional Behavior in Organizations (v. 2):
Nonviolent Behaviors in Organizations. Stamford, CT: JAI Press.
Ones, D.S., Viswesvaran, C., & Schmidt, F.L. (1993). Comprehensive meta-analysis of
integrity test validities: Findings and implications for personnel selection and theories of job
performance. Journal of Applied Psychology, 78, 679-703.
Paajanen, G.E. (1988). The prediction of counterproductive behavior by individual and
organizational variables. Unpublished doctoral dissertation, University of Minnesota,
Minneapolis.
Paajanen, G.E., Hansen, T.H., & McLellan, R.A. (1993). PDI Employment Inventory and
PDI Customer Service Inventory Manual (1st ed.). Minneapolis: Personnel Decisions
International.
Integrity & Personality 27
Personnel Decisions International. (1995). Personal Performance Inventory. Minneapolis,
MN: Author.
Ryan, A.M., Schmit, M.J., Daum, D.L., Brutus, S., McCormick, S.A., & Brodke, M.H.
(1997). Workplace integrity: Differences in perceptions of behaviors and situational factors.
Journal of Business and Psychology, 12, 67-83.
Sackett, P.R., Burruss, L.R., & Callahan, C. (1989). Integrity testing for personnel
selection: An update. Personnel Psychology, 42, 491-529.
Sackett, P.R., & Harris, N.M. (1984). Honesty testing for personnel selection: A review
and critique. Personnel Psychology, 37, 221-245.
Sackett, P.R. & Wanek, J.E. (1996). New developments in the use of measures of
honesty, integrity, conscientiousness, dependability, trustworthiness, and reliability for personnel
selection. Personnel Psychology, 49, 787-829.
SAS Institute. (1994). PC-SAS 6.10 for OS/2 [Computer software]. Cary, NC: SAS
Institute.
Ward, J. (1963). Hierarchical grouping to optimize an objective function. Journal of the
American Statistical Association, 58, 236-254.
Woolley, R.M. & Hakstian, A.R. (1992). An examination of the construct validity of
personality-based and overt measures of integrity. Educational and Psychological Measurement,
52, 475-489.
Intellectanc
e
M (SD)
14.51 (3.83)
0.68 (0.70)
0.65 (0.66)
-0.99 (0.72)
-0.57 (0.90)
-0.05 (0.65)
Big Five Dimensions
Prudence/
Likability
Ambition
Adjustment
M (SD)
M (SD)
M (SD)
19.24 (3.77)
16.26 (3.57) 13.66 (4.89)
-0.72 (0.59)
0.84 (0.62)
-0.62 (0.61)
0.78 (0.58)
-0.52 (0.94)
Full Sample
(N=164)
Cluster 1
0.80 (0.71)
0.03 (0.48)
-0.80 (0.66)
(N=32; 20%)
Cluster 2
0.37 (0.63)
0.58 (0.68)
0.63 (0.63)
(N=49; 30%)
Cluster 3
0.28 (0.63)
0.11 (0.58)
0.01 (0.72)
(N=36; 22%)
Cluster 4
-1.41 (0.49)
0.54 (0.68)
0.79 (0.74)
(N=24; 15%)
Cluster 5
-0.55 (0.84)
-1.60 (0.55)
-0.81 (0.95)
(N=23; 14%)
Note. Native metric shown for Full Sample; all others standardized.
Sociability
M (SD)
10.02 (3.99)
Table 1
Descriptive Statistics for Study 1
Table 1
-0.30 (1.12)
0.70 (0.68)
0.28 (0.94)
-0.11 (0.83)
-0.35 (1.10)
EI-P
M (SD)
53.68 (8.68)
0.02 (1.15)
0.81 (0.76)
0.21 (0.89)
-0.17 (1.02)
-0.52 (0.83)
EI-T
M (SD)
20.16 (5.19)
-0.20 (1.15)
0.59 (0.91)
-0.10 (0.82)
0.12 (1.11)
PSI Honesty
M (SD)
26.91
(19.60)
-0.26 (0.85)
Integrity Test Scores
Agreeableness
M (SD)
Conscientiousness
M (SD)
Big Five Dimensions
Full Sample
8.60 (2.41)
11.46 (1.78)
10.17 (1.56)
(N=438)
Cluster 1
-0.53 (0.84)
-0.75 (0.86)
0.24 (0.70)
(N=99;23%)
Cluster 2
0.73 (0.52)
0.65 (0.65)
0.76 (0.54)
(N=156; 36%)
Cluster 3
0.41 (0.62)
0.44 (0.63)
-0.67 (0.81)
(N=96; 22%)
Cluster 4
-1.15 (0.75)
-0.81 (0.86)
-0.89 (0.91)
(N=87; 20%)
Note. Native metric shown for Full Sample; all others standardized.
Extraversion
M (SD)
Table 2
Descriptive Statistics for Study 2
Table 2
8.92 (1.73)
0.11 (0.60)
0.90 (0.54)
-0.47 (0.69)
-1.21 (0.64)
0.34 (0.73)
0.72 (0.60)
-0.54 (0.81)
-1.07 (0.73)
Openness
M (SD)
Emotional
Stability
M (SD)
7.83 (2.64)
0.01 (1.11)
-0.21 (0.94)
0.03 (0.98)
0.18 (0.97)
54.94 (7.56)
EI-P
M (SD)
0.10 (0.96)
-0.20 (1.00)
-0.06 (0.95)
0.20 (1.11)
25.05 (4.52)
EI-T
M (SD)
Integrity Test Scores
0
10
20
30
40
0
5
10
Clusters Retained
15
20
Figure 1
Figure 1
ESS Increase for Final 30 Fusions (Study 1)
ESS Increment
25
30
All 7
Lik
a
bil
it
y
(A
)
So
c
i
a
bil
it
y
(E
)
Note. Standard scores shown. H = Homogeneity coefficient
-2
-1
0
1
ud
Pr
2
e/A
en
c
ion
mb
it
Figure 2
Cluster Centroids for Study 1
Ad
ju
stm
S)
t (E
en
Figure 2
In
t
e
lle
c
t
a
nc
e
(O
)
Cluster 5 (H=1.36)
Cluster 4 (H=1.02)
Cluster 3 (H=.91)
Cluster 2 (H=.89)
Cluster 1 (H=.84)
Figure 3
0
50
100
150
0
5
10
Clusters Retained
15
Figure 3
ESS Increase for Final 30 Fusions (Study 2)
ESS Increment
20
25
30
Note. Standard scores shown.
-2
-1
av
Ex
tr
0
on
ers
i
1
ab
2
A
ss
len
e
gre
e
ou
Figure 4
Cluster Centroids for Study 2
sc
Co
n
Figure 4
e
Op
ss
sn
e
ien
ti
ss
nn
e
Em
o
tio
n
a
lS
t
a
b
ility
Cluster 4 (H=1.23)
Cluster 3 (H=1.03)
Cluster 2 (H=.65)
Cluster 1 (H=1.13)
EI Performance
Note. Mean scores shown on their native metrics
20
30
40
50
60
Figure 5
Mean Integrity Test Scores
EI Tenure
Figure 5
Cluster 1-1
Cluster 1-2
Cluster 1-3
Cluster 1-4
Cluster 1-5
PSI Honesty
Cluster 2-1
Cluster 2-2
Cluster 2-3
Cluster 2-4