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Meta-analysis of a Personality Profile for Predicting Sales Success
Stephen Murphy, M.A.University of Oklahoma
Scott Davies, Ph.D.
Hogan Assessment Systems
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1 – INTRODUCTION
In many personnel selection testing situations, scales from various tests arecombined to provide optimal prediction of a criterion space. Linear methods are
often used, such as summation of scale scores, or use of multiple linear
regression models (Guion, 1978). This is known as a compensatory model, where
two or more scale scores are combined into a composite according to an
algorithm (Dawes, 1979). By combining scores in this way, compensation for
one or more of the scale scores by another scale score is allowed. An alternative
method is configural scoring, which consists of scoring a set of two or more scales
as a pattern or profile of responses to the scales.
Meehl (1950) is credited for first presenting methods for scoring psychological
tests in configural, rather than compensatory models. Dunnette furtherdescribed configural scoring in his Personnel Selection and Placement text in
1966. Although configural scoring has long been best practice for personnel
selection procedures using personality tests (Hogan, 1983), critics (e.g., Murphy,
2004) continue to state that there is no evidence for the validity of configural
scoring. This view is shared by Jenkins (2002). In this line of research, Jenkins
tested the incremental validity of a profile scoring approach over single scales
and found that the profile scoring was not effective in accounting for variance in
supervisor performance. Jenkins suggested caution in using profile-level
judgments. Likewise, Waters and Sackett (2006) contend that a linear regression
approach outperforms configural methods.
Other researchers (Adler, 1996; Jackson, Wroblewski, & Ashton, 2000; McCrae &
Costa, 1995; Tenopyr, 2002) contend that configural scoring is an important
component of interpreting personality test results. Personality scale scores do
not occur in isolation, but are related to one another in constellations, or
configurations. For example, it is important that a sales person be both ambitious
and social. Being overly social cannot compensate for being lazy—in fact this
combination may be worse than low scores on both scales. This is the key to why
configural scoring of personality tests is predictive: personality and job
performance are not composed of only discrete components, both are
multifarious syndromes with higher level factors. Research on the MMPI
supports this view and has shown the efficacy of using configural scoring forpredicting a number of psychiatric syndromes.
Barrick and Mount (2003) state that “it is time to examine the multivariate
relation among personality traits when predicting performance” (p. 214). This
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includes configural approaches to combining scales. These researchers also note
that the application of meta-analysis to this line of research is years away due to
a “dearth of primary studies” (p.216). The present research crosses this threshold
by presenting meta-analysis results for configural scoring from a sample of 19
primary sales performance validation studies. Here we will show that: a)
configural scoring of a personality test does predict job performance, and b)
configural scoring outperforms a multiple linear regression approach to
combining scales for prediction.
1.1 Personality and sales performance
Five Factor Model (FFM) personality scales are useful predictors of sales
performance, both as main effects and in configurations. Vinchur, Shippmann,
Switzer, and Roth (1998) found that the Five Factor scales of Conscientiousness (r
= .21) and Extraversion (r = .18) were primary predictors of sales performanceratings. Vinchur et al. results were consistent with those of Barrick and Mount’s
(1991) meta-analysis at the scale level. Vinchur et al. also drilled down and
found that the Potency sub dimension was the driver of the relationship between
Extraversion and ratings, and that Achievement was the driver of performance
relationships with Conscientiousness.
The Vinchur et al. (1998) study was an important step in combining the results of
sales performance validation studies conducted across the 20th century.
However, limitations include: a) potential moderators (e.g., situations or criterion
alignment) were not included in the study, and b) only main effects of
personality scales were studied, not combinations of scales. Situational factors(Tett & Burnett, 2003) and criterion alignment (Hogan & Holland, 2003), are
important moderators of personality-performance relationships. Combining
personality scales (e.g., interaction effects, configural scoring, profile analysis)
has utility over using scales in isolation (Foster, 2003) and describes how
personality scales are used in actual personnel selection settings.
Warr, Bartram, and Martin (2005) addressed these issues regarding sales
performance research to some degree with three small-sample studies. Although
positive main effects were found for Openness to Experience and
Conscientiousness, and significant negative main effects for Agreeableness, no
significant effects were found for situation (i.e., type of sales organization) as amoderator, or for combinations of scales (i.e., interactions).
In this paper, we extend the FFM personality-sales performance research by
demonstrating that: a) there are important moderators of personality-sales
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performance relationships (i.e., criterion alignment), and b) configurations of
scales are valid predictors of performance across situations. These hypotheses
were tested using a meta-analysis of 32 studies in which the Hogan Personality
Inventory (HPI) and performance ratings for sales persons were gathered. An a
priori Sales Profile configuration of HPI scales was tested in a meta-analysis of 19
studies.
1.2 Research hypotheses
Past research indicates that the FFM Conscientiousness and Extraversion scales
are generally valid predictors of sales performance (Barrick & Mount, 1991;
Barrick, Mount, & Strauss, 1993; Barrick et al., 2002; Conte & Gintoft, 2005; Hurtz
& Donovan, 2000; Mount, Barrick, & Strauss, 1994; Stewart, 1996; Thoresen,
Bradley, Bliese, & Thoresen; 2004; Vinchur et al., 1998). Conscientiousness is
regarded as the single best personality predictor of job performance in general,and sales performance in particular (Mount et al., 1994). Conscientiousness
predicts both supervisory ratings [r = .09 (Barrick & Mount, 1991) to r = .26
(Barrick et al., 2002)] and objective sales performance [r = .17 (Vinchur et al.,
1998) and r = .21 (Barrick et al., 2002)]. Additionally, Conscientiousness predicts
both initial sales performance and growth in maintenance samples (Thoresen, et
al. 2004). Conscientiousness consistently predicts sales performance because
conscientious people are hard working, persistent, planful, and concerned about
doing a good job.
Sales-related occupations are inherently Enterprising (Holland, 1997);
consequently, it is not surprising that Extraversion, which subsumes the themesof ambition, sociability, and assertiveness, predicts sales performance.
Extraversion predicts ratings of sales performance [r = .21 (Barrick et al., 2002)] as
well as objective sales indices [r = -.01 (Barrick et al., 1993) to r = .12 (Vinchur et
al., 1998)]. Conte & Gintoft (2005) found that Extraversion was significantly
related to supervisor ratings of customer service (r = .27), sales performance (r =
.20), and overall performance (r = .25). Additionally, Extraversion was positively
related to overall sales in the Thoresen, et al. (2004) maintenance sample.
Agreeableness, distinguished by themes of being warm and pleasant, is related
to getting along and getting ahead in jobs. Hogan and Holland (2003) found that
the Interpersonal Sensitivity (i.e., Likeability) HPI scale is related to criteriacoded as getting along ( p = .23) and getting ahead ( p = .11). Additionally, Hogan
and Holland found a strong relationship ( p = .34) between Interpersonal
Sensitivity and aligned performance criteria. Thoresen, et al. (2004) found that
Agreeableness is a significant predictor of sales performance and growth in a
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transitional sample. Other researchers have found that Agreeableness is
negatively related to sales performance (e.g., Warr, Bartram, & Martin, 2005).
Finally, previous research has shown that Emotional Stability, characterized bysuch terms as being calm and composed, is also a valid predictor of job
performance, although somewhat smaller than other scales. For instance, Hogan
and Holland (2003) found that Emotional Stability (HPI Adjustment) was related
to performance criteria important across all jobs (ρ = .43). Vinchur, et al. (1998)
found a smaller relationship ( p = .10) between Emotional Stability and
managerial ratings of sales performance. Hurtz and Donovan (2000) examined
sales and customer service positions as two separate groups; they found that
Emotional Stability (ρ = .13) predicted sales performance and customer service
performance (ρ = .12).
Given these above findings, we expect:
H 1 Meta-analysis of linear relationships between personality and sales
performance will find meaningful relationships for Emotional Stability,
Conscientiousness and Agreeableness scales, and the Ambition subscale
of Extraversion.
1.3 Domain Model of Performance
Criterion alignment (Hogan & Holland, 2003) provides important moderation of
personality-performance relationships; existing research (Davies & Hogan, 2005;
Foster & Hogan, 2006) supports alignment at a competency domain level ofperformance to best satisfy fidelity-bandwidth issues (Hogan & Roberts, 1996).
In the current research, we use a Domain Model of Performance that is a
synthesis of existing performance criteria models: Leadership Skills, Work Skills,
Interpersonal Skills, and Intrapersonal Skills (Hogan & Warrenfeltz, 2003;
Warrenfeltz, 1995) (Table 1.1). This model provides a useful taxonomy for all
performance criteria—regardless of occupation or status. The intrapersonal,
interpersonal, and work skills competency domains mirror Strupp’s (1986)
tripartite model of human functioning (cf. intrapersonal functioning, or getting
along with ones self; interpersonal functioning, or getting along with others; and
personal productivity, or getting things done). The leadership domain is added
to the present model to include functioning specific to organizational life. Basedon the moderating effects of aligning predictors with criteria using the Domain
Model, we propose:
H 2 Criterion alignment of sales performance will serve as a moderator in
a meta-analysis of HPI linear relationships.
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Table 1.1. Domain Model of Job Performance, Example Competencies, and Personality
Measures
Metaconcept Domain Example Competency FFM Measurement
Leadership
Achievement
Building Teams
Business Acumen
Decision Making
Delegation
Employee Development
Initiative
Leadership
Managing Performance
Resource Management
Surgency/Extraversion
Getting Ahead
Technical
Analysis
Creating Knowledge
Decision Making
Political Awareness
Presentation Skills
Problem Solving
Safety
Technical Skill
Training Performance
Written Communication
Openness to Experience
Interpersonal
Building Relationships
Communication
Consultative Skills
CooperatingInfluence
Interpersonal Skill
Organizational Citizenship
Service Orientation
Teamwork
Trustworthiness
Agreeableness
Surgency/Extraversion
Getting Along
Intrapersonal
Dependability
Detail Orientation
Flexibility
Following Procedures
Integrity
Planning
RespectRisk Taking
Stress Tolerance
Work Attitude
Conscientiousness
Emotional Stability
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There are several methods for combining validities across predictors into a single
coefficient representing the link between a predictor battery and job
performance; these are reviewed by Scherbaum (2005). Peterson, Wise, Arabian,
& Hoffman (2001) specifically discussed various weighting options for predictor
batteries and find little difference in outcomes across methods. One of the most
popular methods in selection research is a compensatory model using optimal
weights in a multiple regression equation; we test the efficacy of this method
here:
H 3 A configuration of multiple HPI scales in a multiple linear regression
model will have incremental validity over single HPI scales.
Existing research includes no studies of a configural approach to combining FFM
personality scales for predicting job performance. Some research exists using
MMPI scale configurations (e.g., Matthiesen & Einarsen, 2001) for variouspredictive purposes, but the present study will be the first to test the validity of a
FFM measure configural profile for predicting job performance. Thus, we
propose:
H 4 Meta-analysis of an apriori configuration of multiple HPI scales in a
cutoff score profile will be significantly related to sales performance.
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2 – METHOD
In order to test our research hypotheses, we created two databases of criterion-
related validity studies for sales jobs from the Hogan Archive for meta-analysis.The first database included study level data and was used to develop a
predictive sales profile based on a meta-analysis of single HPI scale validities
from these previous studies. From these results (i.e., meta-analysis for seven HPI
scales and sales performance) we identified the valid HPI scales to include in our
predictive sales profile. To conduct the configural scoring used to create and
validate the predictive sales profile, we created a second database of individual
level HPI predictor and criterion-related validity data coded by study. The HPI
predictor data was configurally scored into the sales profile for all individuals in
the database. This profile was then meta-analyzed across all studies in the
disaggregate database.
2.1 Meta-analytic database
Search for Studies
For the meta-analytic database, computer-based and manual searches for
published and unpublished empirical studies investigating the relationship
between the Hogan Personality Inventory (HPI), as the predictor, and supervisor
ratings of job performance, as the criterion, was conducted for all sales jobs. The
HPI is composed of seven scales and maps onto the FFM model. The HPI is
distinguished from the FFM because it breaks out Ambition from Extraversion as
a separate main scale, and Learning Approach and Inquisitive from theOpenness scale (Figure 1). R. Hogan and Hogan (1995) point out that the HPI
measures characteristics that facilitate or inhibit a person’s ability to get along
with others and achieve job-relevant goals—i.e., get ahead. The HPI relates
closely to other Big Five measures. Sales was selected as the job to demonstrate a
configural scoring system because sales jobs are common in the workplace,
currently making up 10.6% of all jobs in the US economy, second only to office
and administrative support (17.6%) jobs (BLS, 2004). The meta-analysis search
involved a review of the Hogan Research Archive, and a search of the PsyINFO
(1970-2005) database and Business Source Elite (1970-2005). In order to be
included in the meta-analytic database, an empirical study had to meet a number
of criteria.
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Figure 2.1 Relation between HPI and Five Factor Models.
Note. Median correlation coefficients summarize HPI relationswith the NEO PI -R (Goldberg, 2000), Goldberg’s (1992) Big-FiveMarkers ( R. Hogan & Hogan, 1995), Personal CharacteristicsInventory (Mount & Barrick, 2001), and the Inventario dePersonalidad de Cinco Factores (Salgado & Moscoso, 1999). Theranges of correlates are as follows: Adjustment/ EmotionalStability/ Neuroticism (.66 to .81); Ambition/Extraversion/ Surgency (.39 to .60); Sociability/ Extraversion/ Surgency (.44 to
.64); Interpersonal Sensitivity / Agreeableness (.22 to .61);Prudence/ Conscientiousness (.36 to .59); Inquisitive/ Openness/ Intellect (.33 to .69); Learning Approach/ Openness/ Intellect (.05to .35).
Adjustment
Ambition
Sociability
Interpersonal
Sensitivity
Prudence
Inquisitive
Neuroticism
Extraversion
Agreeableness
Conscientiousness
Openness
Median r = .56
Median r = .62
Median r= .50
Median r = .51
Median r = .57
Median r = .30
Median r = .73
Learning A pproach
First, the sales job had to fit the sales job criteria as defined by the O*NET
(O*NET, 1999) code 41-, Sales and Related. We used O*NET codes as the
benchmark to define the sales job because it is a broad, widely-used system for
defining jobs. The various O*NET codes and titles under this broad category
appear in Table 2.1. Sales jobs with such O*NET codes as 41-1011.00 and 41-
2011.00 were not included in this study because they are not core sales jobs. The
job titles with these O*NET codes included Supervisors/Managers of RetailWorkers and Cashiers. Second, we included studies that reported correlations,
means, standard deviations, and sample size. Third, the study had to be a
criterion-related validity study that included the HPI and supervisory ratings of
job performance. Due to the nature of the current study, and the difficulty in
comparison across studies, objective sales criteria were not included. The search
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resulted in a total of 26 criterion-related validity studies to be included in the
meta-analysis.
Table 2.1 O*NET Sales Jobs
Sales Job Titles O*NET Code
Retail Salespersons 41-2021.00 and 41-3031.00
Sales Agents, Financial Services 41-3031.02
Sales Representatives, Services, All other 41-3099.99
Sales Representatives, Wholesale and Manufacturing 41-4011.00
Sales Representative, Agricultural 41-4011.0
Sales Representative, Chemical and Pharmaceutical 41-4011.02
Sales Representative, Mechanical Equipment and Supplies 41-4011.04
Sales Representative, Medical 41-4011.05Sales Representative, Wholesale and Manufacturing,Except Technical and Scientific Products
41-4012.00
Telemarketers 41-9041.00, 41-4011.03, 41-4011.06, and 41-2031.00
Procedure
Once the search was complete, two researchers worked independently to code
every study. Each researcher was a given a list of variables for coding the
empirical studies. The studies were coded for O*NET codes, sales level, jobcomplexity, type of job analysis, type of industry, and type of criteria. The two
researchers coded the studies independently and then, once ratings were
complete, the researchers met to reach consensus so that each study was
assigned only one code per variable. The criteria used to assess job performance
in the sales jobs were sorted into the four broader job performance domains of
the Domain Model (Davies & Hogan, 2005).
Criteria were sorted into performance domains independently by two trained
I/O psychologists and then agreement was met through consensus. In many
cases there was more than one domain a performance criterion could be assigned
to. The raters were instructed to rank order the criteria that best fit the domain,and during the consensus meetings, they reached agreement as to which criteria
best fit a particular domain. To be included in the meta-analysis a study had to
have at least one of its criteria sorted into a performance domain. All 26 studies
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had at least one criterion that was sorted into a performance domain and all were
included in the meta-analysis.
The sales jobs in the meta-analytic database had sample sizes ranging from 15 to416. The types of sales job ranged from Sales Representatives/Agents to
Account Executives and Telephone Sales Representatives. The types of
industries represented in the meta-analysis were Financial, Retail,
Transportation, Agriculture, Construction, Communication, Leisure and
Hospitality, Pharmaceutical, and Utilities. Dates of the criteria-related validity
studies ranged from January, 1995 to February, 2004. After the studies had been
coded and placed into a single database, the procedures used by Hunter and
Schmidt (1990) for meta-analysis were applied. As appropriate, the studies were
corrected for predictor and criterion unreliability and range restriction in the HPI
scores.
HPI Range Restriction
All of the studies included in the meta-analysis were concurrent criterion-related
validity designs. As such, range restriction on the HPI was expected. The
studies had a wide range of standard deviations, some higher and some lower,
so corrections were made using the standard deviations reported in the HPI
technical manual (R. Hogan & Hogan, 1995). The correction for range restriction
followed the procedures used by Hunter and Schmidt (1990).
Predictor and Criterion Reliability
Predictor reliability was based on the reported test-retest reliability estimates
detailed in R. Hogan and Hogan (1995). Specifically, the reliabilities used were
.89 for Adjustment, .86 for Ambition, .83 for Sociability, .71 for Interpersonal
Sensitivity, .78 for Prudence, .78 for Inquisitive, and .75 for Learning Approach.
The criterion reliability corrects for the error (unsystematic) in supervisor ratings.
None of the studies reported reliability estimates for the criteria ratings; the
correction of .52, recommended by Viswesvaran, Owens, & Schmidt (1996), was
applied.
2.2 Aggregated Database
Procedure
The aggregated database was built using all criterion-related validity sales
studies in the Hogan archive. The archive was searched for criterion-related
validity studies that included sales as a component of the job definition, or had a
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sales criterion. Once all sales studies were found, they were assigned an O*NET
code. The O*NET code was assigned based on overlap of the definition in the
sales job in the archive with the definition of the O*NET job code. Two trained
I/O psychologists reached consensus on the assignment of sales jobs to sales
O*NET codes. Only jobs that could be assigned an O*NET sales code were
included in the aggregated database.
To organize the individual databases prior to aggregation and analyses, two
researchers coded industry, sales level, job complexity, and job analysis variables
into each individual database. Using the same procedures as in the meta-
analysis, the job performance criteria for the sales jobs in the archive were sorted
into the four domains of performance and overall job performance. Since the
HPI included both the short and long form, and the criteria were on different
scales, z-scores were computed for the HPI scales and the domain performance
criteria for each study. These data were then combined to form an overalldatabase. There were K = 18 studies in the database with a total N of 1467. The
sample size within study ranged from 15 to 291. The industries represented in
the aggregated database included Agriculture, Construction, Financial, Leisure
and Hospitality, and Transportation.
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3 – RESULTS
The meta-analysis and aggregated database results are reported in Table 3.1 and
show that Adjustment, Ambition, Interpersonal Sensitivity, and Prudence are allsignificantly related to overall job performance, supporting H 1. The pattern of
results in Tables 3.1 and 3.2 support criterion alignment as a moderator. For
instance, Adjustment, Ambition, Interpersonal Sensitivity, and Prudence were
differentially predictive across the four domains of performance. The meta-
analytic results (Table 3.2) and the correlations computed based on the
aggregated database (Table 3.1) both support criterion alignment and H 2. Based
on the meta-analytic and aggregated database results, a profile for the sales job
was established.
A combination of the Adjustment, Ambition, Interpersonal Sensitivity, and
Prudence scales was determined as an optimal set for predicting salesperformance. These scales were entered into a multiple linear regression model
and tested in the aggregated database in a compensatory scoring model. The
results, (Tables 3.3 and 3.4) do not support H 3. Across the domain levels of job
performance, the multiple R was not larger than any of the single scale validities.
The combination of the Adjustment, Ambition, Interpersonal Sensitivity, and
Prudence scales was then used in a configural scoring approach. Cuts were set
on these four scales according to importance to sales jobs based on job analysis
results from the Hogan Research Archive and the pass rates frequently used in
selection practice. Cases were labeled as 1 (matching profile) or 0 (not matching
profile). Correlations were calculated between the profile and the five domainsof performance, which appear in Table 3.5. These correlations were then meta-
analyzed following Hunter & Schmidt’s (1990) procedure. Corrections were
made for dichotomous variables, restriction in range, and unreliability in
predictor and criteria. The results of the meta-analysis appear in Table 3.6. As
illustrated in the table the sales profile is predictive of all five domains of
performance. This supports the validity of a configuration approach (i.e., Sales
Profile) for predicting performance in sales jobs and provides support for H 4.
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Table 3.1 Correlation (uncorrected) of HPI Scales to Domain
LeadershipInterpersonal
SkillsIntrapersonal
Skills Work SkillsOverall Job
Performance
Adjustment .08 .06 .15 .08 .09 p-value .01 .05 .00 .01 .00
N 1133 1157 1097 1164 1513
Upper CI .02 .00 .09 .02 .04
Lower CI .14 .12 .21 .14 .14
Ambition .17 .09 .10 .24 .17
p-value .00 .00 .00 .00 .00N 1130 1154 1094 1161 1510
Upper CI .11 .03 .04 .19 .12
Lower CI .23 .15 .16 .29 .22
Sociability .08 .07 .02 .13 .07
p-value .01 .01 .61 .00 .00
N 1132 1156 1096 1163 1512Upper CI .02 .01 -.04 .07 .02
Lower CI .14 .13 .08 .19 .12
InterpersonalSensitivity
.08 .11 .15 .08 .09
p-value .00 .00 .00 .00 .00N 1126 1150 1090 1157 1506
Upper CI .02 .05 .09 .02 .04
Lower CI .14 .17 .21 .14 .14
Prudence .07 .01 .09 .00 .05
p-value .03 .79 .00 .95 .06N 1132 1156 1096 1163 1512
Upper CI .01 -.05 .03 -.06 .00Lower CI .13 .07 .15 .06 .10
Inquisitive .01 .01 -.01 .07 .02
p-value .65 .67 .86 .02 .41N 1132 1156 1096 1163 1512
Upper CI -.05 -.05 -.07 .01 -.03
Lower CI .07 .07 .05 .13 .07
LearningApproach
.03 .04 .05 .08 .06
p-value .39 .22 .08 .01 .02N 1134 1158 1098 1165 1514
Upper CI -.03 -.02 -.01 .02 .01
Lower CI .09 .10 .11 .14 .11
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Table 3.2 Meta-analytic results of HPI to Domain levels of performance
Domain Model of
Performance K N ADJ AMB SOC INP PRU INQ LRN
Interpersonal Skills 16 1656 .14 .22 .10 .11 .04 .01 .06
CI L .05 .13 .03 .06 -.07 -.07 -.03
CI H .23 .32 .18 .16 .15 .09 .14
Intrapersonal Skills 13 1383 .24 .29 .10 .13 .14 .06 .15
CI L .17 .18 .02 .08 .03 -.03 .05
CI H .32 .40 .18 .19 .24 .16 .25
Leadership 12 1123 .16 .37 .15 .19 .11 .04 .11
CI L .04 .24 .05 .07 -.01 -.09 .01
CI H .28 .50 .26 .30 .24 .18 .21
Work Skills 18 1577 .14 .35 .16 .10 -.05 .08 .14CI L .05 .27 .09 .03 .08 .00 .05
CI H .28 .44 .23 .17 .21 .17 .23
Overall Job Performance 26 1828 .14 .29 .12 .08 -.05 .00 .10
CI L .06 .20 .04 .03 .03 -.01 .02
CI H .21 .38 .23 .14 .11 .01 .18
Note. K = number of studies; N = sample size; ADJ = Adjustment,; AMB = Ambition; SOC =Sociability; INP = Interpersonal Sensitivity; PRU = Prudence; INQ = Inquisitive; LRN = LearningApproach; CI = Confidence Interval; L = Lower bound (5%); H = Upper bound (95%).
Table 3.3 Domain performance regressed on single scales
LeadershipInterpersonal
SkillsIntrapersonal
Skills WorkSkills
Overall JobPerformance
β β β β β
Adjustment -.02 .01 .09 -.03 .00
Ambition .15 .07 .03 .25 .14
InterpersonalSensitivity
.02 .11 .11 .05 .06
Prudence .06 .01 .01 -.02 .03
Multiple R .17 .13 .18 .25 .18
R2 .03 .02 .03 .06 .03
Significance .000 .000 .000 .000 .000
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Table 3.4 Domain performance regressed on Sales Profile and incremental validity
LeadershipInterpersonal
SkillsIntrapersonal
Skills WorkSkills
Overall JobPerformance
β β β β β
SalesProfile
.14 .12 .12 .17 .13
R2 .02 .01 .01 .03 .02
R2 Change*
.001 .00 .001 .001 .001
Sig. ofchange*
.192 .581 .320 .233 .180
Note. *this is the R2 Change of the Sales Profile after single scales.
Table 3.5 Correlation (uncorrected) of HPI Meta-profile to Domain
Leadership
InterpersonalSkill
IntrapersonalSkill
WorkSkill
Overall JobPerformance
Sales Profile .14 .11 .12 .17 .13
p-value .00 .00 .00 .00 .00
N 1087 1111 1051 1118 1467
Upper CI .08 .05 .06 .11 .08
Lower CI .20 .17 .18 23 .18
Table 3.6 Meta-analysis of Sales Profile to Domain Levels of Performance.
K N LED K N INT K N INP K N WK K N OVER
SalesProfile
11 1025 .29 11 1050 .26 9 991 .23 12 1061 .33 19 1402 .32
CI – L .17 .14 .12 .22 .22
CI – H .41 .38 .34 .45 .42
Note. K = number of studies; N = sample size; LED = Leadership; INT = Interpersonal Skills; INP =Intrapersonal Skills; WK = Work Skills; OVER = Overall Job Performance; CI = Confidence Interval; L = Lowerbound (95%); H = Upper bound (95%).
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4 – DISCUSSION
This research provides support for three very important propositions: a)
relationships between FFM personality scales and sales performance aremeaningfully large and generalize quite well across situations, b) predictor-
criterion alignment using the Domain Model moderates FFM personality and
sales performance relationships, and most importantly, c) a configural approach
to scoring FFM personality scales is predictive of performance and generalizes
across situations.
This meta-analysis of sales studies extends previous work on at least three
dimensions. First, we included only studies from a single FFM measure (i.e.,
HPI) provides more clarity of construct definition and straightforward
interpretation of the results. The Vinchur et al. (1998) study included a number
of different personality measures coded to a common FFM framework. A largecriticism frequently targeted at personality assessment in the selection context is
the use of psychometrically inadequate personality instruments (e.g., Murphy &
Dzieweczynski, 2005) and the difficulty this brings to interpreting meta-analytic
estimates of validity. The present study addressed this issue by using a single
personality inventory designed to measure normal personality in the workplace
with an established research background. Initial evidence from this study
indicates that future meta-analyses should focus on well-established, researched,
normal workplace personality assessments.
Second, we included predictor-criterion alignment as a moderator; moderators
have not been included in previous meta-analyses of personality and salesperformance. We found that predictor-criterion alignment is important
theoretically and practically. Theoretically, a common sense argument can be
clearly made for the idea that if you are measuring Ambition, the desire to get
ahead, on the predictor side it should map conceptually to Leadership,
demonstrating the ability to get ahead, on the criteria side (e.g., Hogan and
Holland, 2004). Further, the variables are matched at the appropriate bandwidth
(cf., Rothstein & Jelly, 2004) – both broad. Practically, in an applied setting,
human resource decision-makers can see the link, when defined appropriately,
between conceptually similar variables.
Finally, we examined the predictive validity of single personality scales, multiplepersonality scales in a compensatory scoring model, and multiple personality
scales in a configural scoring model. No previous meta-analysis of personality
and sales performance has included these components.
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Results from the meta-analysis of single HPI scales demonstrated that there are
meaningful relationships in the population between the HPI scales and sales job
performance. In particular, Adjustment, Ambition, Prudence, and Interpersonal
Sensitivity have strong, positive, relationships with job performance. These
findings extend and affirm Vinchur, et al. (1998) findings. Importantly, this
analysis indicated the valid scales to include in the predictive profile for sales
performance.
The predictive profile for sales performance consisted of a combination of the
Adjustment, Ambition, Interpersonal Sensitivity, and Prudence scales. An
individual who meets this profile will be described as capable and driven
towards established goals, but is capable of handling stressors, such as frequent
sales “no’s”, and gets along well with others. The predictive profile for sales
performance maps, as expected, onto important requirements of sales jobs. For
instance, sales jobs require persistence and drive and the ability to interact wellwith others.
Interestingly, a compensatory model (i.e., MLR) that included the scales
identified as valid for predicting sales success (i.e., Adjustment, Ambition,
Interpersonal Sensitivity, and Prudence) did not provide significant incremental
validity as a set over the single scale validities. Although these results were not
expected, there are two possible reasons for this finding. One explanation may
be that the non-significant findings result from overlapping variance because
model is made up of oblique scales. An alternative explanation, not totally
independent from the first, is that moving from the scale level to compensatory
model necessitates a loss of information. This loss of information shrinksvariance and possibly attenuates findings.
Clearly the most significant result of the study is the meta-analytic support
evidenced for the predictive profile for sales performance. The significant meta-
analytic finding is important because it is the first published finding of a
configural scoring system with real-world job performance data. As suggested
by Meehl (1950) and later endorsed by others (e.g., Dawes, 1979), this finding is
important because it uses statistical (meta-analysis) and local validation evidence
to establish the critical scales and appropriate cuts to build the Sales Profile.
A limitation of this study concerns sample size and number of studies. First,increased sample size and number of studies included in the meta-analysis
would have increased confidence in the present findings. Given the nature of
this study, only one personality inventory was necessary, and desired, to assess
the relationship between personality and job performance. As a result, only a
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smaller sample of studies was available for meta-analysis. Despite this, the
confidence intervals around the effects indicate the stability of the current
findings.
There are a number of important implications of this current study to research
and practice in selection. With respect to applied settings, this research indicates
that applying a configural approach to scoring is a practical option. In fact, the
results suggest that measurement of a worker job profile for a particular job may
be more accurate than using the traditional linear approach. The need to further
understand the configural approach to scoring is focal because recent critics
suggest, and may correctly do so, that there is not a perfect linear relationship
between a personality variable and job performance criteria (Arthur, Woehr, &
Graziano, 2000; Murphy & Dzieweczynski, 2005). Accordingly, these results
indicate that a predictive profile for sales performance is a more accurate
representation of test validity than published single scale linear validities.Further, this study also points to the importance of conceptually mapping
predictor to criteria for appropriate bandwidth considerations and theoretical
overlap between predictors and criteria.
Future application of this research could focus on the ability of these findings to
generalize to other jobs. For instance, following similar procedures outline in the
present, would a predictive profile for manager performance differ from the
predictive profile for sales performance, and if so, in what ways? Obviously, it is
critical that these two sets of configural scoring systems differ because the nature
of these two jobs is very distinct. From the current research perspective, there is
reason to suspect that a distinct configural scoring system could, and would,emerge from a meta-analysis of manager jobs.
In regard to research, this current study proposes that further research into the
relationship between personality and job performance requires the use of a single
measure and/or equivalent quality measures prior to aggregating. This research
also configured a profile of normal personality in a workplace sample. Previous
research into configural scoring mostly focused on non-normal personality
assessment. Additionally, this study is the first to supply strong evidence for the
potential of using a configural scoring approach to building worker job profiles.
Accordingly, research can use the findings of this study to extend the research
into appropriate methods for building configural scoring methods, as well asapplicable methods for analyzing configural profiles.
While serving to open the door on the importance of configural scoring in
personnel assessment and selection, future research is needed. In particular,
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research is needed to develop best practices for building a configural profile.
While expert-based methods have consistently demonstrated weaker results
(e.g., Dawes, 1979), other methods, besides the meta-analytic approach used
here, may be available to establish worker job profiles. Along similar lines, a
configural approach to analyzing data is needed in selection designs where cut-
off scores are applied. Methods, such as those pursued by von Eye and
colleagues, may need further exploration. In fact, the configural frequency
analysis approach to determining patterns in data to build configurations may be
a fruitful avenue (e.g., von Eye, 2002). Additional areas for fruitful future
research may be to apply similar methods used here to other jobs to ensure
adequate generalizability. Interestingly, research may be needed on mixed
measurement approaches where batteries of tests are used to select personnel to
build preferred profiles.
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REFERENCES
Adler, S. (1996). Personality and work behavior: Exploring the linkages. Applied
Psychology: An International Review, Special issue: Work andPersonality, 45, 207-224.
Arthur, Jr., W., Woehr, D.J., & Graziano, W.G. (2000). Personality testing in
employment settings: Problems and issues in the application of typical
selection practices. Personnel Review, 30, 657-676.
Barrick, M.R., & Mount, M.K. (1991). The Big Five personality dimensions and
job performance: A meta-analysis. Personnel Psychology, 44, 1 – 26.
Barrick, M.R., & Mount, M.K. (2003). Impact of meta-analysis methods on
understanding personality-performance relations. In Kevin Murphy (ed.)Validity generalization: A critical review (pp. 197-221). Mahwah, NJ:
Lawrence Erlbaum.
Barrick, M.R., Mount, M.K., & Strauss, J.P. (1993). Conscientiousness and
performance of sales representatives: Test of the mediating effects of goal
setting. Journal of Applied Psychology, 78, 715-722.
BLS. (2004). Occupational Employment and Wages.
http://www.bls.gov/news.release/ocwage.nr0.htm
Conte, J.M., & Gintoft, J.N. (2005). Polychronicity, Big Five personality
dimensions, and sales performance. Human Performance, 18, 427-444.
Davies, S. & Hogan, J.C. (2005). Recombinant Personality Measures for
Predicting Leadership Competence. Society for Industrial and
Organizational Psychologists Annual Convention, Los Angeles, CA.
Dawes, R.M. (1979). The robust beauty of improper linear models in decision-
making. American Psychologist, 34, 571-582.
Dunnette, M.D. (1966). Personnel selection and placement. Belmont, CA:
Wadsworth Publishing Company.
Foster, J. (2003). An investigation of statistical alternatives for identifying
relationships between personality variables and job performance.
Dissertation Abstracts International: Section B: The Sciences and
Engineering, 65, 3206-3292.
8/21/2019 NEO Sales 1
http://slidepdf.com/reader/full/neo-sales-1 22/24
22Copyright© Hogan Assessment Systems, Inc. 2006. All rights reserved.
Holland, J.L. (1997). Making vocational choices: A theory of vocational
personalities and work environments (3rd ed.). Odessa, FL: Psychological
Assessment Resources.
Hogan, R., & Hogan, J. (1995). Hogan Personality Inventory manual (2nd ed.).
Tulsa, OK: Hogan Assessment Systems.
Hogan, J.C., & Holland, B. (2003). Using theory to evaluate personality and job-
performance relations: A socioanalytic perspective. Journal of Applied
Psychology ,88, 100 - 112.
Hogan, J., & Roberts, B. (1996). Issues and non-issues in the fidelity/bandwidth
tradeoff. Journal of Organizational Behavior, 17, 627-637.
Hunter, J.E. & Schmidt, F.L. (1990). Methods of meta-analysis: Correcting error inand bias in research findings. Newbury Park, CA: Sage.
Hurtz, G.M., & Donovan, J.J. (2000). Personality and job performance: The Big
Five revisited. Journal of Applied Psychology, 85, 869-879.
Jackson, D.N., Wroblewski, V.R., & Ashton, M.C. (2000). The impact of faking on
employment tests: Does forced choice offer a solution? Human
Performance, 13, 371-388.
Jenkins, C.A. (2002). Improving the accuracy of personality measurement
through profile-level judgments. Dissertation Abstracts International:
Section B: The Sciences and Engineering, 62, 3408-3495.
Matthiesen, S.B., & Einarsen, S. (2001). MMPI-2 configurations among victims of
bullying at work. European Journal of Work and Organizational Psychology, 10,
467-484.
Meehl, P. (1950). Configural scoring. Journal of Consulting Psychology, 14, 165-
171.
McClelland, D.C., & Boyatzis, R.E. (1982). Leadership motive pattern and long-
term success in management. Journal of Applied Psychology, 67, 737-743.
McCrae R.R., & Costa P.T., Jr. (1995) Trait explanations in personality
psychology. European Journal of Personality, 9, 231-252.
8/21/2019 NEO Sales 1
http://slidepdf.com/reader/full/neo-sales-1 23/24
23Copyright© Hogan Assessment Systems, Inc. 2006. All rights reserved.
Mount, M.K., Barrick, M.R., & Strauss, J.P. (1994). Validity of observer ratings of
the big five personality factors. Journal of Applied Psychology, 79, 272-
280.
Murphy, K.R., & Dzieweczynski, J.L. (2005). Why don’t measures of broad
dimensions of personality perform better as predictors of job
performance? Human performance, 18, 343-357.
National O*NET Consortium. (1999). Occupational Information Network
(O*NET) Online [On-line database]. Available:
http://online.onetcenter.org/.
Peterson, N.G., Wise, L.L., Arabian, J., & Hoffman, R.G. (2001). Synthetic
validation and validity generalization: When empirical validation is not
possible. In John P. Campbell & Deirdre J. Knapp (eds.) Exploring thelimits in personnel selection and classification (pp. 411-451). Mahwah, NJ:
Lawrence Erlbaum.
Rothstein, M.G., & Jelley, R.B. (2003). The challenge of aggregating studies of
personality. In Kevin Murphy (ed.), Validity Generalization: A Critical
Review (pp.223-262). Mahwah, NJ: Lawrence Erlbaum.
Scherbaum, C.A. (2005). Synthetic validity: Past, present, and future. Personnel
Psychology, 58, 481-515.
Stewart, G.L. (1996). Reward structure as a moderator of the relationshipbetween extraversion and sales performance. Journal of Applied
Psychology, 81, 619-627.
Strupp, H.H. (1996). The tripartite model and the Consumer Reports study.
American Psychologist. Special issue: Outcome assessment of
psychotherapy, 51, 1017-1024.
Tenopyr, M.L. (2002). Theory versus reality: Evaluation of g in the workplace.
Human Performance, 15, 107-122.
Tett, R. P., Burnett, D. D., (2003). A personality trait-based interactionist model of
job performance. Journal of Applied Psychology, 88, 500-517.
Thoresen, C. J., Bradley, J. C., Bliese, P. D., Thoresen, J. D. (2004). The Big Five
Personality Traits and Individual Job Performance Growth Trajectories in
8/21/2019 NEO Sales 1
http://slidepdf.com/reader/full/neo-sales-1 24/24
24
Maintenance and Transitional Job Stages. Journal of Applied Psychology.
89, 835-853.
Vinchur, A. J., Schippmann, J. S., Switzer, F. S. III, Roth, P. L. (1998). A meta-analytic review of predictors of job performance for salespeople. Journal
of Applied Psychology. 83, 586-597.
Viswesvaran, C., Ones, D.S., & Schmidt, F.L. (1996). Comparative analysis of the
reliability of job performance ratings. Journal of Applied Psychology, 81,
557-574.
Von Eye, A. (2002). Configural frequency analysis: Methods, models, and
applications. Mahwah, NJ: Lawrence Erlbaum.
Warr, P., Bartram, D., & Martin, T. (2005). Personality and sales performance:Situational variation and interactions between traits. International Journal
of Selection and Assessment, 13, 87-91.
Waters, S. & Sackett, P. (2006). On the possibility of using configural scoring to
enhance prediction. Poster presented at the Society for Industrial and
Organizational Psychologists Annual Convention, Dallas, TX.