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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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2Copyright© Hogan Assessment Systems, Inc. 2006. All rights reserved.

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


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


SkillsIntrapersonal

Skills WorkSkills


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


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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