Received: 6March 2019 Revised: 15May 2020 Accepted: 18May 2020

DOI: 10.1111/peps.12411

OR I G I N A L A RT I C L E

Vocational interests, gender, and job performance:Two person–occupation cross-level interactions

SerenaWee1 Daniel A. Newman2 Q. Chelsea Song3

John A. Schinka4

1 School of Psychological Science, University of

Western Australia, Crawley,Western

Australia, Australia

2 Department of Psychology and School of

Labor & Employment Relations, University of

Illinois at Urbana–Champaign, Champaign,

Illinois

3 Department of Psychological Sciences,

Purdue University,West Lafayette, Indiana

4 School of Aging Studies, University of South

Florida, Tampa, Florida

Correspondence

SerenaWee, School ofPsychological Science,

University ofWesternAustralia, 35Stirling

Highway,Crawley,WA6009,Australia.

Email: serena.wee@uwa.edu.au

Anearlier versionof this articlewaspresented

at theAnnualMeetingof theSociety for Indus-

trial andOrganizational Psychology,Orlando,

Florida,April 2017.

Abstract

Vocational interest theories imply a person–occupation

cross-level interaction effect (e.g., artistic interests predict

job performance better in artistic occupations), which has

rarely if ever been tested as such. Using a largemilitary sam-

ple, we find person–occupation interest congruence effects are

supported: (a) on core technical job performance for six of

eight interest dimensions, and (b) on job performance rat-

ings for structural/machines and rugged outdoors (i.e., Real-

istic) interests. Another cross-level interaction involves the

person–occupation gender congruence effect. Our data also

confirm the job performance gap favors men when in male-

dominated occupations, but favors women when in gender-

balanced occupations. Due to strong overlap between voca-

tional interests and gender, we conduct a critical test of

whether person–occupation interest congruence might be

due to person–occupation gender congruence. In only two

of six cases (i.e., rugged outdoors and administrative inter-

ests), did the person–occupation interest congruence effect

disappear after controlling for the person–occupation gen-

der congruence effect; the gender congruence effect also

remained significant after controlling for the interest con-

gruence effect. Consequently, the two cross-level inter-

actions on job performance (for vocational interests and

for gender) appear to represent distinct effects. In a sec-

ond, service organization sample, the person–occupation

interest congruence effect (for Realistic interests) on job

Personnel Psychology. 2020;1–46. © 2020Wiley Periodicals, Inc. 1wileyonlinelibrary.com/journal/peps

2 WEE ET AL.

performance ratings and the person–occupation gender

congruence effect were both replicated.

KEYWORDS

vocational interests, job performance, gender, mutilevel model,person–situation interaction, tokenism

1 INTRODUCTION

Following a three-decade hiatus of vocational interest research in the field of industrial–organizational psychology,

recent reviews have announced the return of vocational interests as a major topic area in the field (Ryan & Ployhart,

2014; Sackett, Lievens, Van Iddekinge, & Kuncel, 2017). This enthusiasm has been fueled in part by meta-analyses

examining the validity of vocational interest measures for predicting job performance (Nye, Su, Rounds, & Drasgow,

2012, 2017; Van Iddekinge, Roth, Putka, & Lanivich, 2011), by papers validating newmeasurementmodels and instru-

ments for vocational interests (Tay, Drasgow, Rounds, &Williams, 2009; Van Iddekinge, Putka, &Campbell, 2011), and

by papers ostensibly assessing vocational interest congruence [i.e., profile correlations predicting choice of a college

major in STEM (Le, Robbins, &Westrick, 2014) and Holland’s (1997) congruence index predicting counterproductive

work behavior (Iliescu, Ispas, Sulea, & Ilie, 2015)].

Despite the recent empirical attentiondirected towardvocational interests,weassert that oneof the corehypothe-

ses in vocational interest theory—person–occupation interest congruence—has not been formally tested. Person–

occupation interest congruence refers to a cross-level interaction effect, where the relationship between a vocational

interest and job performance depends upon the occupation-level vocational interest. Simply put, we do not know

whether Artistic interests predict job performance better in occupations full of Artistic people. Despite many past

studies and three meta-analyses published on vocational interests and job performance, we see no theoretically con-

sistent multilevel test of the vocational interest congruence hypothesis. Although Van Iddekinge et al.’s (2011) meta-

analysis coded job relevance as a moderator of the bivariate validity of vocational interests (using different interest

measures across primary studies), no past primary research has appropriately tested the multilevel interaction effect

(Ostroff&Schulte, 2007; Su,Murdock,&Rounds, 2015). In providing sucha test, the current studyaddresses an impor-

tant practical problem by clarifying when vocational interests will predict job performance.

Overall, we attempt to make three contributions toward understanding how vocational interests might inform

personnel selection. First, we provide a novel test of person–occupation interest congruence: that is, a person voca-

tional interest × occupation vocational interest cross-level interaction effect on job performance. Second, we test a

person–occupation gender congruence effect (the interaction between a person’s gender and the gender composition

of the occupation predicting job performance; cf. Joshi, Son, & Roh, 2015; Sackett, DuBois, & Noe, 1991). Impor-

tantly, because there is a well-known association between vocational interests and gender (Su, Rounds, & Armstrong,

2009), we then further control for the person–occupation gender congruence effect when estimating the hypoth-

esized person–occupation interest congruence effect. That is, we disambiguate the effect of interest congruence

from the effect of gender congruence, when predicting job performance. Third, we examine both the interest con-

gruence and gender congruence effects using multiple measures of job performance, demonstrating the gender con-

gruence effect for knowledge- and skill-based measures of job performance (in addition to performance ratings).

This may suggest the gender congruence effect on job performance is in part a pipeline/gravitational issue, and can-

not be completely explained by stereotype-based rating bias (i.e., gender bias; see Eagly & Karau, 2002; Heilman,

1983).

By specifying the person–occupation interest congruence and person–occupation gender congruence effects

as multilevel (cross-level) interactions, we emphasize the importance/centrality of occupational context (Dierdorff,

WEE ET AL. 3

2019). That is, investigating interest congruence and gender congruence as cross-level interaction effects properly

contextualizes two classic individual-level parameters: (a) the relationship between vocational interests and job per-

formance and (b) the relationship between gender and job performance.

1.1 Vocational interest theory

Vocational interests refer to relatively stable individual differences in people’s preferences for certain types of work

activities and work environments (see Holland, 1997; Kuder, 1977; Rounds, Shubsachs, Dawis, & Lofquist, 1978;

Strong, 1943). Vocational interests can be distinguished from personality traits (Mount, Barrick, Scullen, & Rounds,

2005), although vocational interests have been found to exhibit greater temporal stability than personality traits do

(Low, Yoon, Roberts, & Rounds, 2005). Although some disagreement remains as to the appropriate factor structure

for describing vocational interests (e.g., Rounds, 1995; Tay, Su, & Rounds, 2011; Tinsley, 2000), researchers do agree

that vocational interests are multidimensional (rather than unidimensional). For example, Holland’s (1959, 1997) the-

ory of vocational interests, the most well-known and empirically tested theory of vocational interests, postulates six

interest dimensions: Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (i.e., RIASEC). Individu-

als with Realistic interests enjoy hands-on activities and/or working outdoors. Individuals with Investigative interests

enjoy intellectual and scholarly activities such as thinking through problems and trying to organize and understand

the world. Individuals with Artistic interests enjoy activities that allow creative expression such as writing, painting, or

dancing. Individuals with Social interests enjoy activities characterized by helping, teaching, or caring for others. Indi-

viduals with Enterprising interests enjoy assertive or persuasive activities such as selling or leading others, and envi-

ronments affording power and status (e.g., business settings). And finally, individuals with Conventional interests enjoy

structured activities and environments characterized by rules, regulations, and routines.

In addition to characterizing individuals, vocational interests also characterize occupational environments (Hol-

land, 1997; Rounds et al., 1978). That is, the substantive nature of the work situation can be described using the

same dimensions that characterize an individual’s vocational interests. For example, a Realistic environment pro-

vides opportunities to engage in hands-on activities and/or to work outdoors, as well as to interact with others who

also have Realistic interests. Echoing Schneider’s (1987, p. 437) dictum “the people make the place”, Holland (1997,

p. 41) defined the characteristic interests of an environment by “the situation . . . created by the peoplewhodominate a

given environment.” Specifically, Holland posited that characteristic interests of an occupational environment emerge

in part because people with similar vocational interests gravitate toward, and remain in, settings that allow them to

engage in the activities they enjoy and are well suited for (Hansen & Dik, 2005; Wille, Tracey, Feys, & De Fruyt, 2014;

Wilk, Desmarais, & Sackett, 1995). In other words, even though occupational vocational interests could be measured

by job activities and job characteristics, a stringent test of Holland’s theory requires that occupation-level vocational

interests be measured in terms of the interests of the actual people who make the place (i.e., the incumbent method;

Harmon, Hansen, Borgan, &Hammer, 1994).

1.2 Relevance of vocational interests for personnel selection

Despite their seeming relevance to personnel selection—after all, vocational interests assess preferences for work

activities and environments—there have been relatively few validity studies within the last three decades that have

examined the relationship between vocational interests and job performance. In part, this may have been due to an

influential meta-analysis (Hunter & Hunter, 1984) demonstrating a bivariate validity of only rcorrected = .10 between

vocational interests and supervisor ratings of job performance (k= 3, N= 1,789; corrected for attenuation in the cri-

terion). Although we should be cautious when drawing conclusions based on only k = 3 studies, more comprehen-

sive meta-analyses also report effects of about the same magnitude. That is, Van Iddekinge et al. (2011) reported the

average of observed validities of vocational interests, while only considering the most theoretically matched

4 WEE ET AL.

dimensionof vocational interests for each job (e.g., correlationbetweenRealistic interests and jobperformance, in jobs

forwhich the authors deemedRealistic interests to be themost relevant RIASECdimension; see p. 1173). The average

validity correlation between a theoretically matched vocational interest and job performance was only rcorrected = .14

(k = 80, N = 14,522). Further, in other recent meta-analyses, Nye et al. (2012, 2017) reported vocational interest

validities predicting task performance (without matching interests to jobs), with mean rcorrected = .09 (i.e., based on

the unweighted mean correlation across interest measures; range: .05–.14 [Nye et al., 2012] and .05–.15 [Nye et al.,

2017]; all of which were corrected for indirect range restriction and attenuation in the criterion—uncorrected validi-

tieswere not reported). As a point of reference, these performance validities for vocational interests aremuch smaller

than validities for generalmental ability (Schmidt &Hunter, 1998), although they are similar inmagnitude to observed

validities for measures of Big Five personality traits (Barrick, Mount, & Judge, 2001).

Nonetheless, it is the contextualized nature of vocational interests that makes them a potentially powerful predic-

tor within selection contexts. As noted by Rounds and Su (2014), the utility of interests lies in their implicit acknowl-

edgement of congruence between a person and a context or activity, and a central hypothesis in vocational interest

theory (Holland, 1997) is that job performance is enhanced by vocational interest congruence (e.g., due to the sense

of subjective fit, motivation, or work engagement), and conversely that job performance is impaired when vocational

interest congruence is low.

1.3 Person–occupation interest congruence

Vocational interest theories (e.g., Holland, 1997; Parsons, 1909; Super, 1980) conceptualize vocational interest con-

gruence as a multilevel phenomenon, resulting from the interaction between commensurately defined person and

occupation vocational interests. To elaborate, by assuming that people gravitate toward and remain in occupations

based on their vocational interests, Holland (1997) (a) distinguishes between entities at two levels (i.e., people and

environments), (b) describes the mechanism through which occupational environments derive “many of [their] psy-

chologically important features” (p. 48)—that is, through “the people who dominate a given environment” (p. 41), and

(c) specifies themanner inwhich the occupational environment should be operationalized (i.e., the incumbentmethod;

Harmon et al., 1994). Conceptualizing the environment in thisway—theorized to arise through “composition”—implies

that the environment is described by the same interest dimensions used to describe persons (i.e., a compositionmodel

with isomorphic constructs across levels of analysis; Kozlowski&Klein, 2000). The approachof aggregating individual-

level reports of interests to the occupation level (in order to represent person-level and environment-level interests in

a commensurate fashion) is also the key feature that differentiates person–environment congruence from “all possible

P*E interactions” (Harrison, 2007, p. 394). Thus, we here hypothesize the classic notion of vocational interest congru-

ence, and we appropriately formalize this notion as a cross-level person–occupation interest interaction effect in a

multilevel model.

Hypothesis 1 (Person–occupation interest congruence): The relationship betweenavocational interest and jobper-

formance depends on the occupation-level vocational interest (cross-level interaction), such that a

vocational interest is more positively [negatively] related to job performance when the occupation-

level vocational interest is high [low].

1.4 Methodological contribution of operationalizing person–occupation interestcongruence as a person × occupation interest interaction

Although the vocational interest congruence hypothesis has been extensively discussed in prior work, we assert in

the current paper that prior work has yet to appropriately test the vocational interest congruence phenomenon (i.e.,

WEE ET AL. 5

as a cross-level interaction involving commensurate measures of interests at the individual and occupational levels).

To compare our approach with prior work, we surveyed the references in Nye et al.’s (2017) meta-analysis of voca-

tional interest congruence and performance to examine how each prior study operationalized vocational interest

congruence (summarized in Appendix A). The methods that have typically been used fall into two major categories:

(a) individual-level correlations between an interest dimension and performance, which do not assess congruence

between an individual variable and an environmental variable, and (b) congruence indices (e.g., Holland’s highpoint

congruence index and its variants).

1.4.1 Correlations

Although a correlation between an interest dimension and job performance captures the relationship between a per-

son’s vocational interest and job performance, it does not directly take into account the occupation-level variation

in vocational interests. That is, this approach does not allow an examination of the vocational interest congruence

hypothesis, which is about how the strength of the relationship between a person’s vocational interest and the perfor-

mance criterion varieswith the occupation-level vocational interest.

1.4.2 Congruence indices

Other methods of operationalizing congruence combine the person and/or environment vocational interests in some

way to obtain a single, composite score. These indices take scores on six continuous dimensions (i.e., RIASEC), and con-

vert them into a single rank-based profile, discarding most of the information from the six dimension scores. The con-

gruence index then compares a person’s rank-based profile against an environment’s rank-based profile. For example,

inHolland’s highpoint congruence, the singleRIASECdimensionwith thehighest standard score for a person is treated

as that person’s highpoint code, and the single RIASEC dimension with the highest standard score for an environment

is treated as the environment’s highpoint code. So a personmight be assigned a code of “Realistic” and an environment

a code of “Social.” A score is then assigned to each person based on the degree to which these codes match between

the person and the environment. As Edwards (2002; Edwards & Parry, 1993) has pointed out, this imposes a con-

straint stipulating that the interest dimension identified as the highpoint code is more important than other interest

dimensions. Serious misspecifications can occur by imposing, rather than estimating, these constraints. Moreover—

because congruence indices combine a person’s vocational interest profile with the environment’s vocational inter-

est profile to obtain a single numerical value—person effects can no longer be disentangled from environment

effects.

In short, the problems associated with previous methods have compromised previous tests of the vocational inter-

est congruence hypothesis. In contrast, our current approach operationalizes a person–occupation interest congru-

ence effect as the cross-level person × occupation interaction term in a multilevel regression model. This means that

person effects and environment effects can be separately estimated in the multilevel regression model. Additionally,

because all the previous operationalizations of congruence have focused on the individual level of analysis only, the

nonindependence of people within the same occupation is ignored (Bliese, 2002), potentially biasing standard error

estimates and increasing the risk of drawing incorrect statistical inferences (Bliese &Hanges, 2004).

1.5 Person–occupation gender congruence

We further note that commensurate person–occupation congruence effects are not limited to vocational interest

congruence alone. Another relevant concept is person–occupation gender congruence. That is, theory and empirical

6 WEE ET AL.

evidence (Joshi et al., 2015) confirm that, although the mean effect size for the gender gap in job performance rat-

ings is small and nonsignificant (d = –0.04, k = 93, 95% CI [–.20, .12]), the gender gap in job performance ratings (i.e.,

how gender relates to individual job performance ratings) is significantlymoderated by the proportion of women in an

occupation (Joshi et al., 2015; Kanter, 1977; see also Sackett et al., 1991). As an occupation becomesmore dominated

bymen, the gender gap in job performance ratings favorsmen. Conversely, as an occupation becomesmore dominated

bywomen, the gender gap in job performance ratings favors women.

Role congruity theory (Eagly&Karau, 2002;which extends Eagly’s [1987] social role theory) provides one plausible

explanation for why occupation gender composition is expected to moderate the gender gap in job performance rat-

ings. According to social role theory (Eagly, 1987), gender role stereotypes represent expectations about howmenand

women behave. These expectations emerge because people observe men and women in different—and sex-typical—

roles. Traditionally, for example, most men took on the breadwinner role (associated with agentic attributes, e.g.,

assertive and confident) and most women took on the homemaker role (associated with communal attributes, e.g.,

nurturing and kind). Based on the correspondent inference (Gilbert & Malone, 1995), perceivers will infer a person’s

internal disposition from the typical behaviors that are engaged in for that role. Thus, agentic attributes aremore likely

to be ascribed tomen, and communal attributes aremore likely to be ascribed to women.

However, people occupy several roles simultaneously (e.g., gender roles, occupational roles, etc.), and each role

is associated with its own stereotypic expectations about how members should behave. Thus, role congruity theory

proposes that the congruence between a person’s gender role and another role (e.g., occupational role) will impact

how that person is perceived in the other role. Men in predominantly male occupations (such as heavy machinery

operator) will be perceived as good fits for the occupation—stereotypic expectations about what men are like will be

congruent with stereotypic expectations of what heavymachinery operators are like. Conversely, stereotypic notions

of what women are like will be incongruent with stereotypic expectations of what heavy machinery operators are

like. It is the dissimilarity between attributes predominantly associated with women and attributes associated with

typical/successful heavy machinery operator that is expected to result in rater discrimination and prejudice. Because

the finding that person–occupation gender congruence relates to job performance ratings is now well known (see

Joshi et al., 2015; Sackett et al., 1991), wedonot claim to advance a novel hypothesis that the gender–job performance

relationship ismoderatedby the gender composition of the occupation (i.e., a cross-level interaction effect for gender).

Nonetheless, in the current paper we are interested in such a gender congruence effect for two reasons.

First, the existence of a person–occupation gender congruence effect on job performance might lead to spurious

support of our focal hypothesis (i.e., person–occupation interest congruence). This is because gender is substantially

related to vocational interests at the individual level (Su et al., 2009) and at the occupation level (Lippa, Preston, &

Penner, 2014). As such, a gender congruence effect might masquerade as an apparent interest congruence effect. In

other words, person–occupation gender congruence is a common correlate—and thus, possibly a common cause—of

both person–occupation interest congruence and job performance. Interestingly, because interests and gender are

strongly correlated at both the individual and occupational levels of analysis, it is also logically possible that person–

occupation gender congruence might be spuriously due to person–occupation interest congruence. If the cross-level

effects (for interests and gender) are tested in isolation, the apparent effects of onemight be due to the actual effects

of the other. Thus, we test both cross-level interaction effects simultaneously—in order to disambiguate the two.

Second, previous theory proposing person–occupation gender congruence effects on job performance has relied

almost exclusively on the notions of stereotyping, discrimination, and performance rating bias (e.g., Eagly & Karau,

2002;Heilman, 1983), and has not examined the possibility that gender gaps in job performance ratings could be due—

at least partly—to actual differences in job performance (i.e., differences in job knowledge and skills that a person has

to do the job; Campbell,McHenry, &Wise, 1990;McHenry, Hough, Toquam,Hanson, &Ashworth, 1990). For example,

Heilman’s (1983) Lack of Fit model argues that negative performance expectations are formed when there is a mis-

match between perceptions of what women are like (i.e., gender stereotype) and perceptions of what is required to be

successful in amale-dominated job (i.e., gender-based job stereotype [or job sex-type]). Specifically, Heilman proposed

a discrimination mechanism through which these negative performance expectations influence the gender gap in job

WEE ET AL. 7

performance (i.e., other-directed sex bias,manifested, for example, in an expectation thatwomenwill not performwell

in male sex-typed jobs). That is, some parts of Heilman’s (1983) model assume an “actual equivalence of women and

men in many achievement-related characteristics” (p. 288), and thus do not allow for the possibility that a gender gap

in job performance ratings may be due to actual gender differences in knowledge and skills required for successful

performance on a given job.

In contrast, several well-established theories in the field of vocational psychology (e.g., Ackerman, 1996; Eccles

[Parsons] et al., 1983; Lent, Brown, &Hackett, 1994) have delineated how interests guide themanner in which people

direct and invest their intellectual resources in the development of knowledge and skills in a particular educational

or occupational domain. For example, Ackerman’s (1996) PPIK (intelligence-as-process, personality, interests, and

intelligence-as-knowledge) theory argues that occupational success is largely determined by individual differences

in occupation-specific knowledge (i.e., expertise), which is determined by the “time and effort devoted to knowledge

acquisition,” and a person’s intelligence and interests (p. 245).More specifically, Ackerman (2014) argues that a gender

difference in domain knowledge is “largely an epiphenomenon in an individual’s orientation toward or away from par-

ticular knowledge domains, after accounting for individual differences in abilities, personality traits, interests, motiva-

tion, and so on” (p. 249). It is individual differences in interests (in tandem with individual differences in abilities and

personality) that “are instrumental in determining the observed sex differences in domain knowledge,” which in turn

contributes to the gender gap in job performance (p. 249).

The issue of gender differences in interests and abilities as they relate to occupational segregation has perhaps

beenmost extensively examined in the context ofmathematically intensive academic disciplines such as science, tech-

nology, engineering, and mathematics (STEM). In summarizing the large body of empirical research that has accumu-

lated in this area, Ceci, Ginther, Kahn, and Williams (2014) convincingly show that gender differences in interests,

attitudes, and expectations toward STEM-occupations (a) are observed in children (e.g., Legewis & DiPrete, 2012),

(b) solidify in middle- and high-school, and (c) predict college major choices (Xie & Shauman, 2003). Ceci et al. (2014,

p. 83) conclude that “early socialization . . . can lead to [cumulative] differences in comparative advantage,” such that

men andwomenwill gravitate toward different academicmajors and occupations.

Interestingly, Heilman’s (1983) model does discuss a similar phenomenon, which she labels self-limiting behavior

(i.e., self-directed sex bias, e.g., manifested in women’s own expectations that they are less likely to be successful

in male sex-typed occupations). These negative performance expectations “influence whether and how individuals

seek to advance their career” (p. 279) such that women’s “performance expectations for more masculinely defined . . .

jobs are apt to be low because of the presumed lack of fit between perceived qualities of oneself and the perceived

requirements of these jobs” (p. 287). In otherwords, Heilman’s contention that this stereotype-based process can give

rise to self-limiting behaviors—that is, job choice and career investment/advancement—highlights how one’s beliefs

and expectancies are developed through a process of socialization (i.e., the internalization of gender stereotypes and

norms), which in turn influence the vocational interests people develop and hence the activities they choose to invest

in and skills they ultimately accrue (see also Correll, 2001; Eccles, 1994; Gottfredson, 1981).

Thus, there are two potential explanations for the observed gender gap in job performance in predominantly male

or predominantly female occupations: (a) Heilman’s (1983) stereotype-based mechanism of discrimination, which

should exert an influence on only job performance ratings, and (b) Ackerman’s (1996) pipeline-based mechanisms of

differential interest and investment in occupation-related knowledge and skills (similar to Heilman’s [1983] notion of

self-limiting behavior), which should exert an influence on knowledge- and skill-based measures of job performance.

Although our current data do not permit a direct test of these two competing explanations for the person–occupation

gender congruence effect on job performance, in the current paper we are able to assess the person–occupation gen-

der congruence effect on separate operationalizations of job performance. If the cross-level gender congruence effect

is obtained for ratings of job performance but not for knowledge- and skill-based measures of job performance, this

would attest to Heilman’s (1983) discriminationmechanism as a likely root cause of the gender gap in job performance

ratings. Conversely, if the cross-level gender congruence effect is obtained for measures of job knowledge and skills,

it would suggest that the gender gap in job performance could be—at least in part—explained by pipeline issues (i.e.,

8 WEE ET AL.

howmen andwomen invest in different domains of knowledge and skills). Neither of these explanations can be tested

directly, because we do not have measures of rating bias, or of pipeline vocational experiences and past educational

opportunities and choices. Nonetheless, the current paper can offer initial evidence of whether the gender gap in job

performance in predominantly female or predominantly male occupations exists for job performance ratings only, or

if it also exists for measures of performance of job skills and knowledge acquired.1

Research question: Does the person–occupation gender congruence effect on job performance apply only to job

performance ratings, or does it also apply to knowledge-based and skill-based measures of job

performance?

2 STUDY 1 METHOD

2.1 Sample

Study 1 is based on data from the U.S. Army’s Project A concurrent validation study (Campbell, 1990; Campbell &

Knapp, 2001;McHenry et al., 1990). Participantswere 3,532 first-term, entry-level enlistedmilitary personnel (86.4%

male, 72.8%White, 20.6%Black, 6.6%Hispanic or other, 94.3%with at least a high-school diploma). All had been in the

service for at least 12 months. Each participant was employed in one of eight occupations (see Table 1): infantryman,

canon crewman, tank crewman, single channel radio operator, light wheel vehicle mechanic, administrative specialist,

medical specialist, and military police. Data were collected in four half-day sessions across two consecutive days. A

4-hr battery of predictor tests—includingmeasures of vocational interests—was administered during one session, and

job performance assessments were conducted during the other three sessions. Job performance was assessed using

a combination of hands-on tests (i.e., work samples), paper-and-pencil job knowledge tests, paper-and-pencil training

knowledge tests, and supervisor and peer ratings of performance. General mental ability had been assessed during

recruitment, prior to occupational assignment.

2.2 Measures

2.2.1 Vocational interests

Vocational interests were measured using the Army Vocational Interest Career Examination (AVOICE), a revised ver-

sion of the Vocational Interest Career Examination (VOICE; Alley & Matthew, 1982). AVOICE was designed to mea-

sure all six RIASEC dimensions, and to provide “sufficient coverage of vocational areas most important in the Army”—

that is, roughly half of the 22 scalesmapped onto Holland’s Realistic interest dimension (Peterson et al., 1990, p. 269).

The 22 occupational scales of the AVOICE are listed in Table 2.2AVOICE items describe job titles and activities, and

participants indicated their preference for each item on a scale from 1=Dislike very much to 3= Indifferent to 5= Like

verymuch. TheProjectA teamconductedprincipal components analyses to “identify sets ofAVOICE scales that cluster

together empirically,” but “the method of parallel analysis (Humphreys &Montanelli, 1975;Montanelli & Humphreys,

1976) indicated that as many as 22 components underlie the 22 scales” (Peterson et al., 1992, p. 171; Russell &

Peterson, 2001, p. 297). Ten additional models were examined via confirmatory factor analysis and compared. Finally,

an eight-factor model “was selected for its superior fit to the data and the interpretability of the model’s composites”

(Peterson et al., 1992, p. 171).3

The final eight-factor structure differed from the proposed six-factor RIASEC structure in the following ways:

(a) the Realistic interest scales were grouped into three homogenous interest dimensions—Structural/Machines

(Mechanics, Heavy Construction, Electronics, and Vehicle Operator), Rugged outdoors (Combat, Rugged

WEE ET AL. 9

TABLE1

Jobdescriptions,samplesizes,andsexproportionsforoccupations

Occupation-levelvocationalinterestmeans

Jobdescriptiona

NGen

der

comp.

Structural

Rugged

Protective

Skilled

Arts

Intrprs.

Admin.

Food

Infantrym

an:R

esponsibleforbasic

weapons,fieldtechniques,andunit

tactics

491

.000

30.18

36.49

16.36

14.70

17.01

38.14

21.28

11.82

Can

onCrewman

:Participates

in

tran

sportingan

doperatingfield

artillery

equipmen

t

464

.000

31.10

34.55

16.02

15.52

17.44

39.32

24.26

13.17

TankCrewman

:Responsiblefordriving

tankan

doperatingweaponssystem

394

.000

31.32

37.31

15.68

14.87

16.99

37.02

21.93

12.53

SingleChan

nelRad

ioOperator:

Operates

radio,teletyp

e,an

dsatellite

equipmen

t

289

.156

29.11

33.24

14.35

15.79

17.84

39.34

23.75

12.52

Ligh

tWheelV

ehicleMechan

ic:

Troubleshootsproblemsan

dperform

s

regu

larmaintenan

ce

478

.075

33.82

36.30

14.88

13.87

15.99

35.33

20.90

11.94

AdministrativeSp

ecialist:Perform

s

varietyofclericalandad

ministrative

tasks

427

.555

23.69

28.86

13.85

14.55

17.18

40.29

25.80

13.22

Med

icalSp

ecialist:Administers

emergency

treatm

entan

dassistsin

outpatientan

dinpatientcare

under

supervisionofa

physician

392

.296

26.15

32.02

14.57

14.57

17.61

45.23

22.41

12.35

Military

Police:Su

pportsbattlefield

operations,carriesoutlaw

enforcem

ent,an

dsecurity

operations

597

.079

27.21

35.33

19.37

13.14

15.96

37.57

19.74

11.65

Note.Male=0;Fem

ale=1.G

ender

Composition(Gen

der

Comp.)=.000meanstheoccupationwas

all-male.

Abbreviations:Structural=

Structural/Machines;R

ugged

=Rugged

Outdoors;P

rotective=ProtectiveServices;Skilled=Skilled

/Technical;A

rts=AudiovisualArts;Intrprs.=

Interper-

sonal;A

dmin.=

Administrative;Fo

od=Fo

odServices.

aJobdescriptionstakenfromCam

pbell(1990,p.237).

10 WEE ET AL.

TABLE 2 Composite predictor measures and their subscales/subtests

Vocational Interests Example Interest Item

(Measured using AVOICE) No. of Items α (Hough, Barge, & Kamp, 1987, pp. 28–32)

Structural/Machines

Mechanics 10 .94 “replace valves in an engine”

Heavy construction 13 .92 “mason”

Electronics 12 .94 “repair a television set”

Vehicle operator 3 .70 “taxi driver”

Rugged Outdoors

Combat 10 .90 “use cover, concealment, and camouflage”

Rugged individualism 15 .90 “work outdoors”

Firearms enthusiast 7 .89 “gunsmith”

Protective Services

Fire protection 2 .76 Example item not available

Law enforcement 8 .89 “prison guard”

Skilled/Technical

Electronic communication 6 .83 “operate radio and teletype equipment”

Science/chemical 6 .85 “mix chemical compounds”

Computers 4 .90 “computer programmer”

Mathematics 3 .88 “solve arithmetic problems”

Audiovisual Arts

Aesthetics 5 .79 “read poetry”

Drafting 6 .84 “draw blueprints for a bridge”

Audiographics 5 .83 “photographer”

Interpersonal

Leadership guidance 12 .89 “give on-the-job training”

Medical services 12 .92 “physical therapist”

Administrative

Clerical/administrative 14 .92 “keep accurate records”

Warehouse/shipping 2 .61 “take inventory for a department store”

Food Services

Food service professional 8 .89 “buy supplies for a restaurant”

Food service employee 3 .73 “dishwasher”

GeneralMental Ability

(Measured using ASVAB)

Math knowledge 25 .87

Arithmetic reasoning 30 .91

Auto and shop information 25 .87

Mechanical comprehension 25 .85

Electronics information 20 .81

Coding speed 84 –

(Continues)

WEE ET AL. 11

TABLE 2 (Continued)

GeneralMental Ability

(Measured using ASVAB)

Number operations 50 –

Word knowledge 35 .92

Paragraph comprehension 15 .81

General science 25 .86

Note. For vocational interests, the number of items and internal consistency reliability (α; based on N = 8,224 to 8,396) for

each of the AVOICE scales was obtained from Peterson et al. (1990, p. 274). For general mental ability, the number of items

and internal consistency reliability (α) for each of the ASVAB scales was obtained from Russell et al. (2001, p. 77). Internal

consistency reliability was not computed for speeded tests (i.e., Coding speed andNumber operations).

Individualism, and Firearms Enthusiast), and Protective services (Fire Protection and Law Enforcement), (b) three of

the four Investigative interest scales (Science/Chemical, Computers, andMathematics) were grouped with Electronic

communication to form the Skilled/Technical interest dimension, (c) the Artistic interest scale (Aesthetics) was

grouped with Drafting and Audiographics to form the Audiovisual arts interest dimension, (d) the Social/Enterprising

interest scale (Leadership guidance) was groupedwithMedical services to form the Interpersonal interest dimension,

and (e) the Conventional interest scales were grouped into two homogenous interest dimensions—Administrative

(Clerical/Administrative and Warehouse/Shipping) and Food services (Food Service Professional and Food Service

Employee). Scale mean internal consistency reliability is .85 (range: .61–.94; see Table 2). Vocational interest com-

posites were created by unit-weighting the scales associated with each interest dimension listed in Table 2 (e.g.,

the Audiovisual Arts interest dimension was a unit-weighted composite of Aesthetics, Drafting, and Audiographics

interest scale scores).

2.2.2 General mental ability

General mental ability was measured with the Armed Services Vocational Aptitude Battery (ASVAB). The 10 ASVAB

scales were composited (using unit-weights), and the ASVAB composite had an internal consistency reliability of .98

(Mosier’s [1943] composite reliability formula, ASVAB subscale reliabilities [Table 2] and correlations [1980 ASVAB

norming study;N=9,173;Department ofDefense, 1982]). The two speeded tests (i.e., “Coding Speed” and “Numerical

Operations”) were not included in the reliability calculation as we did not have reliability information for these two

subscales.

2.2.3 Job performance

Job performance was assessed using several measures: paper-and-pencil job knowledge tests, hands-on tests (i.e.,

work samples), paper-and-pencil training knowledge tests, and a composite of supervisor and peer ratings of job

performance (Campbell, Ford, et al., 1990). Descriptions of each of thesemeasures are provided in Appendix B.When

testing the two key cross-level interaction effects (person–occupation interest congruence and person–occupation

gender congruence), we focused on core technical performance, that is, “the proficiency with which the individual per-

forms the tasks that are specific and ‘central’ to his or her job,” which represents, “the core content of the job that

distinguishes it from other jobs” (McHenry et al., 1990, p. 342). Core technical performance is a composite of three

measures: (a) paper-and-pencil job knowledge tests, (b) hands-on tests, and (c) paper-and-pencil training knowledge

tests (see Appendix B). These occupation-specific job performance measures were found to load together in a factor

analysis by Campbell, McHenry, et al. (1990, p. 325; average factor loading = .60). Core technical performance was

12 WEE ET AL.

TABLE 3 Descriptive statistics for vocational interests, general mental ability, and job performancemeasures

Overall

(N= 3,532) Men (N= 3,051)

Women

(N= 481) ICC values

Variable M SD M SD M SD Sex d ICC(1) ICC(2)

Vocational interests

Structural/Machines 29.10 7.15 30.26 6.52 21.75 6.62 −1.29 .20 .99

Rugged outdoors 34.42 7.62 35.49 7.15 27.64 7.02 −1.12 .13 .99

Protective services 15.88 4.26 16.15 4.20 14.17 4.29 −0.46 .17 .99

Skilled/Technical 14.51 3.23 14.58 3.26 14.02 2.99 −0.18 .07 .97

Audiovisual arts 16.90 3.37 16.84 3.39 17.28 3.19 0.14 .04 .95

Interpersonal 38.84 8.05 38.37 7.98 41.82 7.85 0.44 .13 .98

Administrative 22.30 5.73 21.87 5.61 25.05 5.76 0.55 .12 .98

Food services 12.35 3.68 12.21 3.63 13.27 3.86 0.28 .02 .91

General mental ability 52.64 4.52 52.84 4.62 51.34 3.58 −0.40 .09 .98

Job performance

Core technical performance 102.06 15.97 102.03 15.94 102.26 16.17 0.01 – –

Job knowledge tests 53.38 19.20 53.19 19.49 54.40 17.59 0.07 – –

Hands-on tests 74.06 30.91 73.84 30.53 75.23 32.85 0.04 – –

Training knowledge tests 55.66 21.48 55.75 21.85 55.20 19.36 −0.03 – –

Performance ratings 24.31 6.23 24.42 6.24 23.66 6.09 −0.12 – –

Note. Bolded values are statistically significant at p < .05. Male = 0, Female = 1. All job performance measures have ICC = 0

by design, because each job performance measure was standardized within-occupation (Campbell, Hanson, & Oppler, 2001,

p. 318).

“obtained simply by summing scores within eachmeasurement method, standardizing, and then taking the single sum

acrossmethods” (Campbell,McHenry, et al., 1990, p. 324). A fourthmeasure used job performance ratings from super-

visors and peers (see Appendix B; and ratings results in Appendix D).

2.3 Analytic strategy

Earlier we discussed how person–occupation interest congruence should be examined as the person interest × occu-

pation interest cross-level interaction effect. We also mentioned that person–occupation gender congruence (i.e.,

person gender × occupation gender composition cross-level interaction effect) might spuriously create a person–

occupation interest congruence effect. Thus, we tested our predictions using hierarchical linear models with persons

(at Level 1) nested within occupations (at Level 2).

Job performance was the dependent variable, and the Level 1 predictor (individual-level vocational interests) was

group-mean centered, as recommended (e.g., Aguinis, Gottfredson, & Culpepper, 2013; Hofmann & Gavin, 1998).

The Level 2 predictor (occupation-level vocational interests) was grand-mean centered. General mental ability was

a control variable, as it is known to predict job performance (Schmidt & Hunter, 1998). Gender was dummy coded

(male = 0; female = 1). Occupation-level vocational interest was operationalized as the mean of individuals’ scores

within each occupation (Bliese, 2002). Each vocational interest variable showed significant occupation-level variance

(all one-way ANOVAs exhibited p < .05; see Table 3): ICC(1) values ranged from .02 to .20, mean ICC(1) = .11; ICC(2)

ranged from .91 to .99, mean ICC(2) = .97. We assessed gender composition at the occupation-level (see Joshi et al.,

2015; Sackett et al., 1991) and consistent with Kanter’s (1977) assertions about tokenism in male-dominated fields.

WEE ET AL. 13

TABLE 4 Correlations among group-mean-centered study variables

Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

1. Gender (Male= 0; Female= 1) –

2. General mental ability −.09 –

3. Structural/Machines −.25 .02 –

4. Rugged outdoors −.20 .15 .56 –

5. Protective services −.05 −.08 .24 .39 –

6. Skilled/Technical −.06 .07 .44 .29 .19 –

7. Audiovisual arts .02 .02 .31 .24 .12 .59 –

8. Interpersonal .06 −.02 .21 .28 .36 .59 .55 –

9. Administrative .09 −.15 .23 .03 .17 .62 .51 .57 –

10. Food services .06 −.10 .24 .14 .16 .30 .36 .37 .52 –

11. Core technical performance .00 .40 .08 .16 .00 .05 .00 .01 −.08 −.07 –

12. Job knowledge testsa

.02 .40 .07 .14 .00 .02 .00 .00 −.06 −.05 .73 –

13. Hands-on testsa

.02 .18 .08 .10 .01 .04 .00 .01 −.04 −.03 .84 .36 –

14. Training knowledge testsa

−.01 .44 .07 .16 −.01 .04 −.01 .00 −.09 −.07 .76 .62 .37 –

15. Performance ratings −.04 .15 .06 .11 .03 .02 −.02 .02 −.04 −.05 .30 .23 .22 .26

Note. N= 3,532 except where otherwise indicated.aN = 3,041 because job performance measures based on job knowledge tests, hands-on tests, and training knowledge tests

were not available for infantrymen. All |r| ≥ .04 are statistically significant at p< .05.

Occupation-level gender composition was operationalized as proportion of women in an occupation. We used the

“multilevel” package (version 2.6; Bliese, 2016; R version 3.6.1; R Core Team, 2019) to estimate the hierarchical linear

models. To facilitate interpretation of the cross-level interaction effects, we report simple slopes analyses (Preacher,

Curran, & Bauer, 2006).

3 STUDY 1 RESULTS

3.1 Descriptive statistics

Tables 3 and 4 show individual-level descriptive statistics and correlations. As seen in Table 3, on average men have

stronger interests than women on these dimensions: structural/machines (d = –1.29, p < .05), rugged outdoors (d =

–1.12, p < .05), and protective services (d = –0.46, p < .05). By contrast, on average women have stronger interests

than men on administrative (d = 0.55, p < .05) and interpersonal (d = 0.44, p < .05) interest dimensions, and slightly

stronger interests on the food services (d= 0.28, p< .05) interest dimension. Consistent with previous research (Joshi

et al., 2015; d = –0.04), there were only small (d = 0.01 [averaged across job performance measures]; range: 0.07 to

–0.12) gender differences on job performance, when gender differences were averaged across all occupations.

3.2 Test of the person–occupation interest congruence effect

Hypothesis 1 states that the relationship between a vocational interest and job performance depends on the

occupation-level vocational interest, such that a vocational interest is more positively related to job performance

when the occupation-level vocational interest is high, and more negatively related to job performance when the

14 WEE ET AL.

occupation-level vocational interest is low. Table 5 provides the results for person–occupation interest congruence

predicting job performance. As shown in Table 5, for each interest (e.g., structural/machines), we estimated two mod-

els. Model 1 (M1) is the vocational interest main effects model. It includes individual-level general mental ability (i.e.,

Ability) as a control variable, individual-level interest (i.e., P = person-level interest), and occupation-level interest

(i.e., E = environment-level interest) on a given vocational interest dimension. Model 2 (M2) is the interaction effects

model. It is identical toM1,with the addition of the person× occupation interest (i.e., P× E) cross-level interaction and

a squared term for the occupation-level interest (i.e., E2).4Each model included a random intercept to allow for vari-

ation in mean job performance across occupations, and a random slope for vocational interests, to allow for variation

in the relationship between individual-level vocational interest and job performance across occupations. This set of

analyses (i.e., M1 andM2) was conducted eight times, once for each vocational interest dimension.

In support of Hypothesis 1, there was a statistically significant person× occupation interest cross-level interaction

for six of theeight vocational interest dimensions: structural/machines (𝛾 =0.100;p< .05), ruggedoutdoors (𝛾=0.057;

p < .05), skilled/technical (𝛾 = 0.415; p < .05), audiovisual arts (𝛾 = 0.355; p < .05), interpersonal (𝛾 = 0.039;

p < .05), and administrative (𝛾 = 0.093; p < .05). The significant cross-level interactions explained an average of

86% (range: 46–97%) of the random slope variation (Bliese, 2002, p. 429). As an example, the random slope varia-

tion accounted for by the cross-level rugged outdoors interest interaction was calculated as variance accounted for,

VAF(%)= (1 – .025/.046)× 100%= 46% (see Table 5).

To facilitate the interpretationof the cross-level interest interactioneffects, for eachof theeight vocational interest

dimensions, we plotted in Figure 1 the person× occupation interest interaction (P× E) at high (+1 SD) and low (–1 SD)

levels of occupation-level interest. In Figure 1, a solid line indicates the relationship between individual-level interest

and job performance at low (–1 SD) levels of occupation-level interest; and a dashed line indicates the same relation-

ship at high (+1 SD) levels of occupation-level interest. In all, for six of the cross-level interaction effects (Figure 1),

the individual-level vocational interest–job performance relationship ismoderated by the occupation-level vocational

interest.

For additional information, the statistical tests for each of the simple slopes (i.e., the relationship between

individual-level interest and job performance, at a given level of occupation-level interest) are presented in Table 7.

As seen in Figure 1, when the occupational environment was characterized by high levels of interest (i.e., dashed line)

on the same interest dimension as the individual-level interest, there was a significant positive relationship between

a person’s vocational interest and job performance for six of the eight interest dimensions: structural/machines

(B = 0.516, SE = 0.087, p < .05), rugged outdoors (B = 0.381, SE = 0.096, p < .05), protective services (B = 0.237,

SE= 0.112, p< .05), skilled/technical (B= 0.458, SE= 0.122, p< .05), audiovisual arts (B= 0.211, SE= 0.106, p< .05),

and interpersonal (B= 0.156, SE= 0.052, p< .05). Conversely, when the occupational environment was characterized

by low levels of interest on that dimension (i.e., solid line), there was a significant negative relationship between a per-

son’s vocational interest and jobperformance for twoof the eight dimensions: audiovisual arts (B=–0.261, SE=0.107,

p< .05) and administrative (B=−0.232, SE= 0.106, p< .05).

In summary, in support of Hypothesis 1 (i.e., the person-occupation interest congruence effect), the occupation-level

vocational interestmoderated the relationshipbetween individual-level vocational interest and jobperformance. That

is, a person’s vocational interestwaspositively related to jobperformance inoccupational environments characterized

by high levels of that interest, and negatively related (or unrelated) to job performance in occupational environments

characterized by low levels of that interest.5

3.3 Test of the person–occupation interest congruence effect while controlling forthe person–occupation gender congruence effect

The person–occupation gender congruence effect occurs when the gender gap in job performance is moderated by an

occupation’s gender composition. Based on previous research (Joshi et al., 2015; Sackett et al., 1991), we expected to

WEE ET AL. 15

TABLE5

Multilevelm

odelingresultsforperson–occupationinterestcongruen

cepredictingcore

technicalperform

ance

Fixed

effects

Randomeffects

Interestdim

ension

Intercep

tAbility

PE

E2P×E

Intercep

tSlope

Covariance

Residual

Structural/Machines

M1

102.163

1.471

0.205

−0.186

6.809

0.118

0.482

208.219

(0.955)

(0.056)

(0.128)

(0.261)

M2

101.477

1.479

0.210

0.293

0.075

0.100

5.009

0.017

0.024

208.214

(1.136)

(0.057)

(0.060)

(0.281)

(0.084)

(0.019)

Rugged

outdoors

M1

102.068

1.417

0.225

−0.637

7.347

0.046

0.318

209.267

(0.990)

(0.057)

(0.083)

(0.212)

M2

101.542

1.423

0.234

0.174

0.095

0.057

5.540

0.025

0.130

209.237

(1.030)

(0.057)

(0.066)

(0.395)

(0.084)

(0.025)

Protectiveservices

M1

101.993

1.491

0.149

−0.776

3.676

0.000

0.000

213.631

(0.729)

(0.057)

(0.064)

(0.441)

M2

103.380

1.492

0.163

−0.082

−0.447

0.042

2.036

0.005

−0.007

213.569

(0.815)

(0.057)

(0.070)

(0.454)

(0.189)

(0.044)

Skilled

/Technical

M1

101.748

1.474

0.172

3.634

6.827

0.135

−0.827

212.925

(0.960)

(0.057)

(0.153)

(0.813)

M2

102.839

1.476

0.120

1.448

−1.291

0.415

2.753

0.009

−0.017

212.841

(0.850)

(0.057)

(0.086)

(0.808)

(0.860)

(0.108)

(Continues)

16 WEE ET AL.

TABLE5

(Continued

)

Fixed

effects

Randomeffects

Interestdim

ension

Intercep

tAbility

PE

E2P×E

Intercep

tSlope

Covariance

Residual

Audiovisualarts

M1

102.052

1.477

−0.006

1.247

4.934

0.035

−0.005

213.637

(0.832)

(0.057)

(0.100)

(1.262)

M2

103.287

1.477

−0.025

0.693

−2.725

0.355

4.619

0.001

−0.001

213.425

(1.318)

(0.057)

(0.075)

(1.297)

(2.308)

(0.114)

Interpersonal

M1

102.103

1.479

0.052

0.298

6.377

0.007

−0.026

213.518

(0.929)

(0.057)

(0.044)

(0.314)

M2

102.354

1.481

0.052

0.114

−0.028

0.039

6.865

0.001

−0.004

213.337

(1.192)

(0.057)

(0.035)

(0.479)

(0.100)

(0.014)

Administrative

M1

101.909

1.469

−0.037

1.267

4.850

0.051

−0.213

212.650

(0.819)

(0.058)

(0.093)

(0.309)

M2

102.653

1.472

−0.053

0.847

−0.185

0.093

3.214

0.023

−0.061

212.647

(0.873)

(0.058)

(0.071)

(0.397)

(0.161)

(0.038)

Foodservices

M1

102.012

1.468

−0.134

3.086

2.954

0.019

−0.046

213.498

(0.659)

(0.057)

(0.084)

(1.063)

M2

102.502

1.468

−0.134

2.949

−1.573

0.130

3.508

0.024

−0.071

213.491

(1.017)

(0.057)

(0.088)

(1.397)

(2.452)

(0.154)

Note.Statistically

sign

ifican

tcross-levelinteractionterm

sareunderlin

ed.M

1=Vocationalinterestmaineffectsmodel,M

2=Vocationalinterestcongruen

ceeffectsmodel,P=Person-level

interest,E=Environmen

t/Occupation-levelinterest,P×E=Person×Environmen

t/Occupationinterestcross-levelinteraction(i.e.,vocationalinterestcongruen

ce),an

dCov.=

Covariance.

Stan

darderrorsarepresentedinparen

theses.Bolded

values

arestatistically

sign

ifican

tat

p<.05.

WEE ET AL. 17

F IGURE 1 Vocational interest congruence effect (cross-level person interest× occupation interest interaction)on core technical performance for each vocational interest dimension. Two asterisks (**) indicate a statisticallysignificant cross-level interaction at p< .05. One asterisk (*) indicates a statistically significant simple slope at p< .05

replicate the finding that in predominantly male [female] occupations, the gender gap in job performance would favor

men [women]. However, because our Study 1 sample included predominantly male occupations and gender-balanced

occupations (i.e., it did not include predominantly female occupations), we could not directly test the expectation

regarding predominantly female occupations. Table 6 (Model 2a [M2a]) provides the multilevel results for person–

occupationgender congruencepredicting jobperformance.M2a includes aperson’s gender (Sex:male=0, female=1),

occupation-level gender composition (Prop.: proportion ofwomen in occupation), and the person gender×occupation

gender composition (Sex × Prop.) cross-level interaction. The model has a random intercept for variation in mean job

performance across occupations, and a random slope for variation in the gender gap in job performance across occu-

pations.

As shown inTable6 (M2a), therewas apositive persongender×occupation gender composition (Sex×Prop.) cross-

level interaction (𝛾 = 32.994; p < .05), which explained 68.2% of the random slope variation. We plotted this person–

occupation gender interaction for men (solid line) and women (dashed line) in Figure 2. As can be seen in Figure 2,

for male-dominated occupations (i.e., 95%male), the gender gap in job performance (i.e., difference between the solid

18 WEE ET AL.

TABLE6

Multilevelm

odelingresultsforcross-levelperson–occupationinterestcongruen

ce(controllingforperson-occupationgender

congruen

ce)predictingcore

technicalperform

ance

Fixed

effects

Randomeffects

Variable

Intercep

tAbility

Sex

Prop.

PE

E2Sex×E

Prop.×

PSex×Prop.

P×E

Intercep

tSlope

Cov.

Residual

Gen

der

congruen

ce

M2a

102.764

1.510

−7.386

−9.223

32.994

5.780

18.282

−98.379

209.685

(1.136)

(0.057)

(2.631)

(5.033)

(9.576)

Structural/

Machines

M1

101.477

1.479

0.210

0.293

0.075

0.100

5.009

0.017

0.024

208.214

(1.136)

(0.057)

(0.060)

(0.281)

(0.084)

(0.019)

M2

101.153

1.497

−2.279

2.957

0.063

0.840

−0.028

−0.460

1.351

18.341

0.135

3.581

0.006

0.016

205.563

(1.267)

(0.056)

(1.791)

(13.495)

(0.079)

(0.610)

(0.142)

(0.517)

(0.460)

(8.062)

(0.028)

Rugged

outdoors

M1

101.542

1.423

0.234

0.174

0.095

0.057

5.540

0.025

0.130

209.237

(1.030)

(0.057)

(0.066)

(0.395)

(0.084)

(0.025)

M2

104.042

1.446

2.040

−23.634

0.280

−0.765

0.081

−3.123

−0.125

−16.483

0.019

4.985

0.033

0.158

205.675

(2.074)

(0.057)

(3.431)

(16.296)

(0.177)

(0.996)

(0.114)

(1.629)

(1.197)

(22.574)

(0.083)

Protective

services

M1

103.380

1.492

0.163

−0.082

−0.447

0.042

2.036

0.005

−0.007

213.569

(0.815)

(0.057)

(0.070)

(0.454)

(0.189)

(0.044)

M2

103.811

1.519

−6.826

−8.830

0.178

−0.215

−0.411

1.606

0.019

37.267

0.036

1.594

0.010

−0.012

209.817

(0.748)

(0.057)

(1.670)

(6.697)

(0.094)

(0.942)

(0.340)

(0.598)

(0.470)

(5.484)

(0.056)

(Continues)

WEE ET AL. 19

TABLE6

(Continued

)

Fixed

effects

Randomeffects

Variable

Intercep

tAbility

Sex

Prop.

PE

E2Sex×E

Prop.×

PSex×Prop.

P×E

Intercep

tSlope

Cov.

Residual

Skilled

/

Technical

M1

102.839

1.476

0.120

1.448

−1.291

0.415

2.753

0.009

−0.017

212.841

(0.850)

(0.057)

(0.086)

(0.808)

(0.860)

(0.108)

M2

103.295

1.496

−5.484

−10.523

0.045

1.367

−0.836

0.243

0.892

29.144

0.428

2.981

0.019

−0.045

209.195

(1.119)

(0.057)

(1.644)

(3.970)

(0.115)

(0.842)

(0.890)

(1.208)

(0.526)

(4.444)

(0.117)

Audiovisual

arts

M1

103.287

1.477

−0.025

0.693

−2.725

0.355

4.619

0.001

−0.001

213.425

(1.318)

(0.057)

(0.075)

(1.297)

(2.308)

(0.114)

M2

103.257

1.501

−5.064

−10.439

−0.037

1.174

−0.970

0.553

0.118

26.731

0.328

5.235

0.003

−0.007

210.202

(1.542)

(0.057)

(1.638)

(5.004)

(0.096)

(1.430)

(2.454)

(1.360)

(0.456)

(4.747)

(0.120)

Interpersonal

M1

102.354

1.481

0.052

0.114

−0.028

0.039

6.865

0.001

−0.004

213.337

(1.192)

(0.057)

(0.035)

(0.479)

(0.100)

(0.014)

M2

102.886

1.503

−5.659

−10.639

0.069

0.147

0.009

0.035

−0.127

28.296

0.041

6.221

0.002

−0.009

210.094

(1.464)

(0.057)

(1.582)

(6.266)

(0.049)

(0.523)

(0.094)

(0.276)

(0.240)

(4.553)

(0.017)

Administrative

M1

102.653

1.472

−0.053

0.847

−0.185

0.093

3.214

0.023

−0.061

212.647

(0.873)

(0.058)

(0.071)

(0.397)

(0.161)

(0.038)

M2

103.680

1.496

−5.254

−11.944

−0.035

0.896

−0.197

−0.080

−0.220

27.944

0.090

2.481

0.037

−0.082

209.418

(0.879)

(0.057)

(2.529)

(4.834)

(0.111)

(0.418)

(0.158)

(0.870)

(0.569)

(9.823)

(0.055)

Foodservices

M1

102.502

1.468

−0.134

2.949

−1.573

0.130

3.508

0.024

−0.071

213.491

(1.017)

(0.057)

(0.088)

(1.397)

(2.452)

(0.154)

M2

103.932

1.497

−5.894

−12.554

−0.081

3.122

−2.813

−0.764

−0.547

30.554

0.113

3.304

0.041

−0.078

209.999

(1.146)

(0.057)

(3.397)

(4.444)

(0.129)

(1.496)

(2.560)

(4.386)

(0.608)

(12.848)

(0.197)

Note.Bolded

values

arestatistically

sign

ifican

tatp<.05.Statisticallysign

ifican

tcross-levelP×Einteractionterm

sareunderlin

ed.M

1=Vocationalinterestcongruen

cemodel,M

2=Voca-

tionalinterestcongruen

ceplusgender

congruen

cemodel,andM2a=Gen

der

congruen

cemodel(person–occupationgender

interaction).Sexwas

dummycoded

(male=0;fem

ale=1).

Prop.=

Proportionofw

omen

inan

occupation,P=Person-levelinterest,E=Environmen

t/Occupation-levelinterest,P×E=Person×Environmen

t/Occupationinterestcross-levelinter-

action(i.e.,vocationalinterestcongruen

ce),an

dCov.=

Covariance.Standarderrorsarepresentedinparen

theses.

20 WEE ET AL.

F IGURE 2 Gender congruence effect(cross-level person gender× occupationgender composition interaction) on coretechnical performance. Two asterisks (**)indicate a statistically significantcross-level interaction at p< .05. Oneasterisk (*) indicates a statisticallysignificant simple slope (i.e.,individual-level gender gap in jobperformance) at p< .05. Simple slopes:(a) for male-dominated occupations,predicted d̂ = −0.36 (men have highercore technical performance scores thanwomen), (b) for gender-balancedoccupations, predicted d̂ = 0.57 (womenhave higher core technical performancescores thanmen)

line and the dashed line) indicated that men had higher job performance scores than women (B= –5.736, SE= 2.325,

p< .05; predicted d̂ = −0.36; see also Table 7). Conversely, for balanced occupations (i.e., 50%male), the gender gap in

job performance was reversed such that women had higher job performance scores than men (B= 9.111, SE= 3.521,

p< .05; predicted d̂ = 0.57). These results are consistentwith previous research (i.e., Joshi et al., 2015): the gender gap

in performance favorsmen (overwomen) in predominantlymale occupations, and thismale advantage is reduced—and

even reversed—in gender-balanced occupations.

As highlighted earlier, gender is related to vocational interests at both individual and occupational levels. Given

the cross-level gender congruence effect on job performance, it is possible that the vocational interest congruence

effects on job performance we claimed in support of Hypothesis 1 could in part be spuriously due to the cross-level

person–occupation gender interaction. Thus, we re-examinedHypothesis 1 (the person× occupation interest interac-

tion hypothesis) while also controlling for the person × occupation gender interaction effect. As shown in Table 6,

we estimated two models for each vocational interest dimension. Model 1 (M1) is the vocational interest congru-

ence model (i.e., the same model as M2 in Table 5). Model 2 (M2 in Table 6) is the vocational interest congruence

model, including the gender congruence control variables (individual-level gender, occupation-level gender compo-

sition, gender × gender composition, gender × occupation-level interests, and interests × gender composition). Each

model included a random intercept and random slope. This set of analyses (M1 and M2) was conducted eight times,

once for each interest dimension.

As can be seen from these results, even after controlling for all five gender covariates, four of the six vocational

interest congruence effects remained statistically significant (see Table 6,M2). That is, therewas a person–occupation

interest interaction effect for the structural/machines (𝛾 = 0.135; p < .05), skilled/technical (𝛾 = 0.428; p < .05),

WEE ET AL. 21

audiovisual arts, (𝛾 = 0.328; p < .05), and interpersonal (𝛾 = 0.041; p < .05) interest dimensions, even after control-

ling for the person–occupation gender interaction effect (along with various other lower order terms). Thus, a per-

son’s vocational interest was positively related to job performance in occupations characterized by high levels of that

interest, and negatively related (or unrelated) to job performance in occupations characterized by low levels of that

interest. In support of the robustness of the vocational interest congruence effect, these results were obtained even

after controlling for the statistically significant gender congruence effect.

Furtherwenote that, after controlling for vocational interest congruence, the gender congruence effect on job per-

formance remained statistically significant for seven of the eight vocational interest dimensions (see Table 6M2). That

is, there was a statistically significant person–occupation gender interaction effect on job performance when control-

ling for the following vocational interest dimensions: structural/machines (𝛾Sex × Prop. = 18.341; p < .05), protective

services (𝛾Sex × Prop. = 37.267; p < .05), skilled/technical (𝛾Sex × Prop. = 29.144; p < .05), audiovisual arts (𝛾Sex × Prop. =

26.731; p < .05), interpersonal (𝛾Sex × Prop. = 28.296; p < .05), administrative (𝛾Sex × Prop. = 27.944; p < .05), and food

services (𝛾Sex × Prop. = 30.554; p< .05). Taken together, these results suggest that vocational interest congruence and

gender congruence can be thought of as two distinct effects on job performance.

3.4 Examining the person–occupation gender congruence effect using separatemeasures of job performance

Wenext examinedwhether the person–occupation gender congruence interaction effect could be obtained for differ-

entmeasuresof jobperformance: hands-on tests, jobknowledge tests, trainingknowledge tests,6and jobperformance

ratings. These results are presented in Appendix C. Consistent with previous research on rater discrimination, the

gender congruence effect was observed for occupation-specific performance ratings (𝛾 = 10.046; p < .05;

VAF = 56.0%). Additionally, the gender congruence effect was also observed for each of the following measures

of job performance: job knowledge tests (𝛾 = 33.710; p < .05; VAF = 57.2%), hands-on tests (𝛾 = 48.731; p < .05;

VAF = 81.5%), and training knowledge tests (𝛾 = 46.890 p < .05; VAF = 41.0%). That is, these results demonstrate

gender congruence effects for knowledge-based (i.e., job knowledge tests and training knowledge tests) and hands-

on7(i.e., work samples) measures of job performance, in addition to job performance ratings. These results suggest

that the person × occupation gender interaction effect is robust across ratings-based and skills-based measures of

job performance, and cannot be completely accounted for by prejudicial biases that might be associated with rater

discrimination. Instead, some of these results might be cautiously interpreted to imply pipeline issues associated with

gender differences in socialization (i.e., different experiences and interests of men vs. women that in turn relate to the

different levels of job-relevant knowledge and skill they acquire).8

4 STUDY 2

Although Study 1 leveraged a large, multi-occupation sample (N= 3,532) with multisource data, one limitation is that

the hypotheses were examined in a sample that was predominantly male: % male in the sample ranged from 44% to

100% across occupations, with 86% male in the overall sample. Our Study 1 findings are thus based upon occupa-

tions that range from male-dominated to gender-balanced, and as such might not generalize to the range of occupa-

tions fromgender-balanced to female-dominated. To elaborate,webelieve theperson–occupation gender congruence

phenomenon applies to bothmale-typed and female-typed jobs, but Study 1 (Project Amilitary sample) did not permit

an evaluation of this belief. As such, we attempted to replicate the person–occupation gender congruence effect in

predominantly female occupations.9

22 WEE ET AL.

TABLE7

Simpleslopes

analyses

forcross-levelperson–occupationinterestcongruen

ceandperson–occupationgender

congruen

cepredictingcore

technicalperform

ance

Occupation-levelinterest

Lowoccupationinterest(–1SD

belowmean)

Highoccupationinterest(+1SD

abovemean)

Variable

B𝜷

SEt-value

B𝜷

SEt-value

𝚫𝜷

Individual-levelinterest

Structural/Machines

−0.096

−0.039

0.082

−1.162

0.516

0.209

0.087

5.928

0.25

Rugged

outdoors

0.087

0.039

0.090

0.967

0.381

0.171

0.096

3.980

0.13

Protectiveservices

0.088

0.021

0.096

0.917

0.237

0.058

0.112

2.112

0.04

Skilled

/Technical

−0.218

−0.043

0.124

−1.754

0.458

0.090

0.122

3.744

0.13

Audiovisualarts

−0.261

−0.054

0.107

−2.440

0.211

0.044

0.106

1.989

0.10

Interpersonal

−0.051

−0.024

0.049

−1.046

0.156

0.074

0.052

3.003

0.10

Administrative

−0.232

−0.078

0.106

−2.197

0.125

0.042

0.099

1.264

0.12

Foodservices

−0.209

−0.048

0.128

−1.629

−0.060

−0.014

0.121

−0.497

0.03

Gen

der

compositioninoccupation

Male-dominated

(95%male)

Gen

der-balanced(50%male)

Variable

Bd̂

SEt-value

Bd̂

SEt-value

𝚫d̂

Personsex(Male

[♂]=

0;

Female[♀]=

1)

−5.736

−0.359

2.325

-2.467

(♂higher)

9.111

0.571

3.521

2.588

(♀higher)

0.93

Note.Bolded

andunderlin

edvalues

indicateastatistically

sign

ifican

tsimpleslopeat

p<.05.Standardized

regressioncoefficien

tcalculatedas𝛽=

B(SDx∕SD

y).

WEE ET AL. 23

TABLE 8 (Study 2) Occupation-level sample sizes, sex proportions, andmean vocational interests

Occupation

code NGender composition

(proportion female) R I A S E C

1910 26 0.27 31.58 28.19 22.73 28.38 27.96 27.69

1165 7 0.29 16.00 22.71 24.29 29.43 30.86 28.00

570 6 0.33 18.17 19.83 20.33 27.00 31.17 26.00

2050 12 0.42 24.92 23.67 14.42 24.33 24.83 30.58

393 7 0.43 31.00 21.57 17.71 29.57 24.86 27.57

346 15 0.47 18.73 16.53 20.13 27.87 28.27 27.80

2003 14 0.50 27.79 25.79 23.93 31.64 29.50 33.43

1101 52 0.54 23.90 24.40 20.56 28.21 25.10 28.37

221 20 0.55 20.85 19.20 19.60 32.35 28.55 28.75

334 304 0.57 23.62 25.48 21.36 26.30 23.09 27.94

1140 5 0.60 24.8.0 31.40 30.40 30.60 27.00 20.20

2001 44 0.64 21.25 16.84 20.82 27.45 23.95 28.98

1102 347 0.65 21.71 23.54 22.97 29.79 29.28 30.01

301 115 0.69 22.41 21.19 22.20 30.02 26.91 28.30

341 13 0.69 18.15 15.23 23.00 30.92 29.62 28.15

201 38 0.71 19.16 19.66 25.34 33.89 30.39 26.55

212 17 0.71 17.59 22.41 26.41 32.53 27.53 27.12

560 67 0.72 18.39 20.52 17.46 25.70 24.51 34.67

2010 59 0.73 20.36 19.86 21.75 31.75 28.90 31.20

230 9 0.78 16.22 23.67 25.22 33.22 29.00 28.33

235 9 0.78 20.67 21.56 20.44 33.89 22.33 25.00

345 91 0.78 19.70 23.42 22.87 30.30 25.37 30.40

343 56 0.82 20.27 19.45 22.07 29.96 27.12 29.68

501 29 0.83 21.72 21.59 24.10 32.97 30.72 32.66

1173 5 1.00 14.80 16.80 23.20 26.20 24.60 24.60

Note. Male= 0; Female= 1. Gender Composition= 1.00means the occupationwas all-female.

Abbreviations: R=Realistic; I= Investigative; A=Artistic; S= Social; E= Enterprising; C=Conventional.

5 STUDY 2 METHOD

5.1 Sample

Data were collected from a large national service organization for the purpose of examining the relationship between

vocational interests and job performance. Results based on subsets of these data have been previously reported in

Kieffer, Schinka, and Curtiss (2004) and Schinka, Dye, and Curtiss (1997). Participants were 1,367 individuals who

reported their gender and who also belonged to occupations for which we had at least five respondents. Each partic-

ipant was employed in one of 25 occupations. The % female in the sample ranged from 27% to 100% across occupa-

tions, with 64.2% female in the overall sample (62.2% of the sample had at least a college degree; average job tenure

was 11.56months [SD= 7.25]). Descriptive statistics for the sample are presented in Table 8.

24 WEE ET AL.

5.2 Measures

Vocational interests were measured using the Self-Directed Search (SDS) Form R (Holland, 1985). The 228-item SDS

assesses Holland’s six RIASEC dimensions. Some items describe job titles, activities, and competencies; and partic-

ipants are asked to indicate whether they “like” or “dislike” each item. Other items describe abilities and skills, and

participants are asked to rate their level of ability/skill on a 7-point scale from 1 = Low to 4 = Average to 7 = High. In

this dataset, we only had access to the six RIASEC dimension scores that were generated, not item-level data. Scale

reliabilities (reported in Kieffer et al., 2004) ranged from α = .78 (Realistic interests) to α = .89 (Investigative inter-

ests). Job performancewasmeasured using six items (fromKieffer et al., 2004; α= .89) to assess an individual’s overall

work quality (e.g., quality, efficiency, accuracy). Supervisors rated employees on a scale from 1=Well below average to

5=Well above average.

6 STUDY 2 RESULTS

Individual-level descriptive statistics and correlations appear in Table 9. On average, men have stronger interests than

women on Realistic (d= –1.01, p< .05), Investigative (d= –0.64, p< .05), and Enterprising (d= –0.22, p< .05) dimen-

sions. By contrast, on average women have stronger interests than men on Conventional (d = 0.40, p < .05), Social

(d= 0.26, p< .05), and Artistic (d= 0.21, p< .05) interest dimensions. In this majority female sample, therewas a small

gender gap on job performance favoring women (d= 0.14, p< .05) when gender differences were averaged across all

occupations.

To estimate the hypothesized relationships involving vocational interests, gender, and job performance, we used

hierarchical linear models with the same specifications as in Study 1. The multilevel results are presented in Table 10,

and Figures 3 and 4. As shown in Figure 3, the relationship between gender and job performance ratings depends upon

the gender composition of the occupation: formale-dominated occupations (i.e., 95%male, seeKanter, 1977), the gen-

der gap in job performance (i.e., difference between the solid line and the dashed line) indicated men had higher job

performance ratings than women (B= –0.42, SE= 0.20, p< .05; predicted d̂ = −0.64); for female-dominated occupa-

tions (i.e., 95% female), the gender gap in job performance indicated women had higher job performance ratings than

men (B= 0.33, SE= 0.12, p< .05; predicted d̂ = 0.50; see also Table 11).

For vocational interest congruence, Table 10 andFigure 4 show the relationship betweenRealistic interests and job

performance ratings depends on the occupational-level vocational interest (𝛾 = 0.002, p< .05; supportingH1). Specif-

ically, the individual-level relationship between Realistic interests and job performance is more positive (B = 0.003,

SE= 0.003, p > .05) when the occupation is characterized by high levels of Realistic interests, and more negative (B=

–0.006, SE = 0.003, p < .10; see Table 11) when the occupation is characterized by low levels of Realistic interests.

However, after controlling for person–occupationgender congruence, theperson–occupationvocational interest con-

gruence was no longer significant (𝛾 = 0.001, p> .05).

Comparing Study 2 results (service organization) to Study 1 results (military sample), the cross-level interactions

predicting job performance ratings were consistently supported for: (a) gender and (b) Realistic interests (i.e., struc-

tural/machines and rugged outdoors)—see Appendices C and D. That is, these same results—that is, the gender cross-

level effect and the Realistic interests cross-level effect on job performance ratings—replicated across both a predom-

inantly male (Study 1) and amajority female (Study 2) sample.

7 GENERAL DISCUSSION

In order to enhance understanding of how vocational interests relate to job performance, we tested a core tenet

of vocational interest theory—namely, the person-occupation interest congruence effect. This fundamental vocational

WEE ET AL. 25

TABLE9

(Study2)D

escriptive

statistics

andcorrelationsforvocationalinterestsandjobperform

ance

ratings

Men

(N=489)

Women

(N=878)

ICCvalues

Correlations

Variable

MSD

MSD

Sexd

ICC(1)

ICC(2)

1.

2.

3.

4.

5.

6.

7.

1.G

ender

–

2.Realistic

28.47

10.73

18.19

9.02

−1.01

.06

.77

−.43

–

3.Investigative

27.10

10.57

20.40

10.08

−0.64

.05

.75

−.28

.48

–

4.A

rtistic

20.53

11.19

22.81

10.85

0.21

.01

.40

.09

.16

.32

–

5.Social

27.40

9.99

29.94

9.80

0.26

.04

.68

.10

.13

.27

.45

–

6.Enterprising

28.16

10.72

25.82

10.01

−0.22

.04

.67

−.12

.26

.27

.35

.60

–

7.C

onven

tional

26.82

10.04

30.72

8.92

0.40

.03

.64

.18

.12

.09

.13

.31

.33

–

8.Jobperform

ance

ratings

3.41

0.64

3.50

0.66

0.14

.00

.21

.06

−.02

.01

−.06

−.05

−.06

.04

Note.N=1,367.Bolded

values

arestatistically

sign

ifican

tat

p<.05.M

ale=0,Fem

ale=1.A

ll|r|≥.054arestatistically

sign

ifican

tat

p<.05.

26 WEE ET AL.

TABLE10

(Study2)Service

organization,m

ultilevelm

odelingresultsforcross-levelperson–occupationinterestcongruen

ce(controllingforperson–occupationgender

congruen

ce)predictingjobperform

ance

ratings

Fixed

effects

Randomeffects

Variable

Intercep

tSex

Prop.

PE

E2Sex×E

Prop.×

PSex×Prop.

P×E

Intercep

tSlope

Cov.

Residual

Gen

der

congruen

ceM2a

3.466

−0.464

−0.085

0.833

0.000

0.000

0.000

0.425

(0.163)

(0.222)

(0.263)

(0.346)

Realistic

M1

3.472

−0.001

−0.011

0.000

0.002

0.001

0.000

0.000

0.422

(0.024)

(0.003)

(0.009)

(0.001)

(0.001)

M2

3.211

−0.099

0.320

0.010

0.011

0.001

−0.014

−0.016

0.257

0.001

0.000

0.000

0.000

0.421

(0.248)

(0.346)

(0.390)

(0.022)

(0.016)

(0.002)

(0.024)

(0.034)

(0.538)

(0.001)

Investigative

M1

3.478

0.000

−0.013

−0.001

0.00

10.001

0.000

0.000

0.426

(0.025)

(0.002)

(0.008)

(0.002)

(0.001

)

M2

3.424

−0.414

−0.043

0.014

−0.006

0.001

0.010

−0.019

0.77

70.001

0.000

0.000

0.000

0.424

(0.204)

(0.258)

(0.316)

(0.014)

(0.013)

(0.002)

(0.017)

(0.022)

(0.401

)(0.001)

Artistic

M1

3.465

–0.004

0.001

0.003

0.001

0.002

0.000

0.000

0.426

(0.025)

(0.002

)(0.011)

(0.002)

(0.001)

M2

3.416

–0.429

−0.034

0.011

−0.023

0.002

0.024

−0.024

0.801

0.001

0.000

0.000

0.000

0.421

(0.168)

(0.228

)(0.269)

(0.011)

(0.017)

(0.002)

(0.021)

(0.017)

(0.355)

(0.001)

(Continues)

WEE ET AL. 27

TABLE10

(Continued

)

Fixed

effects

Randomeffects

Variable

Intercep

tSex

Prop.

PE

E2Sex×E

Prop.×

PSex×Prop.

P×E

Intercep

tSlope

Cov.

Residual

Social

M1

3.470

−0.003

−0.006

0.002

0.000

0.004

0.000

0.000

0.424

(0.034)

(0.003)

(0.011)

(0.004)

(0.001)

M2

3.308

−0.353

0.140

0.004

−0.032

0.001

0.026

−0.012

0.66

70.000

0.000

0.000

0.000

0.421

(0.181)

(0.248)

(0.289)

(0.014)

(0.015)

(0.003)

(0.019)

(0.022)

(0.386

)(0.001)

Enterprising

M1

3.491

–0.004

−0.004

−0.002

0.000

0.003

0.000

0.000

0.425

(0.039)

(0.002

)(0.010)

(0.005)

(0.001)

M2

3.419

–0.379

0.013

0.011

−0.020

−0.001

0.019

−0.022

0.68

70.000

0.000

0.000

0.000

0.424

(0.171)

(0.230

)(0.268)

(0.011)

(0.011)

(0.004)

(0.014)

(0.017)

(0.357

)(0.001)

Conven

tional

M1

3.464

0.003

0.007

0.003

0.000

0.002

0.000

0.000

0.428

(0.025)

(0.002)

(0.011)

(0.003)

(0.001)

M2

3.492

−0.513

−0.128

0.015

0.003

0.002

−0.007

−0.020

0.893

0.001

0.000

0.000

0.000

0.426

(0.180)

(0.241)

(0.288)

(0.012)

(0.019)

(0.002)

(0.022)

(0.018)

(0.375)

(0.001)

Note.Statistically

sign

ifican

tcross-levelP×Einteractionterm

sareunderlin

ed.Coefficien

tsinboldface

typearestatistically

sign

ifican

t(p<.05,two-tailedtest),an

dcoefficien

tsinbolditalics

arep<.10(two-tailedtest;i.e.,p<.05forone-tailedtest).Thistablerepresentsan

attemptedreplicationofresultsfromthemilitary

organ

ization,usingdatafromalargeserviceorgani-

zation.B

ecau

sethiswas

anattemptedreplication,somescholars

mightad

vocate

theuse

ofone-tailedtests.Wereportboth

two-tailedan

done-tailedresults.Allstatistically

sign

ifican

t

interactioneffectsintheseserviceorgan

izationdatawereinthesamedirectionsas

previouslyfoundinthemilitary

organ

izationdata(i.e.,interestspredictjobperform

ance

more

positively

[negatively]when

theoccupation-levelvocationalinterestishigh[low],an

dthegender

gapinjobperform

ance

favorswomen

[men

]more

totheextenttherearemore

women

[men

]inthe

occupation).M1=Vocationalinterestcongruen

cemodel,M

2=Vocationalinterestcongruen

ceplusgender

congruen

cemodel,andM2a=Gen

der

congruen

cemodel(person–occupation

gender

interaction).Sexwas

dummycoded

(Male=0;Fem

ale=1).Prop.=

Proportionofwomen

inan

occupation,P

=Person-levelinterest,E

=Environmen

t/Occupation-levelinterest,

P×E=Person×Environmen

t/Occupationinterestcross-levelinteraction(i.e.,vocationalinterestcongruen

ce),an

dCov.=

Covariance.Standarderrorsarepresentedinparen

theses.

28 WEE ET AL.

F IGURE 3 (Study 2) Gendercongruence effect (cross-level persongender× occupation gender compositioninteraction) on job performance ratings.Two asterisks (**) indicate a statisticallysignificant cross-level interaction atp< .05. One asterisk (*) indicates astatistically significant simple slope (i.e.,individual-level gender gap in jobperformance) at p< .05. Simple slopes:(a) for male-dominated occupations,predicted d̂ = −0.64 (men have higher jobperformance ratings thanwomen), (b) forfemale-dominated occupations, predictedd̂ = 0.50 (women have higher jobperformance ratings thanmen)

interest congruence hypothesis posits that the relationship between vocational interests and job performance

depends on the occupational environment (and thus implies a cross-level interaction). Previous studies have not

directly tested this key effect, and thus the present study provides initial support for the core hypothesis connecting

vocational interests to job performance.

In testing this focal hypothesis, we controlled for the well-known effects of general mental ability (in Study 1), and

person–occupation gender congruence (i.e., the gender cross-level interaction; Joshi et al., 2015; Sackett et al., 1991),

on job performance.We also examined the person–occupation interest congruence effect using a composite measure

of job performance (core technical performance; Campbell, McHenry, et al., 1990), as well as job performance ratings.

Consistent with our focal hypothesis, in Study 1 we found support (on six out of eight vocational interest dimen-

sions) for a person–occupation interest congruence interaction. A person’s vocational interest was positively related

to job performance when an occupation was characterized by high levels of that interest. Likewise, a person’s voca-

tional interest was negatively related (or unrelated) to job performance when an occupation was characterized by

low levels of that interest. This person–occupation interest congruence effect was also observed while controlling for

general mental ability, and for person–occupation gender congruence. When using the criterion of job performance

ratings, both Study 1 and Study 2 found the person–occupation interest congruence effect for the Realistic interest

dimension.

Additionally, consistent with previous research (e.g., Joshi et al., 2015; Sackett et al., 1991) indicating that the gen-

der gap in jobperformance ratings ismoderatedby the gender compositionof theoccupational environment,we found

a cross-level person–occupation gender congruence interaction on job performance ratings (in Studies 1 and 2). Men

WEE ET AL. 29

F IGURE 4 (Study 2) Vocationalinterest congruence effect (cross-levelperson interest× occupation interestinteraction) on job performance ratingsfor realistic and investigative vocationalinterest dimensions. Two asterisks (**)indicate a statistically significantcross-level interaction at p< .05

received higher job performance ratings thanwomen in predominantlymale occupations, andwomen received higher

job performance ratings thanmen in predominantly female occupations.

To restate, both the results from the predominantly male sample in Study 1 (Table 6; Figure 2) and the results from

themajority female sample in Study 2 (Table 10; Figure 3) mutually supported the person–occupation gender congru-

ence interaction, and also supported the person–occupation interest congruence interaction for Realistic interests.

The extent to which gender congruence effects and vocational interest congruence effects overlap with each other

might vary by setting, but results suggest they can sometimes be disambiguated.10

Further, as requested by a reviewer, we explicitly compared the relative magnitudes of each interest congruence

interaction effect against the gender congruence interaction effect and the combined/total of these two interaction

effects. That is, we partitioned the ΔR2 due to the two interaction effects combined (i.e., the two product terms) into

three components: (a) variance uniquely due to the interest cross-level interaction, (b) variance uniquely due to the

gender cross-level interaction, and (c) variance due to the shared overlap between the interest interaction and the

gender interaction (R2 is calculated as predicted score variance divided by sumof predicted score variance plus L1 and

L2 variances [i.e., σ2 and τ00]; LaHuis, Blackmore, & Bryant-Lees, 2019, p. 357; Nakagawa & Schielzeth, 2013). Across

all models for which the interest cross-level interaction was significant, in the Study 1 military sample 24.4% of the

total effect was due to the interest interaction, 65.3% of the total effect was due to the gender interaction, and 10.2%

of the total effectwasdue to the shared/overlappingeffects of interest andgender interactions. For theStudy2 service

sample, these corresponding percentages were 49.1% (interest interaction), 31.3% (gender interaction), and 19.6%

(shared/overlapping effects of interest and gender interactions). To summarize, itwould appear that the unique effects

of the vocational interest cross-level interaction and gender cross-level interaction are on average larger than the

effect of the overlap between these two cross-level interactions. That is, the magnitudes of the effects are consistent

30 WEE ET AL.

TABLE11

(Study2)Sim

pleslopes

analyses

forcross-levelperson–occupationinterestcongruen

ceandperson–occupationgender

congruen

cepredictingjobperform

ance

ratings

Occupation-levelinterest

Lowoccupationinterest(–1SD

belowmean)

Highoccupationinterest(+1SD

abovemean)

Variable

B𝜷

SEt-value

B𝜷

SEt-value

𝚫𝜷

Individual-levelinterest

Realistic

−0.006

−0.097

0.003

–1.764

0.003

0.048

0.003

0.972

0.15

Investigative

−0.003

−0.048

0.003

−1.110

0.004

0.064

0.003

1.308

0.11

Artistic

−0.006

−0.100

0.003

–1.950

−0.002

−0.033

0.003

−0.614

0.07

Social

−0.004

−0.059

0.004

−1.105

−0.002

−0.030

0.003

−0.653

0.03

Enterprising

−0.005

−0.076

0.004

−1.381

−0.004

−0.061

0.003

−1.039

0.02

Conven

tional

0.002

0.028

0.003

0.787

0.004

0.057

0.003

1.319

0.03

Gen

der

compositioninoccupation

Male-dominated

(95%male)

Female-dominated

(95%female)

Variable

Bd̂

SEt-value

Bd̂

SEt-value

𝚫d̂

PersonSex

−0.423

−0.640

0.205

−2.059

0.328

0.496

0.116

2.813

1.14

(Male[♂]=

0;

Female[♀]=

1)

(♂higher)

(♀higher)

Note.Coefficien

tsinboldface

typearestatistically

sign

ifican

t(p<.05,two-tailedtest),an

dcoefficien

tsinbolditalicsarep<.10(two-tailedtest;i.e.,p<.05forone-tailedtest).Thistable

representsan

attemptedreplicationofresultsfromthemilitary

organ

ization,usingdatafromalargeserviceorgan

ization.Becau

sethiswas

anattemptedreplication,somescholarsmight

advocate

theuse

ofo

ne-tailedtests.Wereportboth

two-tailedan

done-tailedresults.Stan

dardized

regressioncoefficien

tcalculatedas𝛽=

B(SDx∕SD

y).

WEE ET AL. 31

with our interpretation that these can be considered two distinct cross-level interactions (interests and gender) on job

performance.

At this point, a discussion of the magnitudes of the cross-level interaction effects is in order. We first note that

the finding of a statistically significant effect (p < .05) does not necessarily warrant the conclusion of practical signifi-

cance. For example, an unstandardized multilevel coefficient of γ= 0.002 in the Study 2 service sample is statistically

significant, but unstandardized coefficients do not give a good sense of the magnitude of the interaction effect. We

therefore calculated the difference between standardized simple slopes at high (+1 SD) versus low (–1 SD) levels of

the moderator (i.e., occupation-level vocational interests). These Δ𝛽 magnitudes appear in Tables 7 and 11. As shown

in Table 7 (Study 1), the Δ𝛽 for structural/machines is .25, and for the other five statistically significant interaction

effects Δ𝛽 ranges from .10 to .13. As shown in Table 11 (Study 2), the Δ𝛽 for Realistic is .15. So, although the unstan-

dardized 𝛾 = 0.002, the Δ𝛽 = .15. This means that as individual-level vocational interests increase by 1 SD, we expect

job performance to increase by .15 more SDs when at high (vs. low) levels of the moderator. Whether we evaluate a

.15 increase in the interest–job performance 𝛽 as practically significant depends on context and the utility assigned to

such increments in job performance.

Finally, we found evidence for cross-level interactions (both for vocational interests and for gender) when looking

exclusively at knowledge- and skill-based measures of job performance. This suggests that the cross-level interaction

effects were not due exclusively to performance rating bias, but might also partly have been due to pipeline issues in

the acquisition of job knowledge and skills.

7.1 Theoretical and practical implications

Our findings advance understanding of how vocational interests relate to job performance in at least three ways.

First and foremost, our study offers the first multilevel test of a key theoretical boundary condition for the vocational

interest–job performance relationship—occupation-level vocational interests. That is, we tested the person × occu-

pation interest interaction effect. This evidence—based upon commensurate measurement of individual- and

occupation-level interests via the same scales (Edwards, 1991; Harrison, 2007)—revealed that vocational interests

can have either positive or negative effects on job performance, depending upon the occupation-level vocational

interests that characterize the context.

Second, our study showed that the person–occupation gender congruence effect on job performancewas obtained

not just for job performance ratings, but also for knowledge- and skill-based measures of job performance. As noted

earlier, previous theory has relied almost exclusively on a stereotype-based discrimination mechanism (e.g., raters’

perceptions of themismatch betweenwomen’s stereotypical roles andmale sex-typed occupations) to explain gender

congruence effects on job performance. However, because person–occupation gender congruence effects were also

obtained for knowledge-basedmeasures of job performance, a discrimination explanation for the gender congruence

effect is not—by itself—sufficient to explain theeffect of gender congruenceonperformance. Tobe clear,we confirmed

the person–occupation gender congruence effect on job performance ratings. Additionally, however, we also obtained

theeffect of gender congruenceonnonrating-basedmeasuresof jobperformance, suchas jobknowledge tests—which

suggests other mechanisms beyond pure rater discriminationmight partly explain our gender congruence findings.

In particular, we believe the current findings provide support for theories emphasizing how individual differences

in the investment of one’s time and resources can result in actual differences in the knowledge and skills one accrues

(e.g., Ackerman, 1996). Under this explanation, one primary reason men and women develop different job knowledge

and skills would be because of gender differences in vocational interests. As shown in Tables 3 and 9, and consistent

with previous research (Su et al., 2009), men have much stronger Realistic interests (e.g., structural/machines, rugged

outdoors, and protective services interest dimensions), whereas women have stronger Social and Conventional inter-

ests (e.g., interpersonal, administrative, and food services interest dimensions), on average. Because a person’s inter-

ests direct their attention toward certain activities and away fromother activities, gender gaps in these interests could

32 WEE ET AL.

cause men and women to gravitate toward occupations at different rates. Women and men can also tend to develop

different sets of domain-specific knowledge and skills, which in turn can affect their performance on a given job. Stated

differently, in some instances in the past, gender differences in vocational interests might have led women to become

more prepared thanmen (on average) for female-sex-typed jobs, and vice-versa.

Beyond Ackerman’s (1996) explanation for the knowledge and skills gap—that is, gender differences in interests

result in differential investment in activities that would develop the knowledge and skills relevant for effective per-

formance in such jobs—we also note Heilman’s (1983) hypothesized mechanism: self-limiting behaviors (which result

from lower expectations of success in gender stereotype-incongruent domains). Very plausibly, individual differences

in expectations, interests, and investments in knowledge and skills are mutually causal and interact over time (Denis-

sen, Zarrett, & Eccles, 2007; Lauermann, Tsai, & Eccles, 2017). Importantly, results of the current study imply that both

mechanisms—the differential investment mechanism and the self-limiting behavior mechanism—could be operating

formen aswell aswomen (i.e., simple slopes in Figures 2 and 3 showperformance gaps favoringwomen aswell asmen,

consistent with person–occupation gender congruence).

Third, vocational interests can have a negative relationship with job performance, if the corresponding

occupational-level interest is low. For example, having an interest in audiovisual arts can impair job performance, if the

occupation is predominantly characterized by low audiovisual arts interests (see Figure 1). So, disinterest (or inter-

est misalignment) can be a liability for job performance. As for what organizations can do with the results, we assert

that vocational interests should be used for personnel selection exclusively in instances where their validity has been

demonstrated, and particularly in occupations that exhibit high degrees of the interest in question. Further, in situa-

tions where gender congruence is low (e.g., women in male-dominated occupations; men in female-dominated occu-

pations), organizations should invest in long-term solutions for attracting and retaining qualified applicants from the

underrepresented gender group. Job previews, internships, mentoring, and job redesign have all been proposed as

effective practices (Bohnet, 2016; Collins, 2001) to encourage gender-counterstereotypic organizational entry and

socialization, to partly mitigate the effects of self-limiting behaviors and differential investment between genders.

7.2 Limitations and future directions

Aswithany study, anumberof limitationswarrant caution in the interpretationandgeneralizationof the research find-

ings. One key strength of the present study is the two large field samples that provided multilevel data (i.e., persons

nested within occupations) on interests and job performance measures. Such samples are indispensable for testing

the focal person–occupation interest congruence hypothesis. Nonetheless, the Study 1 archival Project A concurrent

validation dataset only includes participants fromamilitary context. As posited by vocational interest theory (Holland,

1997) and the attraction–selection–attrition model (Schneider, 1987), people gravitate toward certain work environ-

ments and away from others. Thus, one potential concern is that the range of vocational interests within the military

(Study 1) context may be restricted. Conversely, because military enlistees have a contractual obligation to complete

their term of contract—making it difficult to voluntarily leave the organization even when an occupation is not well-

matched with their interests—it could also be argued that there is wider variation in vocational interests in military

contexts than in civilian organizations. Also, the gender cross-level interaction effects in Study 1may be influenced by

the gendered cultural expectations of the 1980s. The gender congruence effects reported in Study 1 are nonetheless

consistent with those reported in Study 2 and elsewhere, on the basis of a wide range of dates and contexts outside

themilitary (Joshi et al., 2015).

As a second limitation, although the developers of the AVOICE vocational interest instrument intended tomeasure

all six RIASEC interest dimensions, the practical constraint of developing an instrument covering “vocational areas

most important in the Army” (Peterson et al., 1990, p. 269) resulted in the relative oversampling of Realistic interest

content, and the undersampling of other RIASEC content domains (most notably on the Enterprising and Social inter-

est dimensions). The service organization sample in Study 2 suggests that vocational interest congruence effects for

WEE ET AL. 33

supervisor ratings of job performance might be limited to Realistic interest domains, even though the Project A data

(in Study 1) suggest the existence of vocational congruence effects across awider variety of vocational interests when

the criterion is core technical performance.

Third, the current study did not include direct measures of rating bias (e.g., sexism scales, comparisons between

objective vs. subjective measures of the same performance domain) or direct measures of pipeline variables (e.g., past

educational choices, direct indicators of past job opportunities and experiences). As such, future research would help

in enumerating the mechanisms for the consistent person–occupation gender congruence effects observed across all

job performancemeasures in the current study.

8 CONCLUSION

Using a multilevel approach, we assessed the classic notion of vocational interest congruence (i.e., cross-level per-

son × occupation vocational interest interaction) on job performance, and further assessed whether this result could

be distinguished from the gender congruence cross-level interaction effect. Even after controlling for general mental

ability (in Study 1) and the gender congruence effect, a person’s vocational interests often predicted job performance

more positively in environments characterized by high levels of that same vocational interest, and negatively (or not

at all) in environments characterized by low levels of that same interest. We also extended research on the person–

occupation gender interaction effect on job performance (men get higher job performance scores in predominantly

male occupations, and women get higher job performance scores in predominantly female occupations). Indeed, both

the vocational interest–job performance relationship as well as the gender gap in job performance depend heavily on

the occupational context.

ACKNOWLEDGMENTS

We thank the U.S. Army Research Institute for providing the data on which this article is based. The views, opinions,

and findings expressed here are solely those of the authors and do not represent official U.S. Department of the Army

position, policy, or decision unless so designated by other documentation.

ORCID

SerenaWee https://orcid.org/0000-0002-1609-7359

ENDNOTES1 The construct of job performance is measured in multiple ways in the current paper. For Study 1 (Project A military sam-

ple), we used Campbell, McHenry, and Wise’s (1990) composite of core technical performance (i.e., factor 1 from their

5-factor solution, which is the only one of their performance factors/constructs that entailed actual performance of core

job tasks). Consistent with McHenry, Hough, Toquam, Hanson, and Ashworth’s (1990) classic validity study on Project A,

this core technical job performance construct was indexed using the three highest-loading indicators (i.e., hands-on tests,

job knowledge tests, and training knowledge tests), but we also separately analyzed results using job performance ratings

(i.e., occupation-specific technical ratings, provided by supervisors and peers). For Study 2 (service organization sample),

job performancewasmeasured using supervisor ratings.2 We only had access to the 22 occupational interest scale scores, and not the item-level data.3 Peterson et al. (1992; see table 3.77 [p. 175]) provided the fit statistics for four of these 10 hypothesizedmodels that they

tested.4 To ensure that the cross-level interaction effect is not confounded with the between-group interaction effect, Hofmann

and Gavin (1998, p. 631) recommend that the between-group interaction effect be estimated separately from the cross-

level interaction effect. This is done by group-mean centering the Level 1 vocational interest variable as we have done (i.e.,

Xij –Xj), then reintroducing the vocational interest group mean as a Level 2 predictor (i.e., Xj − X̄). Because in our case, the

34 WEE ET AL.

Level 2 vocational interest occupation/groupmean is also our focal group level variable, we substituted (X̄j − X̄)= (Wj − W̄)

into the original equation

Yij = 𝛾00 + 𝛾01(Wj − W̄) + 𝛾02(Wj − W̄) + 𝛾03(Wj − W̄)(Wj − W̄) + 𝛾10(Xij − X̄j) + 𝛾11(Wj − W̄)(Xij − X̄j)

+𝛾20(GMAij − GMA) + u0j + u1j(Xij − X̄j) + rij ,

To obtain

Yij = 𝛾00 + [𝛾01 + 𝛾02](Wj − W̄) + 𝛾03(Wj − W̄)2 + 𝛾10(Xij − X̄j) + 𝛾11(Wj − W̄)(Xij − X̄j) + 𝛾20(GMAij − GMA) + u0j

+ u1j(Xij − X̄j) + rij ,

Which can be simplified to themodel (M2) that was fitted

Yij = 𝛾00 + 𝛾01(Wj − W̄) + 𝛾02(Wj − W̄)2 + 𝛾10(Xij − X̄j) + 𝛾11(Wj − W̄)(Xij − X̄j) + 𝛾20(GMAij − GMA) + u0j

+ u1j(Xij − X̄j) + rij.

5 We also examined the vocational interest congruence hypothesis using polynomial regression (i.e., including quadratic

terms for the person-level interest [P2] and occupation-level interest [E2]) and response surface methodology (Edwards,

2002; Edwards & Parry, 1993). Specifically, we tested a more restrictive person-occupation interest equality effect on job

performance: Job performance will be maximized where individual- and occupation-level vocational interests are equal

(Hypothesis 1b; i.e., negative curvature along the line of mismatch). The results for person-occupation interest equality

predicting job performance are provided in the Supporting Information. First, providing some support for Hypothesis 1b,

there was a significant negative curvature along the line of mismatch for three of the eight vocational interest dimensions:

protective services (curvature = –0.65; p < .05), skilled/technical (curvature = –2.18; p < .05), and administrative (curva-

ture= –0.40; p< .05). That is, job performance was maximized when individual-level interests and occupation-level inter-

ests were equal. Second, the inclusion of the individual- and occupation-level interest quadratic terms did not affect the

statistical significance of any of the cross-level interest interaction (P × E) effects: Hypothesis 1 continues to be supportedin the same six vocational interest dimensions.

6 These are the same measures that made up the composite measure of job performance (i.e., core technical performance)

used in the analyses presented in Table 6.7 As a reviewer has pointed out, although the hands-on tests are measured using a checklist and quantified as the number

of steps that a person has correctly performed on a core performance task, raters probably have some leeway to make

subjective judgments about whether or not a step was correctly performed. Thus, hands-on test scores could be affected

by subjective biases (conscious or unconscious) and are thus not completely immune from potential gender-based rater

discrimination.8 In addition to examining the person–occupation interest interaction predicting a composite measure of job performance

(i.e., core technical performance), we also conducted the same set of analyses separately for each of the following job per-

formance measures: job knowledge tests, hands-on tests, training knowledge tests, and occupation-specific performance

ratings. These results are presented in Appendix D and the Supporting Information, and show that the vocational interest

congruence effect (i.e., cross-level interaction) can be observed for each measure of job performance, for at least some of

the vocational interest dimensions.9 We searched for a second sample that contained multi-occupation, multisource data (i.e., self-rated vocational interests

and job performance ratings) with greater female representation. As a starting point, we reviewed each primary study

included in the most recent meta-analysis of vocational interests and performance (Nye et al., 2017). Out of the 75 stud-

ies we obtained, we identified 24 studies that included multiple occupations (there were 21 studies we could not obtain:

eight were either military reports or based on a military/police sample, and eight were based on single jobs, leaving only

five potentially relevant studies for which we do not have sufficient information). Of these 24multi-occupation studies we

found, seven had sampleswith greater than 50% female representation, and five of these studies also reported on over 200

respondents each. Of these five studies, three were excluded because job performance was measured using self-reported

measures (Dik, 2005; Donohue, 2006; Pseekos, Bullock-Yowell, & Dahlen, 2011), and one study was excluded because job

performancewas operationalized as counterproductivework behavior (Iliescu et al., 2015). Only Kieffer, Schinka, andCur-

tiss (2004) used a multi-occupation sample with non-self-reported job performance and was over 50% female. After con-

tacting the authors, wewere given access to the dataset used for the Kieffer et al. (2004) paper.10 As forwhether the person–occupation interest congruence showed significant unique effects after controlling for person–

occupation gender congruence, these incremental effects for vocational interest congruence survived (a) in the Study 1

military data, for four of six interest dimensions (core technical performance; Table 6), (b) in Study 1 military data, for one

of two interest dimensions (job performance ratings; Appendix D), and (c) in the Study 2 service organization data, for zero

of one interest dimensions (job performance ratings; Table 10). Finally, considering whether person–occupation gender

congruence showed unique effects after controlling person–occupation interest congruence, the incremental effects of

WEE ET AL. 35

gender congruence survived (a) in Study1military data, for sevenof eight interest dimensions (core technical performance;

Table 6), (b) in Study 1 military data, for six of eight interest dimensions (job performance ratings; Appendix D), and (c) in

the Study 2 service organization data, for two of six interest dimensions (job performance ratings; Table 10; and supported

for five of six interest dimensions if a one-tailed test had been used for the replication).11 To obtain Holland’s highpoint dichotomous index requires that the six continuous RIASEC dimensions be rank-ordered

within-person (and within-occupation); our simulation procedure was thus based on generating multivariate—rather than

univariate—vocational interest data.

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SUPPORTING INFORMATION

Additional supporting informationmay be found online in the Supporting Information section at the end of the article.

How to cite this article: Wee S, NewmanDA, SongQC, Schinka JA. Vocational interests, gender, and job

performance: Two person–occupation cross-level interactions. Personnel Psychology. 2020;1–46.

https://doi.org/10.1111/peps.12411

APPENDIX A: COMPARING DIFFERENT OPERATIONALIZATIONS OF VOCATIONAL INTEREST

CONGRUENCE

Previous research on vocational interests has used a variety of techniques to assess whether vocational interest con-

gruence relates to job performance. In order to review past methods used in vocational interest congruence research,

we conducted a survey of the 96 studies referenced in Nye et al.’s (2017) meta-analysis of vocational interest congru-

ence andperformance (performancewasbroadly definedbyNyeet al. to include jobperformance, accident proneness,

turnover, sales, research productivity, self-rated performance, counterproductive work behavior, contextual perfor-

mance, conflict resolution, etc.).Wewere able to locate 75of the primary studies, andTable S1 summarizes the various

methods used to assess congruence in these studies. In summary:

a. 55 papers used correlation/regression/contingency between individual interests and the performance criterion

(note that 16 of these papers examined matched interest scales, of which 13 of these matches were obtained

using empirically keyed job-focused scales—for example, predicting performance in insurance sales using “Insur-

ance Interests,” a scale created to differentiate between high and low performing insurance salespersons),

b. 9 papers used versions of Holland’s (1963, 1973) highpoint congruence index,

c. 5 papers used Brown and Gore’s (1994) C index (which is an update of Kwak and Pulvino’s [1982] K-P index, and is

a generalization of Holland’s highpoint congruence index, to consider weighted congruence among the first three

letters of person and environment codes), and

d. no other congruence index/method was used more than three times. Among these infrequent methods are

individual-level interaction terms between a vocational interest and an individual perception of the environment

(Krebs, Smither, & Hurley, 1991), and self-perceived fit (Hesketh, McLachlan, & Gardner, 1992).

In short, the alternative vocational interest congruencemethods that have been typically used in the past 40 years

fall into two categories: (a) individual-level correlations between an interest dimension and the performance criterion,

which do not assess congruence between an individual variable and an environmental variable (because there is no

environmental variable), and (b) variations onHolland’s highpoint codes (including Brown andGore’s C index, which is

40 WEE ET AL.

empirically fairly redundantwithHolland’s index; r= .80; Brown&Gore, 1994).Our reviewof previous studies showed

no primary research on vocational interest congruence and performance that indexed vocational interest congruence

as a cross-level person–occupation interaction effect (as was done in the current study).

SIMULATIONDEMONSTRATINGHOWCURRENTMETHOD IMPROVESUPONPREVIOUSMETHODS

None of the previous approaches considered vocational interest congruence as a multilevel phenomenon, by opera-

tionalizing vocational interest congruence as the cross-level interaction between an individual-level vocational inter-

est and an occupation-level vocational interest predicting job performance. To demonstrate how our operationaliza-

tion of vocational interest congruence—as a cross-level interaction—improves on previous methods, we conducted a

simulation to compare the predictive validity of our methods against the twomodal alternative approaches that have

been previously used: (a) correlations and (b) congruence indices (e.g., Holland, 1997).

Prior to conducting the simulation, we first identified an appropriate congruence index for comparison against

our proposed multilevel operationalization of vocational interest congruence. Brown and Gore (1994) conducted

an extensive simulation to compare 10 common vocational interest congruence indices that have been modified or

adapted from the original Holland (1963) highpoint dichotomous congruence index, and reported the correlations

among these alternative congruence indices. Noting that the Holland index and its derivatives all strongly intercorre-

late, we conducted a principal components analysis on Brown and Gore’s simulated correlation matrix. These results

are presented in Table S2 and indicate that a single component accounted for 62% of the variance across indices, and

that the Holland (1963) highpoint dichotomous index loaded strongly (loading= .82) on this principal component.We

thus chose Holland’s (1963) classic index as our point of comparison for operationalizing vocational interest congru-

ence. Below, we present the annotated R code for conducting the simulation.11

# Step 1: Generate data from a cross-level P x E interaction for vocational # interest congruence (use Mathieu, Aguinis, Culpepper, & Chen, 2012, # p. 955). # Vocational interest scores at 2 levels of analysis (Level 1: X and Level # 2: GroupX) will be generated as follows: # (a) Generate 6 X variables using meta-analytic corr. matrix among RIASEC # (Mount, Barrick, Scullen, & Rounds, 2005), and # (b) Generate 6 GroupX variables by aggregating X variables to the group # level, using ICC(1) = .15 [similar to ICC(1) estimates in Table 3].

# load required packages library("MASS") library("dplyr") library("lme4")

# set random seed (ensures code reproducibility) set.seed(1)

# set sample size and ICC(1), using large numbers for sample size to reduce the impact of sampling error l1n <- 100 # Level 1 sample size l2n <- 1000 # Level 2 sample size iccx <- 0.15 # see ICC(1) estimates in Table 3

# use corr. matrix from Mount et al. (2005) intcor <- matrix(c(1, 0.45, 0.25, 0.18, 0.20, 0.27, 0.45, 1, 0.36, 0.26, 0.09, 0.17, 0.25, 0.36, 1, 0.39, 0.28, 0.01, 0.18, 0.26, 0.39, 1, 0.51, 0.29, 0.20, 0.09, 0.28, 0.51, 1, 0.53, 0.27, 0.17, 0.01, 0.29, 0.53, 1), ncol = 6) intcov_W <- intcor * (1-iccx) # Level 1 covariance intcov_B <- intcor * iccx # Level 2 covariance

WEE ET AL. 41

# Step 2: Use X and GroupX variables to generate Y variables, in a way that # creates a true cross-level interaction. The model coefficients are # inspired by our own data (see Table 5 coefficients for # structural/machines).

# set values for model coefficients g00 <- 100 # Intercept for B0j equation (Level-1 intercept) g01 <- 0.3 # Direct cross-level linear effect of GroupX on Y g02 <- 0.1 # Direct cross-level quadratic effect of GroupX on Y g10 <- 0.2 # Intercept for B1j equation (Level-1 effect of X on Y) g11 <- 0.1 # Cross-level interaction effect vu0j <- 5 # Random effect for the intercept vu1j <- 0.02 # Random effect for the slope vresid <- 200 # Residual term

# generate Y scores b0 <- g00 + g01*GroupX0 + g02*GroupX0^2 + rnorm(l2n, mean = 0, sd = sqrt(vu0j)) b1 <- g10 + g11*GroupX0 + rnorm(l2n, mean = 0, sd = sqrt(vu1j)) dat <- expand.grid(l1id = 1:l1n, l2id = 1:l2n) dat$X <- X dat$GroupX <- GroupX dat$Y <- b0[dat$l2id] + rowSums(b1[dat$l2id]*(dat$X - dat$GroupX)) + rnorm(l1n*l2n, mean = 0, sd = sqrt(vresid)) dat <- cbind(dat$l1id, dat$l2id, dat$Y, dat$X, dat$GroupX) dat <- as.data.frame(dat) names(dat) <- c("l1id", "l2id", "Y", paste0(rep(c("X.","GroupX."), each = 6), rep(c("R","I","A","S","E","C"), times = 1)))

# Step 3: Calculate Holland dichotomous first letter codes and Holland # (1963) congruence index scores for each individual in each group.

# Identify highpoint interest dimension (i.e., max score) Xi_max <- apply(Xi, 1, which.max) # Level 1 GroupX_max <- apply(GroupX, 1, which.max) # Level 2 Holland_cong <- Xi_max - GroupX_max table(Holland_cong)["0"]/sum(table(Holland_cong)) # Proportion congruent

# Step 4: Calculate correlation between performance criterion variable (Y) and (a) individual-level vocational interest, (b) Holland congruence index, and (c) predicted Y based on the cross-level interaction multilevel model.

# (a) correlation between Y and individual-level vocational interest mod_correlation <- lm(Y ~ X.R + X.I + X.A + X.S + X.E + X.C, data = dat) cor(dat$Y, predict(mod_correlation)) # We obtained r = .051

# (b) correlation between Y and Holland congruence index cor(dat$Y, Holland_cong) # We obtained r = .000

# (c) correlation between Y and predicted Y based on multilevel model mod_mlm <- lmer(Y ~ X.R*GroupX.R + X.I*GroupX.I + X.A*GroupX.A + X.S*GroupX.S + X.E*GroupX.E + X.C*GroupX.C + (1|l2id), data = dat) cor(dat$Y, predict(mod_mlm)) # We obtained r = .197

# Generate Xi, observed scores for each individual: # (a) at Level 1 X <- mvrnorm(n = l1n*l2n, mu = rep(0, ncol(intcov_W)), Sigma = intcov_W) # (b) at Level 2 GroupX0 <- mvrnorm(n = l2n, mu = rep(0, ncol(intcov_B)), Sigma = intcov_B)

# (c) combine L1 & L2 to generate Xi, observed scores for each individual GroupX <- apply(GroupX0, 2, rep, each = l1n) Xi <- GroupX + X # Individual scores

42 WEE ET AL.

Based on the simulation, we obtained (a) r = .05 for the correlation between individual-level vocational interests

and job performance, (b) r = .00 for the correlation between Holland’s (1963) highpoint dichotomous index and job

performance, and (c) r = .20 for the correlation between predicted Y based on the multilevel, cross-level interaction

model and job performance. We note that our results are similar in size to the correlations obtained by Nye, Prassad,

Bradburn, and Elizondo (2018) when Holland’s dichotomous congruence index was used to predict job satisfaction

(r = .01; N = 30,384) and academic course grades (r = .03; N = 1,344), versus when an ordinary least squares (OLS)

regressionmodel containing individual-level interaction termswasused topredict job satisfaction (r= .23;N=30,384)

and academic course grades (r= .20;N= 1,344).

In short, the current simulation shows how—when a true cross-level vocational congruence effect exists—then

operationalizing vocational interest congruence using amultilevelmodel featuring a cross-level interaction resulted in

larger correlations with observed job performance scores, compared to operationalizing vocational interest congru-

ence using a congruence index such as Holland’s highpoint dichotomous index, or individual-level correlations alone.

This result is not surprising: that is, that prediction is betterwhen the terms in the estimationmodelmirror the terms in

the population model. Our point is merely that, when the population model contains person–occupation congruence,

the cross-level interactionmodel is best for detecting it.

APPENDIX B: DIFFERENT MEASURES OF JOB PERFORMANCE

Job knowledge tests

Job knowledge tests varied by occupation, and measured the knowledge required to perform 10–19 representative

occupation-specific tasks for each occupation (see Campbell, 1988; Campbell, Ford, et al., 1990; Campbell, McHenry,

et al., 1990 for details). For example, specific tasks for tank crewmen included operating tanks and tank gunnery; and

specific tasks for medical specialists included clinic/ward treatment and care, and clinic/wardmanagement. The num-

ber of job knowledge test items varied by occupation (range: 40–127 items), with a mean internal consistency relia-

bility (averaged across occupations) of .80 (range: .73 –.86; calculated as composite reliabilities and based upon val-

ues for subtest/component reliabilities and intercorrelations provided in Campbell, 1988 and Appendix C). The job

knowledge test score reflects the unweighted mean percentage of items answered correctly, averaged across sub-

tests/components (e.g., for tank crewmen, the job knowledge test score was computed as the average of the percent-

age of items answered correctly for the operating tank task and the percentage of items answered correctly for the

tank gunnery task).

Hands-on tests

Hands-on tests varied by occupation and measured the knowledge and procedural skills required to perform repre-

sentative occupation-specific tasks for each occupation (Campbell, Ford, et al., 1990; Campbell,McHenry, et al., 1990).

Tasks were a subset of those measured in the job knowledge tests. Hands-on test items were checklists of the steps

required to perform a task (scored as correctly performed or incorrectly performed). The number of hands-on test

items varied by occupation (range: 52–215 items), with a mean internal consistency reliability of .85 (range: .78–.97;

calculated as composite reliabilities, fromvalues provided inCampbell, 1988andAppendixC). Thehands-on test score

reflects themean percentage of steps correctly performed, averaged across tasks.

Training knowledge tests

Training knowledge tests varied by occupation, and measured knowledge required to perform occupation-specific

tasks for each occupation (Campbell, Ford, et al., 1990; Campbell, McHenry, et al., 1990). Reliabilities were not

reported for the training knowledge tests (in either the technical reports or the peer-reviewed published reports),

but the factor loading of the training knowledge composite onto the core technical performance latent factor (i.e., a

factor that also includes job knowledge tests and hands-on tests) was .64 (median= .67; range: .42–.85). The training

knowledge test composite score reflects themean percentage of items answered correctly.

WEE ET AL. 43

Performance ratings

A fourth measure of job performance entailed occupation-specific ratings obtained via behaviorally anchored rating

scales (Campbell, 1988; Campbell, Ford, et al., 1990; Campbell, McHenry, et al., 1990). Rating scales were developed

tomeasure six to 13 occupation-specific dimensions per occupation. As an example, for light wheel vehiclemechanics,

occupation-specific dimensions included “Troubleshooting,” “Repair,” and “Perform routine maintenance.” These rat-

ings were developed based on the critical incident method (Flanagan, 1954; Smith & Kendall, 1963). The rating scale

included: (a) the name of the performance dimension and item, (b) a 7-point rating scale ranging from 1 = Low effec-

tiveness to 7=High effectiveness, and (c) three sets of dimension-specific behavioral exemplars that correspond to low

(1–2), moderate (3–5), and high (6–7) levels of effectiveness on that dimension. For light wheel vehicle mechanics,

an example item measuring performance on the “Repair” dimension was, “How effective is each soldier in correcting

malfunctions to make vehicles operational?” The following behavioral exemplars corresponded to low (“fails to install

all parts of a system after they have been removed; or installs repair parts incorrectly and without making appropri-

ate adjustment”), moderate (“installs and adjusts repair parts correctly and verifies that all parts have been replaced

after removal”), and high (“installs and adjusts repair parts correctly and in an efficient manner; and carefully checks

that all parts have been replaced properly by testing/operating the vehicles”) levels of effectiveness on the “Repair”

performance dimension (Knapp, Campbell, Borman, Pulakos, & Hanson, 2001, p. 195).

On average, each soldier was rated by 1.90 supervisors and 3.26 peers (Campbell, 1988, p. 97). Across occupa-

tions, mean interrater reliability was ICC = .54 for supervisors (range: .44–.65) and ICC = .42 for peers (range: .30–

.57) (Campbell, 1988, p. 102). Raters were told to provide ratings only if they had worked with the soldier for at least

2 months and/or if they had enough familiarity with the soldier’s job performance. All raters received rater training

that was, “developed to reduce various types of rating errors and to persuade raters to try hard to provide accurate

evaluations” (Campbell, 1988, p. 98). For each soldier, the rating on each performance dimensionwas computed as the

unweightedmean of themean peer rating and themean supervisor rating (Knapp et al., 2001, p. 216).We do not have

access to separate supervisor ratings and peer ratings; we only have the composite scores across rating sources.

In the present study, the performance rating scorewas computed based on only the performance dimensions asso-

ciated with core occupation-specific tasks (rather than less core occupation-specific tasks). As an example, for a light

wheel vehiclemechanic, core tasks included “Troubleshooting,” “Repair,” and “Perform routinemaintenance,”whereas

less core tasks included “Safety mindedness,” “Administrative duties,” and “Use technical documents” (Campbell,

1988, p. 119). Thus, the performance rating score represents the sum of all ratings on occupation-specific core task

dimensions.

44 WEE ET AL.

APPENDIX

C:MULT

ILEVELMODELIN

GRESULT

SFORCROSS-LEVELPERSON–OCCUPATIO

NGENDERCONGRUENCE(M

2a)FOREACH

PERFORMANCEMEASURE

TABLEC1

Multilevelmodelingre

sultsfo

rcro

ss-levelperson–occupationgendercongru

encefo

reachperform

ancemeasu

re

Perform

ance

measure

Fixed

effects

Randomeffects

Intercep

tAbility

Sex

Prop.

Sex×Prop.

Intercep

tSlope

Covariance

Residual

Jobkn

owledge

tests

54.001

1.846

−6.683

−8.446

33.710

7.241

32.753

−233.607

305.315

(1.438)

(0.075)

(3.506)

(5.970)

(12.791)

Han

ds-ontests

75.249

1.327

−11.363

−13.593

48.731

2.466

22.855

−48.470

914.788

(1.106)

(0.127)

(4.307)

(5.113)

(13.571)

Trainingkn

owledge

tests

56.338

2.267

−12.860

−7.497

46.890

11.696

100.944

1154.667

364.795

(1.804)

(0.082)

(5.667)

(7.436)

(21.570)

Perform

ance

ratings

24.459

0.203

−3.956

−0.818

10.046

0.057

2.677

−0.137

37.551

(0.173)

(0.023)

(1.205)

(0.898)

(4.061)

Note.Statistically

sign

ifican

tcross-levelSex×Prop.interactionterm

sareunderlin

ed.Sex

was

dummycoded

(male=0;fem

ale=1).Prop.=

Proportionofwomen

inan

occupation(i.e.,

Gen

der

composition),Sex×Prop=Personsex×Occupationgender

compositioncross-levelinteraction(i.e.,gender

congruen

ce).Stan

darderrors

arepresentedinparen

theses.B

olded

values

arestatistically

sign

ifican

tat

p<.05.

WEE ET AL. 45

APPENDIX

D:MULT

ILEVELMODELIN

GRESULT

SFORCROSS-LEVELPERSON–OCCUPATIO

NIN

TERESTCONGRUENCEFORJO

B

PERFORMANCERATIN

GS

TABLED1

Multilevelmodelingre

sultsfo

rcro

ss-levelperson–occupationinte

rest

congru

ence(controllingfo

rperson-o

ccupationgender

congru

ence)pre

dictingperform

ancera

tings

Fixed

effects

Randomeffects

Variable

Intercep

tAbility

Sex

Prop.

PE

E2Sex×E

Prop.×

PSex×Prop.

P×E

Intercep

tSlope

Cov.

Residual

Structural/

Machines

M1

24.173

0.206

0.068

0.029

0.015

0.029

0.035

0.000

0.000

37.509

(0.170)

(0.023)

(0.018)

(0.041)

(0.013)

(0.006)

M2

24.189

0.204

−2.051

2.496

0.037

0.173

−0.020

−0.448

0.129

1.114

0.029

0.000

0.000

0.000

37.327

(0.176)

(0.023)

(0.725)

(2.201)

(0.027)

(0.095)

(0.025)

(0.219)

(0.156)

(3.302)

(0.010)

Rugged

outdoors

M1

24.269

0.184

0.085

−0.010

0.007

0.020

0.016

0.000

0.000

37.552

(0.161)

(0.023)

(0.015)

(0.063)

(0.017)

(0.006)

M2

24.580

0.186

−0.477

−1.650

0.058

−0.071

−0.006

−1.335

0.147

−10.233

0.025

0.011

0.000

0.000

37.321

(0.291)

(0.024)

(1.424)

(2.713)

(0.036)

(0.145)

(0.027)

(0.683)

(0.245)

(9.420)

(0.017)

Protective

services

M1

24.412

0.210

0.062

−0.045

−0.032

0.020

0.002

0.002

0.000

37.920

(0.153)

(0.024)

(0.031)

(0.087)

(0.036)

(0.019)

M2

24.526

0.211

−3.672

−2.253

0.061

−0.237

0.037

0.359

−0.033

10.569

0.014

0.001

0.003

0.000

37.626

(0.154)

(0.024)

(0.676)

(1.640)

(0.044)

(0.218)

(0.078)

(0.251)

(0.220)

(2.202)

(0.026)

Skilled

/

Technical

M1

24.374

0.204

0.025

0.201

−0.092

0.074

0.000

0.000

0.000

37.968

(0.142)

(0.024)

(0.033)

(0.134)

(0.146)

(0.042)

M2

24.435

0.205

−3.277

−0.803

0.033

0.162

0.024

0.126

−0.056

8.246

0.071

0.004

1.000

0.000

37.705

(0.187)

(0.024)

(0.651)

(0.794)

(0.041)

(0.143)

(0.159)

(0.499)

(0.192)

(1.722)

(0.042)

(Continues)

46 WEE ET AL.

TABLED1

(Continued

)

Fixed

effects

Randomeffects

Variable

Intercep

tAbility

Sex

Prop.

PE

E2Sex×E

Prop.×

PSex×Prop.

P×E

Intercep

tSlope

Cov.

Residual

Audiovisualarts

M1

24.428

0.202

−0.040

0.115

−0.258

0.086

0.008

0.000

0.000

37.964

(0.183)

(0.023)

(0.031)

(0.186)

(0.333)

(0.048)

M2

24.363

0.200

−3.316

−1.072

−0.019

0.156

0.272

0.875

−0.116

7.898

0.075

0.001

0.000

0.000

37.671

(0.193)

(0.023)

(0.671)

(0.803)

(0.039)

(0.201)

(0.351)

(0.560)

(0.188)

(1.914)

(0.049)

Interpersonal

M1

24.371

0.203

0.022

0.025

−0.007

0.009

0.041

0.001

0.000

37.921

(0.159)

(0.023)

(0.018)

(0.063)

(0.013)

(0.007)

M2

24.421

0.203

−3.633

−0.546

0.051

−0.042

0.001

0.339

−0.165

7.405

0.013

0.060

0.001

0.000

37.515

(0.217)

(0.023)

(0.650)

(1.085)

(0.023)

(0.078)

(0.014)

(0.116)

(0.112)

(1.857)

(0.008)

Administrative

M1

24.361

0.205

−0.023

0.125

−0.013

0.011

0.000

0.000

0.000

37.962

(0.151)

(0.024)

(0.020)

(0.061)

(0.030)

(0.011)

M2

24.601

0.204

−4.389

−1.052

−0.020

0.106

−0.031

−0.479

0.028

13.138

0.006

0.000

0.001

0.000

37.688

(0.162)

(0.024)

(1.038)

(0.975)

(0.030)

(0.073)

(0.037)

(0.357)

(0.152)

(4.015)

(0.015)

Foodservices

M1

24.281

0.204

−0.070

0.390

0.099

0.053

0.000

0.008

0.000

37.833

(0.172)

(0.024)

(0.043)

(0.204)

(0.418)

(0.077)

M2

24.629

0.202

−5.230

−1.344

−0.048

0.331

−0.360

−2.662

−0.149

16.223

0.053

0.015

0.013

0.000

37.530

(0.207)

(0.024)

(1.369)

(0.848)

(0.065)

(0.243)

(0.459)

(1.753)

(0.306)

(5.156)

(0.100)

Note.Statistically

sign

ifican

tcross-levelP×Einteractionterm

sareunderlin

ed.M

1=Vocationalinterestcongruen

cemodel,M

2=Vocationalinterestcongruen

ceplusgender

congruen

ce

model.M

2a=Gen

der

congruen

cemodel(person-occupationgender

interaction).Sexwas

dummycoded

(male=0;fem

ale=1).Prop.=

Sexproportioninan

occupation,P=Person-level

interest,E

=Environmen

t/Occupation-levelinterest,P

×E=Person×Environmen

t/Occupationinterestcross-levelinteraction(i.e.,vocationalinterestcongruen

ce).Cov.=

Covariance.

Stan

darderrorsarepresentedinparen

theses.Bolded

values

arestatistically

sign

ifican

tat

p<.05.