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Psychological Bulletin
2009, Vol. 135, No. 6, 859 – 884© 2009 American Psychological Association
0033-2909/09/$12.00 DOI: 10.1037/a0017364

Men and Things, Women and People:
A Meta-Analysis of Sex Differences in Interests
Rong Su and James Rounds

Patrick Ian Armstrong

University of Illinois at Urbana–Champaign

Iowa State University

The magnitude and variability of sex differences in vocational interests were examined in the present
meta-analysis for Holland’s (1959, 1997) categories (Realistic, Investigative, Artistic, Social, Enterprising, and Conventional), Prediger’s (1982) Things–People and Data–Ideas dimensions, and the STEM
(science, technology, engineering, and mathematics) interest areas. Technical manuals for 47 interest
inventories were used, yielding 503,188 respondents. Results showed that men prefer working withthings and women prefer working with people, producing a large effect size (d ⫽ 0.93) on theThings–People dimension. Men showed stronger Realistic (d ⫽ 0.84) and Investigative (d ⫽ 0.26)
interests, and women showed stronger Artistic (d ⫽ ⫺0.35), Social (d ⫽ ⫺0.68), and Conventional (d ⫽⫺0.33) interests. Sex differences favoring men were also found for more specific measures of engineering (d ⫽ 1.11), science (d ⫽ 0.36), and mathematics (d ⫽ 0.34) interests. Average effect sizes varied
across interest inventories, ranging from 0.08 to 0.79. The quality of interest inventories, based on
professional reputation, was not differentially related to the magnitude of sex differences. Moderators of
the effect sizes included interest inventory item development strategy, scoring method, theoreticalframework, and sample variables of age and cohort. Application of some item development strategies cansubstantially reduce sex differences. The present study suggests that interests may play a critical role ingendered occupational choices and gender disparity in the STEM fields.
Keywords: vocational interests, RIASEC interests, sex differences, gender disparity in STEM, constructvalidity

ceiving master’s degrees in the social sciences, biological sciences,and psychology are female as are the majority of those with
doctoral degrees in psychology (NSF, 2007). Nevertheless, womenremain a minority in the fields of engineering and the physicalsciences. This raises the question: Why do large sex differences
continue to exist in these areas? Why are more women not represented in science and engineering fields? Debate over these STEM
sex differences has been frequently highlighted in the media andhas led to public debate (e.g., Dillon & Rimer, 2005; Pinker, 2008;
Tierney, 2008).
Among the reasons put forward as potential contributors togender disparity in the STEM fields, interest is often identified asa critical factor that may lead to the low number of womenentering these fields. Interest is a central predictor of educational
choices (e.g., Benbow & Minor, 1986; Hansen & Sackett, 1993;
Lapan, Shaughnessy, & Boggs, 1996), degree completion (e.g.,Webb, Lubinski, & Benbow, 2002), occupational choices bothwithin and outside of the STEM areas (e.g., Fouad, 1999; Parsons,Adler, & Meece, 1984; Strong, 1943), and job satisfaction (e.g.,
Barge & Hough, 1988; Morris, 2003). Lack of interest in the
STEM fields or finding other fields to be more interesting is also
the top reason given for women switching out of the STEM majors
and jobs (Preston, 2004; Seymour & Hewitt, 1997). For decades,
results obtained from developing revisions of the Strong Interest
Inventory (Campbell, 1974; Hansen & Campbell, 1985; Harmon,
Hansen, Borgen, & Hammer, 1994; Donnay, Morris, Schaubhut, &Thompson, 2005), as well as other interest inventories, have documented sex differences in vocational interests. These sex differences in interests have not lessened appreciably since the 1930s

Since the 1970s, researchers have been debating why women are
underrepresented in the STEM (science, technology, engineering,and mathematics) fields (e.g., Ceci & Williams, 2007; Ceci, Williams, & Barnett, 2009; Gallagher & Kaufman, 2005; Watt &Eccles, 2008). The number of women completing a degree andbeing employed in mathematics is less than half the number ofmen. This female–male ratio is approximately two fifths for physical sciences, one fourth for computer sciences, and barely one
fifth for engineering (National Science Foundation [NSF], 2007).
However, the number of women obtaining graduate degrees has
significantly increased across a wide range of scientific disciplines
over the past four decades. In the 1960s, all scientific disciplines
were male dominated, but the majority of students currently re-Rong Su, Department of Psychology, University of Illinois at UrbanaChampaign; James Rounds, Departments of Psychology and Educational
Psychology, University of Illinois at Urbana–Champaign; Patrick Ian Armstrong, Department of Psychology, Iowa State University.
This study was supported by the Robert P. Larsen Grant for Research in
Career Development to Rong Su from the Career Center of the University
of Illinois at Urbana-Champaign. We thank the Buros Institute of Mental
Measurements for their generous assistance in providing data sources. We
also thank David Lubinski, Lawrence Hubert, Christopher Moyer, andLizanne DeStefano for their comments on the manuscript and Jing Guo forindependently calculating a subset of the effect sizes.
Correspondence concerning this article should be addressed to James
Rounds, Department of Psychology, University of Illinois at UrbanaChampaign, 233 Psychology Building, 603 E. Daniel Street, Champaign,
IL 61820. E-mail: jrounds@illinois.edu
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SU, ROUNDS, AND ARMSTRONG

860(Hansen & Campbell, 1985; see also Donnay et al., 2005; Einarsdóttir & Rounds, 2009; Fouad, 1999). Despite the importance ofinterests in educational– occupational choices and the widespread
belief that substantial sex differences exist, there has been nocomprehensive review quantifying the magnitude and examining
the nature of sex differences in interests (Hyde, 2005; Hyde &Linn, 2006). The present study is a meta-analysis of sex differences in vocational interests.1

Interest and Career Development
Interest can be conceptualized from either a situational perspective or a dispositional perspective (Silvia, 2006). Situational interest is defined as the context-specific state of emotional experience,curiosity, and momentary motivation (see Hidi, 1990; Schraw &Lehman, 2001). In comparison, dispositional interest is trait-like,reflecting a person’s preferences for behaviors, situations, contexts
in which activities occur, and/or the outcomes associated with thepreferred activities (Rounds, 1995). From the dispositional perspective, interests are associated with attention, positive emotional
states, and direction toward or away from an object, as well as
actual function of behavior (Savickas, 1999). The situational anddispositional perspectives are most closely identified with thedisciplines of educational and vocational psychology, respectively.
The two perspectives on interests can be traced to the early 20th
century (Arnold, 1910; Parson, 1909) and parallel the emergenceof educational and vocational psychology as separate disciplines ofscientific inquiry (Cronbach, 1956). The educational psychology
tradition of interest research focuses on the relationship between
interest and attention/motivation, primarily examining the functionof interest in learning and academic achievement in classroom
settings with children (Krapp, 1999; Renninger, Hidi, & Krapp,
1992). In comparison, the vocational psychology tradition of interest research focuses on the construction, validation, and interpretation of interest-based measures to address career development
issues with adolescents, college students, and adults (Low &Rounds, 2006; Walsh & Osipow, 1986).
The present study examines interests from the dispositional
perspective, because it is dispositional measures of interest that are
frequently used in applied settings when working with individuals
who are making career-related decisions. Holland (1966) has defined vocational interests as “the expression of personality inwork, hobbies, recreational activities, and preferences” (p. 3). An
individual’s interests are fundamental to the process of careerdevelopment because individuals tend to seek environments inwhich they can express their interests (Holland, 1997). Anotherview of interests, put forward by Hogan (Hogan & Blake, 1999),
is that vocational interests are, rather than outgrowths of personality development, a direct reflection of an individual’s identity(i.e., the way we think of ourselves) that is best conceptualized interms of a person’s motives, goals, values, and aspirations (Hogan& Roberts, 2000). Hogan proposed that, compared with personality measures, which tend to reflect an individual’s reputation (i.e.,
the way others see us), interest measures are a more direct expression of an individual’s identity. Alternatively, Eccles-Parsons(1983; see also Meece, Parsons, Kaczala, Goff, & Futterman,
1982) proposed the concepts of attainment value and intrinsic/
interest value, which are closely related to the concept of interest,
as important determinants of individuals’ achievement motivationand career choices. Eccles-Parsons (1983) suggested that thesesubjective values that individuals attach to different achievementtasks are a stable part of identity and are influenced by thesocialization process. Despite the conceptual and terminologicaldifferences of these theories, interests are integral to one’s identityand are an expression of an individual’s attempts to adjust to theacademic and work environment by finding opportunities thatmatch their identity.
The development of “trait complexes” (Ackerman, 2003; Armstrong, Day, McVay, & Rounds, 2008) can be an approach toidentity formulation. Ackerman and Heggestad (1997) suggested
that there are substantial commonalities among families of nonability traits (e.g., personality, interests, motivation, self-concept)
as well as between ability traits and nonability traits, making it
possible to form meaningful clusters of trait complexes that may
enhance our understanding of the nature of individual differencesin each of these traditionally separated domains. Armstrong et al.
(2008) used interests as a structural framework for identifying trait
complexes and interpreted the emergence of trait complexes as aprocess of contextual convergence, that is, individuals tend to seekout environments that allow for their various individual differences
to function effectively and complement each other. Over the life
span, individuals form interests that are consistent with their existing identity, including personality (e.g., Darley, 1941; Holland,1966), self-perception of abilities (e.g., Danissen, Zarret, & Eccles,
2007; Lent, Brown, & Hackett, 1994; Thorndike, 1915), andgender role (e.g., Eccles-Parsons, 1983; L. S. Gottfredson, 1981;
Tyler, 1955). As an inseparable part of self-identity, interests serveas the impetus for individuals to navigate through and function
effectively in their environments.

Sex Differences in Interest
The issue of how sex differences in interest influence outcomesis central to both the educational and the vocational traditions ofinterest research, despite their different emphases on the contextualand dispositional aspects of interest. In educational psychology,
researchers are concerned about how these sex differences ininterests develop and how they impact individuals’—particularly
girls’ and women’s— educational choices, career decisions, andachievement. For example, Marsh and Yeung (1998) studied thebidirectional relationship of interest and achievement and used aninternal– external framework of reference model to explain whyboys develop stronger mathematics self-concepts and girls develop
stronger English self-concepts (see also Marsh, 1986). Eccles andher colleagues (Eccles, 1994; Parsons et al., 1984) applied an
expectancy–value model to understand the different patterns ofmen’s and women’s educational and occupational choices andconsidered interest (part of the subjective task value) an importantreason why women do not select the same occupational fields—
particularly physical sciences, engineering, and applied mathematics—as men. Other studies (e.g., Betz & Schifano, 2000; Cook et
1

The phrase “sex differences” is used throughout this article because wecompared the vocational interests of men and women on the basis of theirself-reported biological status. In several cases, we used the term “gender”
to refer to the psychosocial characteristics of males and females, such as
gender role identity, and when the accepted usage is gender, for example,
gender disparity in STEM fields.

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

al., 1996; Jacobs, Davis-Kean, Bleeker, Eccles, & Malanchuk,2005) shed light on the influence of socialization process (e.g., rolemodeling, parents’ expectations, educational experiences) on theinterest levels, as well as the changing trajectories of interests, ofboys and girls.
In the vocational psychology field, researchers have widely
acknowledged that sex differences of interests appear at both theitem level and the scale level (e.g., Betz & Fitzgerald, 1987;
Fouad, 1999; Hackett & Lonborg, 1993; Johansson, 2003). Generally, women are more likely than men to indicate interest insocial and artistic activities, whereas men are more likely than
women to indicate interest in scientific, technical, and mechanicalactivities (Betz & Fitzgerald, 1987). As early as one century ago,
Thorndike (1911) wrote that the greatest difference between men
and women is “in the relative strength of the interest in things andtheir mechanisms (stronger in men) and the interest in persons andtheir feelings (stronger in women)” (p. 32). On the basis of areview of studies on masculinity and femininity, Lippa (2001)
proposed that masculinity–femininity as a bipolar trait overlaps
substantially with the People–Things dimension of vocational interests. Lippa (1998) computed the effect sizes (female minusmale) from his multistudy article on the People versus Things
dimension and found effect sizes (Cohen’s d) larger than 1.20 for
all three studies. Lubinski (2000) cited this effect size as the largest
sex difference in the field of individual differences. Furthermore,these sex differences do not seem to vary much across age (Holland, Fritzsche, & Powell, 1994; F. Kuder & Zytowski, 1988) orover decades (Fouad, 1999; Hansen, 1988). However, the literature
has yet to include a systematic meta-analysis examining the patternand size of sex differences in vocational interests.
Much of the debate on sex differences in vocational interest
assessment concerns how to interpret differences in interest scores
between men and women. In particular, these sex differences may
reflect the sex restrictiveness of interest inventories; if this is thecase, the question then becomes how to revise interest inventory
scales to be more sex-balanced. In the early 1970s, Lois-Ellin
Datta from the National Institute of Education brought together
staff from that institution, a senior consultant (Esther E. Diamond),
and researchers to study sex bias and sex fairness in career interest
inventories. The National Institute of Education planning group
was formed to determine what constitutes sex fairness or sex bias
in interest inventories. From this examination, the National Institute of Education planning group produced Guidelines for Assessment for Sex Bias and Sex Fairness in Career Interest Inventories(National Institute of Education, 1974) and two edited books(Diamond, 1975; Tittle & Zytowski, 1978) that had considerable
impact on the development and interpretation of interest inventories.
The Guidelines (National Institute of Education, 1974) are reflected in the current Standards for Educational and Psychological
Testing (American Educational Research Association, American
Psychological Association, & National Council on Measurementin Education, 1999) and form the basis for interest inventory
construction and revision. Probably the most controversial recommendation in the Guidelines was the proposal that interest inventories contain sex-balanced interest scales within the limitations
imposed by validity considerations. This guideline in particular led
to two sharply divided positions: American College Testing Program (ACT) researchers argued for sex-balanced inventories, and861

John Holland and colleagues argued that sex-balanced scales
would reduce the validity of vocational interest inventories. Bothsides of the debate had developed their respective interest inventories: the ACT Interest Inventory (American College Testing
Program, 1974) and the Self-Directed Search (Holland, 1972).
Underlying the debate on sex-balanced inventories were twodifferent approaches to examining the validity of an interest inventory—an “opportunity approach to validation” and a “socialization approach to validation” (Cole & Hanson, 1975; see also
G. D. Gottfredson & Holland, 1978; Holland, 1976; Lamb &Prediger, 1979; Prediger & Cole, 1975; Prediger & Hanson, 1976).
Prediger and Cole (1975) argued that the primary purpose of using
an interest inventory is occupational exploration. These authors
advocated for removing sex differences from interest scores tomaximize career opportunities for individuals. In contrast, G. D.
Gottfredson and Holland (1978) stated that constructs measured byinterest inventories are dependent on differential socialization experiences of men and women. These researchers argued against theremoval of sex differences in the belief that doing so would
decrease the predictive validity of the measure. These contrasting
positions regarding how to interpret the sex differences foreshadowed the wider debate in psychology over whether test consequences fall within the realm of validity (e.g., Linn, 1997; Messick, 1989; Popham, 1997; Shepard, 1997).
The opportunity approach to validation and the Guidelines led to
strategies for reducing sex differences in the scaling and scoring ofmany interest inventories, including eliminating or revising itemswith largely discrepant endorsement rates for men and women andusing combined-sex norm scores instead of raw scores. New
measures, such as the Vocational Interest Inventory (Lunneborg,1981) and the Unisex Edition of the ACT Interest Inventory(UNIACT; American College Testing Program, 1981), were developed during this period of debate using the techniques proposed
for reducing sex differences. It is also during the course of thisdebate that validity of the sex-balanced inventories became a focusof study: Does item selection based on sex differences affectvocational interest scale validity?
A common validity procedure is to use a classification system togroup people on the basis of their interest scores and a criterion(e.g., vocational choice, college major, occupation) and then to
calculate the percentage of agreement. Vocational researchers haveconducted validity studies (e.g., G. D. Gottfredson & Holland,
1975; Hanson, Noeth, & Prediger, 1977), leading to a variety ofconclusions depending on how percentage agreement between
interest score and criterion was assessed. For example, G. D.
Gottfredson and Holland (1978) showed that overall hit rate acrossall interest categories compared with unweighted average hit rates
across categories can suggest divergent interpretations, with overall hit rate favoring the position of Holland and colleagues andunweighted average hit rates favoring the position of the American
College Testing Program. Because these studies focused on the
Self-Directed Search and early forms of the UNIACT and rarely
involved other interest inventories, the results had limited generalizability. The answer to a seemingly straightforward validity
issue became overly complicated. The controversy eventually disappeared, but the issues of how to develop and score interest
inventories remain. In the present study, we examined the effectiveness of the “sex-balanced” techniques and document the extent

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862

SU, ROUNDS, AND ARMSTRONG

to which they minimize sex differences in interest inventory
scores.

Types of Interests and Interest Measurement
Interest measures are typically classified into three levels ofgenerality (Hansen, 1984; Rounds, 1995): occupational interest
scales, basic interest scales, and general interest scales. Occupational scales are empirically keyed scales measuring similarity ofinterest between an individual and incumbents employed in different occupations. Basic interest scales measure interests at thelevel of generality between interest in a certain occupation andinterest in a broad area. These scales characterize shared propertiesof activities (e.g., teaching, selling, and writing) and are often
implied in the object of interest (e.g., mathematics, physical science, and engineering). General interest scales measure broadinterest areas that encompass a heterogeneous group of occupations and activities. Items in general interest scales share somecontent and are often grouped on the basis of theoretical models,such as Holland’s (1959, 1997) interest types. In the current study,
we examined sex differences in responses on general interest(scales) and basic interest scales, because these scales are usedmost frequently when developing vocational interest measures andprovide the best understanding of the nature of sex differences.
The interest model developed by Holland (1959, 1997) is themost widely adopted theoretical framework for interest measurement. Holland organized vocational interests into six types, forming a circular structure referred to collectively as RIASEC: Realistic interest in working with things and gadgets or working
outdoors; Investigative interest in science, including mathematics,
physical and social sciences, and biological and medical sciences;
Artistic interest in creative expression, including writing and thevisual and performing arts; Social interest in helping people;
Enterprising interest in working in leadership or persuasive roles
directed toward achieving economic objectives; and Conventional
interest in working in well-structured environments, especiallybusiness settings. Prediger (1982) proposed two bipolar work task
dimensions underlying Holland’s hexagon: working with things
versus people and working with data versus ideas.
Holland’s (1959, 1997) interest model has received robust empirical support (Armstrong, Hubert, & Rounds, 2003; Day &Rounds, 1998; Tracey & Rounds, 1993). Interest inventories usedmost frequently to assist individuals in making educational andcareer-related plans, such as the Strong Interest Inventory (Donnay
et al., 2005) and the UNIACT Interest Inventory (American College Testing Program, 1995) have either explicit scales to assess
RIASEC types or methods to convert interest scale scores toHolland’s system. The U.S. Department of Labor O*NET occupation classification (Rounds, Smith, Hubert, Lewis, & Rivkin,1999) is also linked to the RIASEC constructs. Given its wide use
and far-reaching influence on interest assessment, we use the
RIASEC model in the present study to examine sex differences byinterest type. As previously noted, the greatest differences between
men and women are in the relative strength of the interest in
working with things versus the interest in working with people(Lippa, 1998; Thorndike, 1911). Thus, we also examined sex
differences in Prediger’s (1982) Things–People and Data–Ideas
dimensions.

The Present Study
The purpose of the present study was to examine the size of sex
differences in RIASEC interests and work task dimensions using ameta-analytic review. We used technical manuals of vocational
interest inventories as our data source because such manuals
generally include results from large samples that are representativeof different ages and ethnic groups, thus controlling sampling
error. Hedges and Nowell (1995; see also Hyde, 2005) recommended the use of large well-sampled data sets as an alternative to
traditional meta-analysis. Additionally, the data obtained fromthese technical manuals represent the empirical foundation of theinterest measures that are used most frequently in applied settings
when working with individuals who are making career-related
decisions. Therefore, in addition to providing insight into thenature and magnitude of sex differences in interests, the results
obtained from this meta-analysis have important practical implications for career guidance and interest inventory development.
We expected sex differences in most of the RIASEC domains,
especially in the Realistic and Social interests that anchor theThings–People dimension. Specifically, we expected to find agreater tendency for men to prefer working with things and women
to prefer working with people and for men to have more interest in
the Realistic category and women to have more interest in theSocial and Artistic categories. Because our intention in undertaking the present study was to inform the ongoing debate about
women’s underrepresentation in the STEM fields, we also investigated basic interest scales related to the STEM fields. We expected to find men to be more interested in engineering, science,and mathematics (mathematics referring restrictively here to themathematician profession that is linked to the Investigative interesttype). We also examined the homogeneity of effect sizes acrossinterest measures and samples. When the results were heterogeneous, we evaluated potential moderators that could account for
the variability in sex differences.

Potential Moderators
Moderators were classified into two groups. The first set ofmoderators involved the interest inventory per se, including (a)item development to remove sex differences; (b) scoring (rawscores vs. combined-sex norm scores); and (c) theoretical framework of the interest inventories (Holland’s RIASEC vs. otherframeworks). Constructing interest inventories using only those
items with little or no response disparity between men and womenis one strategy that has been recommended for reducing sex
differences in inventory scores (e.g., Lamb & Prediger, 1979). For
example, the items of the UNIACT (American College Testing
Program, 1981) were selected so that male and female scoreoverlap for every scale was larger than 85%; the items of the
Vocational Interest Inventory (Lunneborg, 1981) were thoroughly
inspected and revised until the correlation between every item andparticipants’ sex was within the range of ⫾.15; and the items of the
Career Occupational Preference System Interest Inventory wereselected in its Revised Edition (R. R. Knapp & Knapp, 1979) only
if response differences for male and female respondents were
smaller than 15%. Selecting items that minimize sex differences
potentially reduces the differences on interest scales. The present
analysis focused on the effects of item selection on sex differences

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

across the RIASEC scales and inventories. We expected the majority of the heterogeneity in effect sizes across interest inventories
to be explained by the kinds of items in interest inventories.
The use of raw scores in interest inventories has been criticizedfor producing dramatically different score distributions for maleand female respondents and leading to divergent, sex-stereotypicoccupational guidance (e.g., Prediger & Cole, 1975). Little research has been conducted, however, on the degree to which norm
scores can reduce sex differences. We examined the extent towhich raw scores, as compared with combined-sex norm scores,
influenced the size of sex differences in interest inventories. Interest framework refers to whether an interest inventory uses the
RIASEC model (Holland, 1959, 1997) or other interest models,such as G. F. Kuder’s (1977) 10 preference areas, Roe’s (1956)
eight interest groups, or basic interest scales. Because of differences in the constructs and the specificity of interest level measured by the scales, theoretical framework may have had an impact
on the size of sex differences in interest inventory scores.
The second set of moderators focused on sample characteristics,
including (a) age of the sample and (b) cohort. Low (2009) showedthat the mean level of men’s and women’s interests change before
mid-adulthood and follow different trajectories. Different trajectories of mean-level interest change for men and women may leadto observable age effects on sex differences. Cohort effects may
come about because the work tasks and environments havechanged dramatically since the late 1960s (Betz & Fitzgerald,
1987; Savickas, 2002). Changing work environment has led toshifts in the cultural values shared by different cohorts as well as
shifts in access to educational and occupational opportunities,
especially for women. These sociocultural changes may correspond to changes in sex differences in vocational interests acrosscohorts. We expected the effect of the sample moderators to bemuch weaker than the inventory-related moderators that directly
affect sex differences.

Highly Regarded Inventories
A common criticism about meta-analyses is that the good andthe not so good studies get combined, diluting the accuracy ofeffect size estimates. A number of interest inventories are generally held in high regard by the professional testing community oftest developers, teachers, researchers, and practitioners. Although
we are aware of no evidence that the quality of interest inventories
is differentially related to the size of sex differences obtained withthem, estimating the effect sizes for these highly regarded interest
inventories and comparing them with overall effect sizes canprovide additional confidence in the present meta-analytic results.
We propose using the professional reputation of interest inventories as a proxy for their overall quality (this procedure is describedin more detail in the Method section) rather than any single
psychometric characteristic of the inventory (e.g., validity) because it is based on a broad set of criteria, such as inventory
development, reliability, validity, norms, usefulness in practice,and so forth.
Sex differences in the interest inventories that are highly regarded by the professional testing community should be of particular interest to applied researchers and practitioners. Inventories
selected on the basis of their professional reputations are theinterest measures with which practitioners are most likely to have863

received training in administration and interpretation. Therefore,
interest inventories with the strongest reputations are more likely
to be used in applied settings and will possibly have the greatest
impact on the career choices of individuals. In the present study,
we calculated effect sizes of sex differences for most recent
editions of all interest inventories that are currently in print andalso examined effect sizes for inventories that are highly regardedin the field of vocational assessment. Our purpose was twofold:First, the results will enhance understanding about all of thevocational interest measures commercially available and currently
being used in practice. This information can assist practitioners inselecting assessments and interpreting interest scores. Second, theeffect sizes for the most highly regarded interest inventories can beused as a benchmark for comparison purposes. We expected themagnitude and pattern of sex differences for highly regarded
interest inventories to be similar to the results based on all inventories.

MethodLiterature Search ProceduresTechnical manuals of vocational interest inventories constituted
the data source. We went through the first through the 17theditions of the Mental Measurements Yearbook (Buros, 1938,
1941, 1949, 1953, 1959, 1965, 1972, 1978; Conoley & Impara,
1995; Conoley & Kramer, 1989; Geisinger, Spies, Carlson, &Plake, 2007; Impara & Plake, 1998; Kramer & Conoley, 1992;
Mitchell, 1985; Plake & Impara, 2001; Plake, Impara, & Spies,
2003; Spies & Plake, 2005) to identify interest inventories published through 2007. We then searched the publishers’ websites orcontacted the publishers directly to determine whether any new
edition of a test had been published or any new norm sample had
been used since 2007. James Rounds identified interest inventories
from governmental/military sources. Inventories were obtained
either through the library system or from the publishers. In addition, Rong Su visited the Buros Institute of Mental Measurements
to retrieve data from the institute’s archives. The literature search
resulted in 108 inventories.

Inclusion Criteria
Inventories were evaluated for inclusion in the current metaanalysis on the basis of the following criteria: First, the inventories
were published in English with norm samples from the UnitedStates or combined norm samples from both the United States andCanada. Second, the inventories were intended to measure vocational interests. Tests intended to measure educational interests,
such as the College Major Interest Inventory (Whetstone & Taylor,1990) were excluded. Form AH (hand scored) of the Kuder Preference Record (G. F. Kuder, 1948), which measures personality,
was also excluded. Third, the inventories used the same form formale and female respondents. Fourth, and finally, means andstandard deviations for both male and female respondents werereported in the technical manuals, making it possible to calculate
effect sizes of sex differences. Different editions of an inventory
were counted as separate studies if item changes had occurred
between editions. If a new edition of an inventory contained only
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864

SU, ROUNDS, AND ARMSTRONG

weighted its sample and combined it with the sample from theolder edition to avoid statistical dependence. If one sample wasused by multiple inventories (e.g., the Career Assessment Inventory—Enhanced Version, Johansson, 2003, in which an adultsample was also used to develop adult norms for the Interest
Determination, Exploration and Assessment System,
Johansson, 1996), we included only one set of data. Application ofthese inclusion criteria resulted in 47 inventories, published between 1964 and 2007, and a total of 81 samples consisting of
243,670 men and 259,518 women. The mean ages of the samples
ranged from 12.50 to 42.55 years. The oldest cohort of the samples
was born in 1939, and the youngest in 1987.

Coding of Study Variables
Each sample meeting the inclusion criteria was coded for study
variables, including item development (1 ⫽ overlap of male andfemale scores was less than 75% or more than 33% of the items
had response differences larger than 15%; 2 ⫽ overlap of male andfemale scores was between 75% and 85% or 10% to 33% of theitems had response differences larger than 15%; 3 ⫽ overlap ofmale and female scores was larger than 85% or no more than 10%of the items had response differences larger than 15%), scoring(1 ⫽ raw score; 2 ⫽ combined-sex norm), theoretical framework(0 ⫽ RIASEC interests; 1 ⫽ other theoretical models), mean ageof the sample, and year when the sample was collected. For eachsample, we calculated an index for cohort by subtracting mean age
from (year of the sample minus 1900).
Interest inventory scales that were not RIASEC scales werecategorized into the RIASEC types independently by James
Rounds and Rong Su on the basis of the following criteria supported by construct validity evidence from previous studies: First,
if an interest inventory follows Holland’s theoretical framework
but uses different names for the RIASEC scales, the inventory
scales were assigned to the corresponding Holland scales (e.g.,
UNIACT–Revised; American College Testing Program, 1995,
p. 2). Second, if an interest inventory has basic interest scales, weassigned such scales into Holland types on the basis of the scales’
link to a RIASEC type identified from the inventory manual (e.g.,
Interest Determination, Exploration and Assessment System,
Johansson, 1996, pp. 63– 65). Third, if an interest inventory follows Roe’s (1956) model, G. F. Kuder’s (1948) classification, L.
Knapp and Knapp’s (1982) system, or J. P. Guilford, Christensen,
Bond, & Sutton’s (1954) interest categories, we assigned its scales
to RIASEC types on the basis of previous factor analytic results(e.g., R. R. Knapp, Knapp, & Knapp-Lee, 1990, p. 30) establishing
the link between these theoretical frameworks and Holland’s(1959, 1997) model. Fourth, for interest inventories following theUnited States Employment Service occupational interest classification (e.g., Chronicle Career Quest; Chronicle Guidance Publications, 1992), we classified the 12 occupational interest areas ofthis system into the Holland types on the basis of criteria wellrecognized among researchers (G. D. Gottfredson & Holland,
1996, p. 708). Fifth, for interest inventories that do not contain theinformation described above (e.g., Jackson Vocational Interest
Survey; Jackson, 2000), we compared the content of the scaleswith the description of the Holland personality/environment typology (G. D. Gottfredson & Holland, 1996; Holland, 1997) andclassified the scales into the RIASEC types. Agreement rate onclassification of interest scales into the RIASEC types was 96.6%(see Table 1 for the list of interest inventory scales categorized by
RIASEC type).
Rong Su coded all of the inventories; Patrick Ian Armstrong
coded 63% of the inventories. The interrater agreement was 96.4%
on item development, 89.3% on scoring, 100.0% on theoreticalframework, 100.0% on age, and 96.4% on cohort. Differences
between raters were resolved by discussion. A complete list ofcoding classifications on study variables is presented in Table 2.
To identify highly regarded interest inventories, we perused
professional test and measurement books and selected the inventories that were most endorsed. We first conducted a literaturesearch for test and measurement books published in the past 15
years (1994 –2009) using all possible combinations of the following words: interest(s), career, vocational, occupational, test, assessment, and measurement. We then checked the retrieved books
for cross-referenced books and reviewed the content of the books.
Only books including a chapter on interests/values and containing
detailed reviews of selected interest inventories (e.g., a presentation of the scales of an inventory and interpretation of its scores)
were included. This procedure resulted in a total of 28 books. For
books that have multiple editions, we used only the most recent
edition. If one scholar had edited, authored, or coauthored several
different books, we included only the most recent book by that
author to ensure that information from different sources was independent and was not duplicated. Applying these further inclusion criteria led to 14 books. We then tallied the number of times
interest inventories were highlighted in these books.

Analytical Procedures
We calculated sex difference effect sizes for the six RIASEC
types and Prediger’s two dimensions using Cohen’s (1988) d. The
absolute values of the RIASEC effect sizes were then averaged toobtain an average effect size representing the magnitude of sex
difference for each sample. A positive effect size indicated stronger interest for men than for women. We calculated effect sizes for
the Things–People and Data–Ideas dimensions using the formulasin the UNIACT–Revised Edition manual (American College Testing Program, 1995, p. 126), with a larger score indicating stronger
interest in working with things or data. Effect sizes for the
RIASEC scales and the Things–People and Data–Ideas dimensions, as well as the average effect sizes, were then weighted bytheir inverse variance and averaged to generate mean effect sizes
according to a random-effects model of error estimation. We
calculated the 95% confidence intervals and 90% credibility values. We followed the same procedure to compute the mean effectsizes for the latest editions of 29 in-print interest inventories andhighly regarded interest inventories.
For variables with heterogeneous effect sizes, we conducted
moderator analyses to examine the source of heterogeneity using amixed-effects model. We chose the mixed-effects model because
use of this model has been suggested to be the best practice formeta-analysis involving the examination of systematic influence
from moderators (Viechtbauer, 2008; see also Hedges & Vevea,
1998; Lau, Ioannidis, & Schmid, 1998). This model allowed us to
examine the influence of moderators without making the untenableassumption that all variance in effect sizes could be accounted forby systematic factors or that all variance was due to random error.

This document is copyrighted by the American Psychological Association or one of its allied publishers.
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SEX DIFFERENCES IN INTERESTS: META-ANALYSISAs a first step, we examined item development, scoring (rawscores vs. combined-sex norm scores), and interest frameworkusing weighted analysis of variance (ANOVA) and weighted regression by inventory. Second, we evaluated age and cohort using
weighted regression by sample. We performed both effect size
calculation and moderator analyses in SPSS 13.0 using the modules given by Lipsey and Wilson (2001, pp. 208 –220).
To detect the likelihood and possible influence of publicationbias, we conducted a trim-and-fill procedure (Duval & Tweedie,
2000), a nonparametric statistical technique of examining thesymmetry and distribution of effect sizes plotted by inverse ofstandard error. This technique first estimates the number of studies
that may be missing as a result of publication bias and then allows
a new, attenuated effect size to be calculated on the basis of theinfluence such studies would have if they were included in theanalysis. We performed this procedure on the average effect sizes
by inventory and by sample with the statistical computing program
R (Schwarzer, 2008). Two estimators of the number of missing
studies, L0 and R0, were generated and used to evaluate thepotential influence of publication bias.

Results
RIASEC Interests
We began our examination of sex differences with the two broad
RIASEC dimensions of Things–People and Data–Ideas and thenfocused on the six RIASEC types. Table 3 shows the effect sizes
for the RIASEC interests. To further illustrate the implication ofthese effects sizes, we calculated the percentages that male andfemale interest distributions overlap given the effect sizes, using astatistic called percentage overlap proposed by Tilton (1937). As
hypothesized, the mean effect size for the Things–People dimension (d ⫽ 0.93) favored men and was the largest effect size
observed. In contrast, a very small sex difference was found for theData–Ideas dimension (d ⫽ ⫺0.10). The RIASEC interest scales,with the exception of Enterprising, showed statistically significant
sex differences. As expected, men showed stronger Realistic andInvestigative interests, whereas women showed stronger Artistic,
Social, and Conventional interests. Most of the Things–People sex
difference could be accounted for by the large Realistic interest
effect size (d ⫽ 0.84) and the moderate Social interest effect size(d ⫽ ⫺0.68). The effect sizes for the Investigative (d ⫽ 0.26),
Artistic (d ⫽ ⫺0.35), and Conventional (d ⫽ ⫺0.33) interests
were small. Figure 1 illustrates the sex differences in RIASEC
interests.
Table 4 shows sex differences for the latest editions of 29
in-print interest inventories. The interest inventories are ordered by
the magnitude of their average effect size. Large variability wasevident across inventories. The inventory having the smallest sex
differences was the Career Occupational Preference System Interest Inventory—Revised Edition (R. R. Knapp & Knapp, 1979),
with an average effect size of 0.08; all of the other effect sizes were
within ⫾0.20. The Vocational Interest Inventory–Revised (Lunneborg, 1993), the Career Assessment Inventory—Enhanced Version (Johansson, 2003), the UNIACT-R (American College Testing Program, 1995), and the O*NET Interest Profiler (U.S.
Department of Labor, 2000) also showed relatively small sex
differences. In comparison, the Occupational Aptitude Survey and865

Interest Schedule—Third Edition: Interest Schedule (Parker, 2002)
had an average effect size of 0.79. The Self-Directed Search(Holland et al., 1994) showed large sex differences, particularly for
Realistic interests and the Things–People dimension. We highlightthe results for the seven most highly regarded interest inventories(cited by at least one third of the 14 most currently publishedprofessional test and measurement textbooks: the Strong Interest
Inventory, cited 13 times; the Self-Directed Search, cited 13 times;
the Kuder Occupational Interest Survey, cited 10 times; the Campbell Interest and Skill Survey, cited 10 times; the Career Assessment Inventory—Vocational Version, cited eight times; the Jackson Vocational Interest Survey, cited eight times; the UNIACT,
cited five times) and calculated their mean effect sizes. Table 4
also displays the mean effect sizes for these highly regarded
inventories as well as for all 29 in-print interest inventories. Except
for Conventional interests, the magnitude and pattern of sex differences from highly regarded inventories were very similar to theoverall results, indicating that professional reputation of an interest
inventory had very little impact on the meta-analytic results of sex
differences.
Figure 2 illustrates the sex differences in the Things–People andData–Ideas dimensions for the interest inventories. All of theinventories had positive effect sizes, ranging from 0.14 to 1.65, on
the Things–People dimension, indicating men’s predominant interest in things-oriented activities and occupations rather thanpeople-oriented activities and occupations. In contrast, the effectsizes were distributed evenly on the Data end and the Ideas end,
with less extreme values, suggesting that men and women differedvery little in their preference for working with data or working
with ideas.
We next evaluated the homogeneity of the effect sizes. The Q
statistic (Hedges & Olkin, 1985) showed that the effect sizes for all
interest variables were heterogeneous, indicating the possibility ofmoderator variables to account for this heterogeneity. Tables 5 and 6
show the results of the moderator analyses. Item development had
the greatest moderating effect on sex differences. An inverse
variance weighted one-way ANOVA followed by an inverse variance regression showed that item development had a significant
negative impact on the size of sex differences for the Things–
People dimension (␤ ⫽ ⫺.42, p ⬍ .01, R2 ⫽ 21.8%), the Realistic
type (␤ ⫽ ⫺.46, p ⬍ .01, R2 ⫽ 26.6%), the Investigative type (␤ ⫽⫺.37, p ⬍ .05, R2 ⫽ 13.3%), the Enterprising type (␤ ⫽ ⫺.47, p ⬍.01, R2 ⫽ 13.7%), and the average effect size (␤ ⫽ ⫺.32, p ⬍ .05,
R2 ⫽ 12.6%). The sex differences were reduced for Realistic
interests, from a d of 0.91 to a value of 0.35, and for Investigativeand Enterprising interests, item selection reduced statistically significant effect sizes (d ⫽ 0.29 for Investigative and d ⫽ 0.09 forEnterprising) to nonsignificant effect sizes (d ⫽ 0.05 for Investigative and d ⫽ ⫺0.07 for Enterprising). When inventory developers used a variety of methods to select items that showed small
or no sex differences, smaller response differences between men
and women were found.
Scoring did not significantly influence the average effect sizes
and had mixed moderating effects for different interest types anddimensions. The use of combined-sex norm scores compared with
raw scores led to smaller sex differences for the Realistic type (d ⫽0.88 for raw scores; d ⫽ 0.69 for combined-sex norm scores, p ⬍.01). Raw scores reduced sex differences for the Artistic type (d ⫽⫺0.32 for raw scores; d ⫽ ⫺0.47 for combined-sex norm scores,

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SU, ROUNDS, AND ARMSTRONG

866
Table 1
List of Interest Inventory Scales by RIASEC Type

Scales classified into RIASEC type
Inventory
CCQ

Realistic

Investigative

Artistic

SocialEnterprising

Conventional

Plants and Animals,Protective, Mechanical,
Industrial, Physical
Performing
Outdoors, Industrial Arts

Scientific

Artistic

Accommodating,
Humanitarian

Leading–Influencing,
Selling

Business Detail

Science andTechnology

Art, Writing

Administration, Sales

Clerical, Food
Service

CII

Agriculture, Building Trades,
Transportation, Benchwork,
Machine Operation

Social Science,
Mathematics/
Science

Fine Arts

Health Service,
Teaching andSocial Service,
Personal Service
Health Services,
Educational
Services

CISS
COPS

Producing, Adventuring
Technology–Professional,
Technology–Skilled,
OutdoorAnalyzing
Science–
Professional,
Science–
SkilledGOCL II

Technology–Mechanical,
Technology–Industrial,
Mechanical, Outdoor
Natural, Mechanical

Arts

ServiceBusiness

Literary, Artistic,
Creative
Creative Arts,
WritingServiceEnterprising,
Leadership
Public Speaking,Business, Sales

CDI

GZII
IDEAS

Mechanical/Fixing, Protective
Services, Nature/Outdoors

JVIS

Adventure, Engineering,
Nature-Agriculture, Skilled
TradesKCS

Outdoor, Mechanical

KGIS

Outdoor, Mechanical

KOIS

Outdoor, Mechanical

Scientific

OASIS: IS Nature, Protective,
Mechanical, Industrial,
Physical Performing
OVIS
Manual Work, Machine Work, Medical
Inspecting and Testing,
Crafts and Precise
Operation, Agriculture,
Applied Technology

OVIS-II

Manual Work, Machine
Operation, Crafts andPrecise Operations,
Engineering and PhysicalSciences, Agriculture andLife Sciences

ClericalServices,
Customer
Services
Creating
Helping
Influencing
Organizing
Communication,
Service–Professional, Business–Professional, Clerical,
Arts–Professional,
Service–Skilled
Business–Skilled
ConsumerArts–Skilled
Economics

Mathematics,
Science,
MedicalMath, PhysicalCreative Arts,
Science, Life
Performing Arts,
Science,
AuthorMedicalJournalism,
Services,Technical Writing
Social Science
Scientific
Communications,Fine and
Performing Arts
Scientific
Artistic, Literary,MusicalScientific
Artistic, Literary,MusicalScientific
Artistic

Community Service,
Education, Child
Care
Personal Services,
Family Activity,
Teaching, Social
Service,
Elementary
Education
Social Service

Legal Services, Sales,
Management

ClericalOffice Practices,
Food Service

Professional Advising, Finance, OfficeBusiness, Sales,
Work
Supervision, HR
Management, Law
Persuasive,
Managerial

Computational,
ClericalSocial Service

Persuasive

Social Service

Persuasive

Accommodating,
Humanitarian

Selling, LeadingInfluencing

Computational,
ClericalComputational,
ClericalBusiness Detail

Literary, Artistic,
Personal Services,Management andMusic,
Skilled Personal
Supervision, SalesEntertainment andServices, Training,
Representative,
Performing Arts
Care PeoplePromotion andAnimals,
Communication,
Teaching/
AppraisalCounseling/andSocial Work,
Nursing andRelated Technical
Services
Medical Services Communications,
Health Services,Management,
Visual Arts,
Basic Services,
Marketing, Legal
Performing Arts,
Skilled Personal
Services, Customer
Music, Sport andServices,
Services
Recreation
Education andSocial Work

Clerical Work,
Customer
Services,
Numerical

Clerical,Numerical(table continues)

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

867

Table 1 (continued)
Scales classified into RIASEC type
Inventory

Realistic

InvestigativeVII

Outdoor, Technology

Science

VRII

Plants and Animals,Protective, Mechanical,
Industrial, Physical
Performing
Primary Outdoor, Machine
Work, Structural Work,
Mechanical & ElectricalWork, Mining, Processing,Engineering

WOWI

Artistic

SocialEnterprising

Conventional

ServiceBusiness Contact

Organization

Science

Art andEntertainment,
General Culture
Artistic

Accommodating,
Humanitarian

Leading-Influence,
Selling

Business
DetailSciences

Arts, Graphic Arts

Service, PublicService

Managerial, Sales,
Business Relations

Benchwork,
ClericalNote. CCQ ⫽ Chronicle Career Quest; CDI ⫽ Career Decision Inventory; CII ⫽ Career Interest Inventory; CISS ⫽ Campbell Interest and Skill Survey;
COPS ⫽ Career Occupational Preference System Interest Inventory; GOCL ⫽ Gordon Occupational Check List; GZII ⫽ Guilford-Zimmerman Interest
Inventory; IDEAS ⫽ Interest Determination, Exploration and Assessment System; JVIS ⫽ Jackson Vocational Interest Survey; KCS ⫽ Kuder Career
Search with Person Match; KGIS ⫽ Kuder General Interest Survey; KOIS ⫽ Kuder Occupational Interest Survey; OASIS: IS ⫽ Occupational Aptitude
Survey and Interest Schedule: Interest Schedule; OVIS ⫽ Ohio Vocational Interest Survey; VII ⫽ Vocational Interest Inventory; VRII ⫽ Vocational
Research Interest Inventory; WOWI ⫽ World of Work Inventory; HR ⫽ Human Resources.

p ⬍ .01). In comparison with item development, scoring had amuch weaker moderating effect and generally accounted for no
more than 10% of the variance.
Finally, we examined the moderating effect of the theoreticalframework used in interest inventories. The results showed thatdifferent interest frameworks had a significant impact on sex
differences only for Realistic and Conventional interests, indicating that inventories using other theoretical frameworks might
include scales measuring constructs that differ from the Realistic
and Conventional interests measured by the Holland model. Inventories including RIASEC scales showed larger sex difference
in Realistic interests (d ⫽ 0.96) than did inventories using otherframeworks (d ⫽ 0.70, p ⬍ .01). The reverse is true for Conventional interests (d ⫽ ⫺0.22 for inventories containing RIASEC
scales and d ⫽ ⫺0.43 for inventories using other frameworks,p ⬍ .05).
In all, inventory-related moderators accounted for 28.5% of thevariance in the Things–People dimension, 18.2% of the variance inthe Data–Ideas dimension, 48.1% in the Realistic type, 13.4% in
Investigative type, 15.5% in the Artistic type, 11.1% in the Social
type, 22.2% in the Enterprising type, 9.9% in the Conventional
type, and 13.2% for the average effect sizes. Among the inventoryrelated moderators coded in the meta-analysis, item development
accounted for the majority of this variance.
Sample-related moderators, as hypothesized, showed a weakermoderating effect than did inventory-related moderators. Results
indicated that, on average, sex differences were smaller for older
group (␤ ⫽ ⫺.36, p ⬍ .01) and younger cohort (␤ ⫽ ⫺.29, p ⬍.05). As age increases, effect size of the sex differences on theThings–People dimension (␤ ⫽ ⫺.24, p ⬍ .05) and Social interest(␤ ⫽ .45, p ⬍ .01) decreases. Younger cohort showed smaller sex
differences in the Data–Ideas dimension (␤ ⫽ ⫺.50, p ⬍ .01) andin the Artistic (␤ ⫽ .58, p ⬍ .01) and Enterprising (␤ ⫽ ⫺.54, p ⬍.01) interests.
Taken together, sample variables accounted for 24.1% of thevariance in the Data–Ideas dimension (5.4% by age and 24.0% bycohort, separately), 25.7% in the Artistic type (0.4% by age and22.7% by cohort), 23.8% in the Social type (23.8% by age and8.5% by cohort), 28.1% in the Enterprising type (6.9% by age
and 28.0% by cohort), and 10.9% for the average effect sizes(5.0% by age and 1.1% by cohort). In comparison with inventory
variables, sample variables showed significant moderating effects
for fewer interest types: We found that sex differences in Artistic
and Enterprising interests were smaller for younger generations
and that sex differences in Social interests decreased as people
aged. Age and cohort seemed to impact different interest types:
Age influenced the Things–People dimension, yet cohort had aneffect on interest types that anchor the Data–Ideas dimension.
We performed the trim-and-fill procedure on average effectsizes by inventory and by sample. The funnel plot of average effectsizes by sample is presented in Figure 3. Analyses based on boththe L0 and the R0 estimators indicated no missing studies, suggesting that the current study was not threatened by publication bias.

STEM Interests
We conducted three supplementary analyses using basic interest
scales in science, mathematics, and engineering-related areas to
examine sex differences in the STEM fields. In these analyses, we
not only considered the size of sex differences and relevant moderators but also examined the variance ratio of male and femaleinterests and the comparative size of differences within male andfemale respondents in comparison with the differences between
male and female respondents.
Method. We used the same pool of interest inventory manuals
as in the previous analyses for these analyses. We selected scales
on the basis of the content of their items to represent the threefields. To calculate effect sizes of sex differences in interests forthe science field, we included all science basic interest scales(including scales with the names Physical Science and Research);
to calculate effect sizes for the mathematics field, we included allmathematics, numerical, and computational scales; to calculateThis document is copyrighted by the American Psychological Association or one of its allied publishers.
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SU, ROUNDS, AND ARMSTRONG

868

Table 2
Overview of the Meta-Analysis Database: Average Effect Size and Moderator Variables by Sample
Moderator
Sample size
Inventory
ACT-IV
ACT-IV
ASVAB
CAI-E
CAI-V
CCQ-Form L
CCQ-Form S
CDI
CDI
CDI
CDI
CDI
CDI
CDI
CDM
CDM
CDM
CDM
CDM
CDM-Revised
CDM-Revised
CDM-Spanish
CDM-Spanish
CII-Level 1
CII-Level 1
CII-Level 1
CII-Level 2
CII-Level 2
CII-Level 2
CISS
COPS
COPS

Referenced

Itemdevelopment

Scoring

Theoreticalframework

Age(years)

CohortN

Male

FemaleHanson, 1974
Hanson, 1974
U.S. Department of
Defense, 2005
Johansson, 2003
Johansson, 1984
Chronicle Guidance
Publications,
1992
Chronicle Guidance
Publications,
1992
Jackson, 1986
Jackson, 2003
Jackson, 2003
Jackson, 2003
Jackson, 2003
Jackson, 2003
Jackson, 2003
Harrington &O’Shea, 1981
Harrington &O’Shea, 1981
Harrington &O’Shea, 1981
Harrington &O’Shea, 1981
Harrington &O’Shea, 1981
Harrington &O’Shea, 2001
Harrington &O’Shea, 2001
Harrington &O’Shea, 1981
Harrington &O’Shea, 2001
Psychological
Corporation,
1991
Psychological
Corporation,
1991
Psychological
Corporation,
1991
Psychological
Corporation,
1991
Psychological
Corporation,
1991
Psychological
Corporation,
1991
Campbell et al.,
1992
R. R. Knapp et al.,
1990
R. R. Knapp et al.,
1990

0.57
0.43
0.59

1
1
2

1
1
2

0
0
0

17.50
20.50
13.37

56.50
53.50
83.63

2,971
1,218
1,958

1,233
666
945

1,738
552
1,013

0.25
0.50
0.48

2
1
2

2
2
1

0
0
1

40.35
36.60
16.00

45.65
39.40
74.00

900
1,500
1,311

450
750
661

450
750
650

0.58

2

1

1

14.00

76.00

1,536

797

739

0.62
0.84
0.64
0.55
0.53
0.56
0.58
0.12

1
1
1
1
1
1
1
1

1
1
1
1
1
1
1
1

1
1
1
1
1
1
1
0

16.50
18.50
25.00
35.00

86.50
84.50
78.00
68.00

20.00

71.00

1,000
212
737
386
392
317
276
1,089

500
114
385
206
171
148
145
577

500
98
352
180
221
169
131
512

0.32

1

1

0

1,246

567

679

0.57

1

1

0

13.50

67.50

4,004

2,045

1,959

0.61

1

1

0

16.50

64.50

5,646

3,083

2,563

0.45

1

1

0

18.50

62.50

2,925

1,130

1,795

0.45

1

1

0

13.47

77.53

965

483

482

0.42

1

1

0

16.29

74.71

996

496

500

0.46

1

1

0

648

288

360

0.47

1

1

0

16.10

73.90

966

420

546

0.38

1

1

1

12.50

76.50

13,733

6,901

6,832

0.41

1

1

1

13.50

75.50

20,025

10,054

9,971

0.42

1

1

1

14.50

74.50

26,361

13,04813,313

0.40

1

1

1

15.50

73.50

14,708

7,362

7,346

0.37

1

1

1

16.50

72.50

8,793

4,401

4,392

0.38

1

1

1

17.50

71.50

8,679

4,438

4,241

0.49

1

2

1

5,241

3,442

1,799

0.48

1

1

1

15.50

66.50

4,145

2,034

2,111

0.40

1

1

1

20.00

62.00

1,445

773

672

(table continues)

This document is copyrighted by the American Psychological Association or one of its allied publishers.
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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

869

Table 2 (continued)
Moderator
Sample size
Inventory
COPS
COPS
COPS-Revised
GOCL II
GZII
IDEAS
IDEAS
IDEAS
IDEAS
IF
JVIS
JVIS
JVIS
KGIS (Form E)
KGIS (Form E)
KGIS (Form E)
KGIS (Form E)
KOIS (Form DD)KCSOASIS-3: IS
OⴱNET IPOVISOVIS-IIOVIS-IIOVIS-II
SDS-E
SDS-R
SDS-R
SDS-R
SDS-R
SDS-R
SDS-R
SDS-R
SII
SII
SII
SII
UNIACT
UNIACT-R
UNIACT-R
UNIACT-R

Referenced

Itemdevelopment

Scoring

Theoreticalframework

Age(years)

CohortN

Male

FemaleR. R. Knapp et al.,
1990
R. R. Knapp et al.,
1990
R. R. Knapp &Knapp, 1979
Gordon, 1981
Guilford &Zimmerman,
1989
Johansson, 1978
Johansson, 1978
Johansson, 1996
Johansson, 1996
Wall et al., 1996
Jackson, 1977
Jackson, 2000
Jackson, 2000
Kuder & Zytowski,
1988
Kuder & Zytowski,
1988
Kuder & Zytowski,
1988
Kuder & Zytowski,
1988
Kuder & Zytowski,
1991
Zytowski, 2007
Parker, 2002
U.S. Department ofLabor, 2000
D’Costa et al.,1970
Winefordner, 1983
Winefordner, 1983
Winefordner, 1983
Holland et al., 1994
Holland, 1972
Holland, 1972
Holland, 1979
Holland, 1985
Holland et al., 1994
Holland et al., 1994
Holland et al., 1994
Campbell, 1974
Hansen &Campbell, 1985
Harmon et al., 1994
Donnay et al., 2005
American College
Testing Program,
1981
American College
Testing Program,
1995
American College
Testing Program,
1995
American College
Testing Program,
1995

0.40

1

1

1

15.00

73.00

14,619

7,565

7,054

0.42

1

1

1

20.00

68.00

3,237

1,379

1,858

0.08

3

1

1

15.00

64.00

400

200

200

0.33
0.56

1
1

1
1

1
1

17.00

64.00

359
215

168
97

191
118

0.64
0.59
0.55
0.57
0.34
0.33
0.37
0.26
0.72

1
1
2
2
2
1
1
1
1

2
2
2
2
1
1
1
1
1

1
1
1
1
0
1
1
1
1

16.00
12.50
13.50
16.50
16.40
17.40
15.00

61.00
64.50
75.50
72.50
77.60
59.60
84.00

12.50

50.50

3,436
598
1,770
2,891
1,313
1,000
2,380
1,120
4,109

1,755
292
820
1,208
591
500
1,190
560
2,080

1,681
306
950
1,683
722
500
1,190
560
2,029

0.71

1

1

1

16.00

47.00

5,704

2,766

2,938

0.67

1

1

1

12.50

74.50

5,894

2,714

3,180

0.68

1

1

1

16.00

71.00

7,113

3,402

3,711

0.34

1

1

1

3,214

1,583

1,631

0.34
0.79
0.30

1
2
2

2
1
1

1
1
0

23.60
15.50
32.46

63.54

3,619
1,091
1,123

1,663
551
529

1,956
540
594

0.42

1

1

1

15.50

53.50

46,181

23,272

22,909

0.31
0.36
0.31
0.63
0.77
0.65
0.43
0.45
0.52
0.48
0.51
0.42
0.30

2
2
2
1
1
1
1
1
1
1
1
1
1

1
1
1
1
1
1
1
1
1
1
1
2
2

1
1
1
0
0
0
0
0
0
0
0
0
0

13.50
16.50
20.00
24.50
16.50
20.00

66.50
63.50
60.00
71.50
53.50
50.00

16.50
20.00
38.65
34.30
38.20

77.50
74.00
55.35
39.70
46.80

9,800
6,672
2,800
717
4,961
1,438
600
768
819
1,114
656
600
600

4,915
3,308
1,057
313
2,384
578
235
297
344
399
251
300
300

4,885
3,364
1,743
404
2,577
860
365
471
475
715
405
300
300

0.32
0.38
0.21

1
1
3

2
2
2

0
0
0

42.55
35.46
17.50

51.45
66.54
63.50

18,951
2,250
4,631

9,484
1,125
1,247

9,467
1,125
1,693

0.30

3

2

0

13.50

78.50

4,631

2,294

2,307

0.30

3

2

0

15.50

76.50

4,133

1,979

2,132

0.27

3

2

0

17.50

75.50

4,679

2,219

2,426

83.40

(table continues)

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870
Table 2 (continued)

Moderator
Sample size
Inventory
VII
VII-R
VPI
VPI
VRII
VRII
WOWI

Referenced

Itemdevelopment

Scoring

Theoreticalframework

Age(years)

CohortN

Male

FemaleLunneborg, 1981
Lunneborg, 1993
Holland, 1965
Holland, 1977
Vocational
Research
Institute, 1988
Vocational
Research
Institute, 1988
Ripley et al., 2001

0.36
0.23
0.55
0.38
0.45

3
3
1
1
1

2
2
1
1
1

1
1
0
0
1

16.50
16.50
20.00
28.93
16.60

59.50
68.50
45.00
48.07
68.40

600
1,562
12,433
732
856

300
748
6,290
354
429

300
814
6,143
378
427

0.49

1

1

1

28.10

56.90

525

198

327

0.38

1

1

1

28.88

68.12

169,436

78,56490,872Note. d ⫽ inverse variance weighted effect size; ACT-IV ⫽ ACT Interest Inventory; ASVAB ⫽ Armed Services Vocational Aptitude Battery; CAI-E ⫽Career Assessment Inventory–Enhanced Version; CAI-V ⫽ Career Assessment Inventory—Vocational Version; CCQ ⫽ Chronicle Career Quest; CDI ⫽Career Decision Inventory; CDM ⫽ Harrington–O’Shea Career Decision-Making System; CII ⫽ Career Interest Inventory; CISS ⫽ Campbell Interest andSkill Survey; COPS ⫽ Career Occupational Preference System Interest Inventory; GOCL ⫽ Gordon Occupational Check List; GZII ⫽ Guilford–
Zimmerman Interest Inventory; IDEAS ⫽ Interest Determination, Exploration and Assessment System; IF ⫽ Interest Finder; JVIS ⫽ Jackson Vocational
Interest Survey; KGIS ⫽ Kuder General Interest Survey; KOIS ⫽ Kuder Occupational Interest Survey; KCS ⫽ Kuder Career Search with Person Match;
OASIS-3: IS ⫽ Occupational Aptitude Survey and Interest Schedule— Third Edition: Interest Schedule; O*NET IP ⫽ O*NET Interest Profiler; OVIS ⫽Ohio Vocational Interest Survey; SDS-E ⫽ Self Directed Search (Form E); SDS-R ⫽ Self Directed Search (Form R); SII ⫽ Strong Interest Inventory;
UNIACT ⫽ Unisex Edition of ACT Interest Inventory; UNIACT-R ⫽ Unisex Edition of ACT Interest Inventory–Revised Edition; VII ⫽ Vocational
Interest Inventory; VII-R ⫽ Vocational Interest Inventory—Revised; VPI ⫽ Vocational Preference Inventory; VRII ⫽ Vocational Research Interest
Inventory; WOWI ⫽ World of Work Inventory. In the coding for item development, 1 represents an overlap of male and female scores of less than 75%
or cases in which more than 33% of the items have response differences larger than 15%; 2 represents an overlap of male and female scores from 75%
to 85% or 10% to 33% of the items have response differences larger than 15%; 3 represents an overlap of male and female scores larger than 85% or inwhich no more than 10% of the items have response differences larger than 15%. For scoring, 1 ⫽ raw score and 2 ⫽ combined-sex norm. Theoreticalframework was coded as 0 ⫽ RIASEC model; 1 ⫽ other interest models.

effect size for the engineering and related fields, we used engineering, mechanical, electronics, machine work, and technology
scales. The Engineering and Physical Sciences scale in the Ohio
Vocational Interest Survey II (Winefordner, 1983) was classified
as an engineering scale. The Math/Science scale in the Career
Interest Inventory (Psychological Corporation, 1991) was classified as a mathematics scale. Effect size calculations and moderator
analyses followed the same procedures as were used with RIASEC
interests. The differences between the first quartile and the third
quartile of the male and female groups were used as an indicator
for intragroup differences and were divided by the intergroupmean differences for each inventory. The ratios formed were
denoted as the male ratio and the female ratio. We calculated
variance ratios by dividing female variance by male variance. A
log transformation was performed on each ratio while aggregating
the male and female ratios and the variance ratios. When d ⫽ 0,
and the male ratio and female ratio were infinite, they were set to
the next largest value.
Results. For the science field, we computed and aggregated 34
effect sizes. The mean effect size for sex differences in science was.36 ( p ⬍ .01, ⫺95% CI ⫽ 0.27, ⫹95% CI ⫽ 0.46). Because theeffect sizes showed heterogeneity, we again examined moderating
effects of two inventory variables (item development and scoring)and two sample variables (age and cohort). Only item development
had a significant impact, reducing the effect size from d ⫽ 0.43 tod ⫽ ⫺0.05 (␤⫽ ⫺.43, p ⬍ .01), accounting for 20.2% of thevariance. Within each level of item development, effect sizes were
homogeneous. The four moderator variables accounted for 35.9%of the variance of sex differences in the science area.

For the mathematics field, we computed and aggregated 30
effect sizes. Mean effect size for sex differences in mathematics
was 0.34 ( p ⬍ .01, ⫺95% CI ⫽ 0.27, ⫹95% CI ⫽ 0.40).
Moderator analysis showed that scoring had a substantial effect on
the effect size (␤ ⫽ ⫺.63, p ⬍ .01). Inventories with combined-sex
norm scores had smaller effect sizes (d ⫽ 0.19) than inventories
with raw scores (d ⫽ 0.44). Scoring accounted for 41.7% of thevariance. Other moderators were not significant. The four moderator variables accounted for 64.1% of the variance of sex differences in mathematics areas.
For the engineering field, we computed and aggregated 45 effectsizes. Mean effect size for sex differences in engineering was 1.11
( p ⬍ .01, –95% CI ⫽ 1.01, ⫹95% CI ⫽ 1.20). Item development,
age, and cohort showed significant moderating effects on the effectsize. Item development significantly reduced the effect size fromd ⫽ 1.18 to d ⫽ 0.58 (␤ ⫽ ⫺.37, p ⬍ .01) and accounted for
17.3% of the variance. Effect sizes within each level of item
development were homogeneous. Older samples had a smaller
effect size than younger samples (␤ ⫽ ⫺.62, p ⬍ .01). Over the
decades, there was a trend for younger cohort to have smaller sex
differences in engineering (␤ ⫽ ⫺.30, p ⬍ .05). Age and cohort
accounted for 25.1% of the variance. These four moderator variables accounted for 57.4% of the variance in sex differences inengineering areas. In summary, engineering stood out by having avery large effect size and science and mathematics had small effectsizes, all favoring men. Only engineering interests were impacted
by sample variables of age and cohort; sex differences in scienceand mathematics fields were moderated only by inventory variables, not sample variables.

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

871

Table 3Weighted Mean Effect Sizes for Things–People and Data–Ideas Dimensions, RIASEC Types, and Science, Technology, Engineering,and Mathematics (STEM) Interests

Interest
Dimension
Things–People
Data–Ideas
RIASEC interests
Realistic
Investigative
Artistic
SocialEnterprising
Conventional
Average d
STEM interests
Science
Mathematics
Engineering

Female–maleratio among
top 25%kd

SD

Lower 95% CI

Upper 95% CI

Lower 90% CV

Upper 90% CV

Male–female
overlap

79
79

0.93
ⴚ0.10

0.242
0.217

0.87
ⴚ0.15

0.99
ⴚ0.05

0.52
⫺0.46

1.33
0.27

46.9%
92.3%

0.287
1.134

80
79
80
80
79
80
80

0.84
0.26
ⴚ0.35
ⴚ0.68
0.04
ⴚ0.33
0.45

0.207
0.217
0.154
0.236
0.182
0.272
0.101

0.79
0.20
ⴚ0.39
ⴚ0.74
⫺0.01
ⴚ0.39
0.43

0.89
0.31
ⴚ0.31
ⴚ0.62
0.08
ⴚ0.26
0.48

0.49
⫺0.11
ⴚ0.61
ⴚ1.08
⫺0.27
⫺0.78
0.28

1.19
0.62
ⴚ0.09
ⴚ0.28
0.34
0.13
0.62

50.9%
81.9%
75.0%
58.4%
96.9%
76.8%
69.8%

0.327
0.720
1.556
2.426
0.963
1.478

34
30
45

0.36
0.34
1.11

0.221
0.169
0.279

0.27
0.27
1.01

0.46
0.40
1.20

⫺0.01
0.06
0.64

0.74
0.62
1.57

75.0%
76.2%
40.7%

0.602
0.638
0.195

Note. k ⫽ number of effect sizes; d ⫽ inverse variance weighted effect sizes; CI ⫽ confidence interval; CV ⫽ credibility value; Male–female overlap
represents the overlap of male and female distribution of interests; Female–male ratio among top 25% ⫽ percentage of male participants divided by thepercentage of female participants among the top 25% in overall population distribution of interests. Significant effect sizes (ds) are presented in boldface.
Similarly, confidence intervals (CIs) and credibility values (CVs) in boldface represent significant values not including 0 within the interval.

Analysis of variance ratios (VRs) showed that men had larger
variability than women in their interests in the engineering areas(mean VR ⫽ 1.51, ⫺95% CI ⫽ 1.32, ⫹95% CI ⫽ 1.74). For male
respondents, intragroup variances were larger than intergroup variances. The mean ratio of intra- versus intergroup differences was
1.35 ( p ⬍ .01). For female respondents, the mean ratio was 1.10
(ns). In contrast, men and women had about equal variability intheir science interests and their mathematics interests (mean VR ⫽1.03 for science, ns; mean VR ⫽ 1.06 for mathematics, p ⬍ .05).
In both science and mathematics, intragroup variances were muchlarger than intergroup variances. The mean ratio of intra- versus
intergroup difference was 5.00 for men ( p ⬍ .01) and 4.95 for

Figure 1. Effect size of RIASEC interests. R ⫽ Realistic; I ⫽ Investigative; A ⫽ Artistic; S ⫽ Social; E ⫽ Enterprising; C ⫽ Conventional.

women ( p ⬍ .01) in science. The mean ratio was 4.91 for men( p ⬍ .01) and 4.78 for women ( p ⬍ .01) in mathematics. These
results showed that intragroup differences were substantially larger
than intergroup differences in the STEM fields, both for men andfor women, indicating the importance of considering individual
differences in vocational interests.

Discussion
Despite improvement over the past four decades in the numberof women pursuing careers in the STEM fields, the continued
underrepresentation of women in these fields is an issue of great
concern to researchers and policy makers. Sex differences in career
preferences are often cited as among the most important underlying reasons for gender disparity in the STEM fields (e.g., Lubinski& Benbow, 1992, 2006, 2007). Researchers and policy makers,
however, have little information on the size and pattern of sex
differences in interests. The present study provides a systematic
review of sex differences in interests that can inform the ongoingdebate and can lay the ground for future research on genderdisparity in the STEM areas.
Except for a few variables, such as quantitative reasoning andspatial ability (Austin & Hanisch, 1990; Wai, Lubinski, & Benbow, in press), past research on individual differences domains
other than interests has generally suggested that sex differences are
small (Hyde, 2005; Maccoby, 1990). The present study, however,revealed substantial sex differences in vocational interests. The
largest difference between men and women was found along theThings–People dimension, with men gravitated toward thingsoriented careers and women gravitated toward people-oriented
careers. Men generally showed more Realistic and Investigativeinterests as well as stronger interests in the STEM areas; incomparison, women tend to have more Artistic, Social, and Conventional interests and to express less interest in the STEM fields.

18,104

35,547

17,357

200
814
450
6,865
594
9,992
1,750
1,956
1,631
90,872378
1,125
30,116
8,912
15,979
982
754
546
650
1,799
750
1,151
6,891
2,633
739
1,013
404
3,437
540
193,923

Female

0.89

0.14
0.36
0.41
0.67
0.55
0.75
0.98
0.78
0.94
0.89
0.80
1.03
0.73
0.83
0.66
1.17
0.30
1.04
0.87
0.88
0.83
1.06
1.16
0.86
0.73
1.58
1.11
1.65
1.20
0.86

Things–People

Realistic
0.16
0.49
0.49
0.40
0.81
0.68
0.62
0.35
0.66
0.90
0.69
1.16
0.81
0.81
0.74
1.14
0.49
1.20
0.59
0.72
0.97
1.09
0.89
0.75
0.52
1.07
1.40
1.70
0.78
0.78
0.80

Data–Ideas
⫺0.06
⫺0.11
0.27
ⴚ0.15
⫺0.25
0.34
ⴚ0.06
0.17
0.02
⫺0.20
0.50
0.08
⫺0.22
⫺0.10
⫺0.36
0.08
⫺0.78
⫺0.43
⫺0.16
0.54
ⴚ0.12
⫺0.48
⫺0.12
⫺0.22
⫺0.57
⫺0.03
⫺0.27
0.00
⫺0.59
ⴚ0.12
ⴚ0.01

Dimension

0.23

0.04
⫺0.03
0.13
0.06
0.24
⫺0.20
0.28
0.33
0.23
0.19
0.05
0.29
0.03
0.18
0.05
0.00
0.08
0.37
0.00
0.39
0.25
0.78
0.67
⫺0.08
0.00
0.25
⫺0.11
0.42
0.37
0.19

Investigative

ⴚ0.30

⫺0.09
⫺0.11
⫺0.49
ⴚ0.20
⫺0.02
⫺0.26
ⴚ0.20
⫺0.24
ⴚ0.19
⫺0.24
⫺0.40
ⴚ0.26
⫺0.21
⫺0.38
⫺0.13
⫺0.39
⫺0.18
⫺0.10
⫺0.60
ⴚ0.85
ⴚ0.58
⫺0.21
⫺0.38
⫺0.56
⫺0.60
⫺0.55
⫺0.31
ⴚ0.60
⫺0.80
ⴚ0.34

Artistic

ⴚ0.60

⫺0.01
⫺0.31
⫺0.08
ⴚ0.62
⫺0.30
⫺0.52
ⴚ0.72
⫺0.71
ⴚ0.81
⫺0.47
⫺0.52
ⴚ0.40
⫺0.60
⫺0.54
⫺0.71
⫺0.81
⫺0.53
⫺0.60
⫺0.78
ⴚ0.29
ⴚ0.46
⫺0.47
⫺0.86
⫺0.77
⫺0.77
⫺1.28
⫺0.97
ⴚ1.02
⫺1.14
ⴚ0.63

SocialRIASEC type

0.03

⫺0.09
0.04
0.25
ⴚ0.29
0.07
⫺0.02
ⴚ0.09
0.27
0.17
⫺0.15
0.33
0.08
⫺0.08
0.00
⫺0.07
⫺0.06
⫺0.30
⫺0.21
⫺0.23
0.41
0.07
0.03
0.31
⫺0.26
⫺0.47
⫺0.12
0.01
0.25
⫺0.45
ⴚ0.03

Enterprising

ⴚ0.12

⫺0.06
⫺0.40
⫺0.06
ⴚ0.17
⫺0.34
0.21
0.06
0.11
0.00
⫺0.32
0.30
0.11
⫺0.57
⫺0.43
⫺0.77
⫺0.17
⫺1.20
⫺0.36
⫺0.70
0.26
ⴚ0.66
⫺0.42
⫺0.12
⫺0.83
⫺1.14
⫺0.26
⫺0.96
ⴚ0.42
⫺1.18
ⴚ0.35

Conventional

0.38

0.08
0.23
0.25
0.29
0.30
0.32
0.33
0.34
0.34
0.38
0.38
0.38
0.38
0.39
0.41
0.43
0.46
0.47
0.48
0.49
0.50
0.50
0.54
0.54
0.58
0.59
0.63
0.74
0.79
0.43

Average dNote. d ⫽ inverse variance weighted effect size; COPS-R ⫽ Career Occupational Preference System Interest Inventory—Revised; VII-R ⫽ Vocational Interest Inventory—Revised; CAI-E ⫽ Career
Assessment Inventory—Enhanced Version; UNIACT-R ⫽ Unisex Edition of ACT Interest Inventory—Revised Edition; O*NET IP ⫽ O*NET Interest Profiler; OVIS-II ⫽ Ohio Vocational Interest
Survey—Second Edition; JVIS ⫽ Jackson Vocational Interest Survey; KCS ⫽ Kuder Career Search with Person Match; KOIS ⫽ Kuder Occupational Interest Survey; WOWI ⫽ World of Work
Inventory; VPI ⫽ Vocational Preference Inventory; SII ⫽ Strong Interest Inventory; CII ⫽ Career Interest Inventory; COPS ⫽ Career Occupational Preference System Interest Inventory; CDM ⫽Harrington-O’Shea Career Decision-Making System (R ⫽ Revised Version); VRII ⫽ Vocational Research Interest Inventory; CCQ ⫽ Chronicle Career Quest; CISS ⫽ Campbell Interest and Skill
Survey; CAI-V ⫽ Career Assessment Inventory—Vocational Version; CDI ⫽ Career Decision Inventory; KGIS ⫽ Kuder General Interest Survey; IDEAS ⫽ Interest Determination, Exploration andAssessment System; ASVAB ⫽ Armed Services Vocational Aptitude Battery; SDS-E ⫽ Self Directed Search (Form E); SDS-R ⫽ Self-Directed Search (Form R); OASIS-3: IS ⫽ Occupational Aptitude
Survey and Interest Schedule—Third Edition: Interest Schedule. Boldface represents highly regarded interest inventories.

200
748
450
6,492
529
9,280
1,750
1,663
1,583
78,564354
1,125
30,003
8,944
16,201
979
627
420
661
3,442
750
1,169
6,116
2,028
797
945
313
2,962
551
179,646

400
1,562
900
13,443
1,123
19,272
3,500
3,619
3,214
169,436732
2,250
60,11917,85632,180
1,961
1,381
966
1,311
5,241
1,500
2,320
13,007
4,661
1,536
1,958
717
6,399
1,091
373,655

COPS-R
VII-R
CAI-E
UNIACT-R
OⴱNET IPOVIS-IIJVISKCSKOIS (Form DD)
WOWI
VPI
SII
CII–Level 2
COPS
CII–Level 1
CDM-R
VRII
CDM–Spanish
CCQ–Form L
CISS
CAI-V
CDI
KGIS (Form E)
IDEAS
CCQ–Form S
ASVAB
SDS-E
SDS-R
OASIS-3: IS
Mean effect size
Mean effect size of highly
regarded interest
inventories

Male

N

Inventory

Sample size

Table 4Weighted Mean Effect Sizes for the Work–Task Dimensions and RIASEC Interests for the Latest Edition of an Inventory by Magnitude of Average Effect Size

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872
SU, ROUNDS, AND ARMSTRONG

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

Figure 2. Effect size of interest inventories plotted by Data–Ideas andThings–People dimensions: a ⫽ Armed Services Vocational Aptitude
Battery; b1 ⫽ Career Assessment Inventory—Enhanced Version; b2 ⫽Career Assessment Inventory—Vocational Version; c1 ⫽ Career Interest
Inventory—Level 1; c2 ⫽ Career Interest Inventory—Level 2; d ⫽ Career
Decision Inventory; e1 ⫽ Harrington–O’Shea Career Decision-Making
System—Revised; e2 ⫽ Harrington–O’Shea Career Decision-Making System—Spanish; f1 ⫽ Chronicle Career Quest—Form L; f2 ⫽ Chronicle
Career Quest—Form S; g ⫽ Campbell Interest and Skill Survey; h1 ⫽Career Occupational Preference System Interest Inventory; h2 ⫽ Career
Occupational Preference System Interest Inventory—Revised; i ⫽ Interest
Determination, Exploration and Assessment System; j ⫽ Jackson Vocational Interest Survey; k ⫽ Kuder General Interest Survey; l ⫽ Kuder
Occupational Interest Survey; m ⫽ Kuder Career Search with Person
Match; n ⫽ Occupational Aptitude Survey and Interest Schedule–Third
Edition: Interest Schedule; o ⫽ O*NET Interest Profiler; p ⫽ Ohio Vocational Interest Survey–II; q1 ⫽ Self-Directed Search (Form E); q2 ⫽ Self
Directed Search (Form R); r ⫽ Strong Interest Inventory; s ⫽ Unisex
Edition of ACT Interest Inventory–Revised; t ⫽ Vocational Interest Inventory; u ⫽ Vocational Preference Inventory; v ⫽ Vocational ResearchInterest Inventory; w ⫽ World of Work Inventory.

Effect sizes from highly regarded interest inventories displayed avery similar pattern to the overall meta-analytic results. The
present study also showed considerable variability in the effectsizes of sex differences across inventories. Item development stoodout as a crucial moderator. Weaker moderating effects were foundfor age and cohort for several interest types. These results indicate
the important role of interests in gendered occupational choices
and gender disparity in the STEM fields and have implications for
how future interest measures are developed and used in applied
settings.

Sex Differences in RIASEC and STEM Interests
Our study revealed substantial sex differences in vocational
interests that parallel the composition of males and females in
educational programs and occupations. Men and women differedby almost a full standard deviation in the Things–People dimen-

873

sion. This mean difference of 0.93 indicates that only 46.9% of themale and female distributions of interest on the Things–People
dimension overlaps or that up to 82.4% of male respondents havestronger interests in things-oriented careers than an average female. Men have stronger Realistic, Investigative, and STEM interests, and women have stronger Artistic and Social interests thatparallel the Things–People sex difference. These differences werelarge, with the mean effect size of .84 for Realistic interests and1.11 for engineering interests, equal to a 50.9% and 40.7% overlapof male and female distributions, respectively. The mean effectsize for Social interest (d ⫽ ⫺0.68) was moderate, equal to a
58.4% overlap of distributions. In other words, only 13.3% offemale respondents were more interested in engineering than an
average man, whereas 74.9% of female respondents showed stronger Social interests than an average man. These findings echo
Thorndike’s (1911) statement that the greatest differences between
men and women are in the relative strength of the interest in
working with things (stronger in men) and the interest in working
with people (stronger in women).
Vocational interests seem to be an exception to the findings thatsex differences are small to nonexistent on most psychologicalvariables (Hyde, 2005). These large sex differences along theThings–People dimension need to be taken into account in understanding men’s and women’s educational and occupational
choices. The effect size found in the present study for the Investigative scale (d ⫽ .26), as well as for science (d ⫽ .36) andmathematics (d ⫽ .34) basic interest scales, were within the small
to moderate range according to Cohen’s (1988) benchmarks. Theoretically, the small effect size shows that distribution of men’s
and women’s interests in science and mathematics mostly overlap
with each other (81.9% for Investigative, 75.0% for science, and76.2% for mathematics). Although these effects sizes may appear
to be less important than the Things–People sex difference, inapplied settings an effect size of .30 may have considerable effects
on an individual’s career decisions.
When individuals make educational or occupational choices,
they tend to compare their interest in a certain area both with otherpeople’s interest in this area and with their own interests in otherareas. Thus, when comparing results across individuals, or fromthe inter-individual comparison perspective, only people in theupper tail of the STEM interest distribution, who have strong
interest in this field relative to the larger population, may choose
to enter these areas. That is, individuals who have high interest in
the STEM fields relative to others are the individuals most likely
to consider careers in the STEM fields. It is in the upper tail of the
STEM interest distribution where female and male distributions
overlap less. Therefore, from the inter-individual perspective,
the individuals who pursue STEM careers are more likely to bemale than female. For example, assuming that individuals within
the highest 25% of a population interest distribution are likely tomake occupational choices consistent with an interest type, thenumber of women entering the engineering occupation, then, isonly 19.5% of the number of men entering the field. This percentage is very similar to the actual female–male ratio of individuals
employed in engineering. In science and mathematics interest
distributions, the female–male ratios in the upper 25% asymptote
are 0.60 and 0.64, respectively. However, the actual female–maleratio of individuals employed in the field of physical sciences isonly about 0.40 and, in mathematics, it is about 0.45. This dis-This document is copyrighted by the American Psychological Association or one of its allied publishers.
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SU, ROUNDS, AND ARMSTRONG

874

Table 5Weighted Effect Sizes and Moderator Analysis on Inventory Variables by Interest
Interest/Moderator valuekMean dSE

95% CI

QW␤

R2⫺0.42ⴱⴱ

21.84%

⫺0.15

0.01%

⫺0.46ⴱⴱ

26.59%⫺0.37ⴱ

13.32%

0.20

0.18%

0.15

5.97%

⫺0.47ⴱⴱ

13.68%

⫺0.12

0.20%

⫺0.32ⴱ

12.61%⫺0.43ⴱ

20.19%

⫺0.20

6.57%

⫺0.37ⴱⴱ

17.29%

⫺0.12

5.59%

Item development
Things–People dimension
1
2
3
Data–Ideas dimension
1
2
3
Realistic
1
2
3
Investigative
1
2
3
Artistic
1
2
3Social123Enterprising
1
2
3
Conventional
1
2
3
Average RIASEC d
1
2
3
Science123Mathematics
1
2
3
Engineering
1
2
3

31
10
4

0.99
0.85
0.44

0.046
0.082
0.130

0.90
0.69
0.19

1.08
1.01
0.70

33.63
14.48
2.10

31
10
4

⫺0.08
⫺0.14
⫺0.03

0.042
0.074
0.117

⫺0.16
⫺0.28
⫺0.26

0.00
0.01
0.19

48.14ⴱ
15.28
0.46

32
10
4

0.91
0.74
0.35

0.033
0.058
0.093

0.85
0.62
0.17

0.98
0.85
0.53

79.77ⴱⴱ
8.95
1.93

31
10
4

0.29
0.11
0.05

0.042
0.074
0.118

0.21
⫺0.04
⫺0.18

0.38
0.25
0.28

38.55
5.19
0.54

32
10
4

⫺0.35
⫺0.47
⫺0.20

0.030
0.054
0.086

⫺0.41
⫺0.58
⫺0.37

⫺0.29
⫺0.36
⫺0.03

40.59
15.69
0.78

32
10
4

⫺0.67
⫺0.68
⫺0.36

0.046
0.082
0.130

⫺0.76
⫺0.84
⫺0.61

⫺0.58
⫺0.52
⫺0.10

27.96
17.41ⴱ
2.73

31
10
4

0.09
⫺0.11
⫺0.07

0.035
0.063
0.100

0.02
⫺0.24
⫺0.27

0.16
0.01
0.12

24.99
12.70
2.49

32
10
4

⫺0.29
⫺0.52
⫺0.15

0.052
0.092
0.147

⫺0.39
⫺0.70
⫺0.44

⫺0.19
⫺0.34
0.14

48.35ⴱ
22.21ⴱⴱ
0.43

32
10
4

0.46
0.47
0.23

0.020
0.035
0.056

0.42
0.40
0.12

0.50
0.54
0.34

32.74
19.43ⴱ
1.74

25
6
3

0.43
0.29
⫺0.05

0.056
0.114
0.165

0.32
0.07
⫺0.38

0.54
0.52
0.27

24.78
4.52
0.27

24
6

0.36
0.24

0.035
0.070

0.30
0.11

0.43
0.38

25.34
8.24

32
10
3

1.18
1.01
0.58

0.055
0.104
0.184

1.07
0.80
0.21

1.29
1.21
0.94

41.91
3.14
2.41

Scoring
Things–People dimension1
2
Data–Ideas dimension
1
2
Realistic
1
2
Investigative
1
2

32
13

0.96
0.79

0.046
0.071

0.87
0.65

1.05
0.93

46.06ⴱ
16.41

32
13

⫺0.15
0.06

0.041
0.064

⫺0.23
⫺0.06

⫺0.07
0.19

46.58ⴱ
10.39

33
13

0.88
0.69

0.034
0.053

0.82
0.59

0.95
0.79

81.61ⴱⴱ
24.74ⴱ

32
13

0.25
0.18

0.043
0.067

0.17
0.05

0.33
0.31

43.22
4.21

0.38ⴱⴱ

12.33%⫺0.19ⴱ

8.20%

0.02

1.56%
(table continues)

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

875

Table 5 (continued)
Interest/Moderator value
Artistic
1
2
Social12Enterprising
1
2
Conventional
1
2
Average RIASEC d
1
2
Science12Mathematics
1
2
Engineering
12kMean dSE

95% CI

QW

33
13

⫺0.32
⫺0.47

0.028
0.043

⫺0.38
⫺0.56

⫺0.27
⫺0.39

41.98
23.93ⴱ

33
13

⫺0.70
⫺0.52

0.044
0.070

⫺0.79
⫺0.65

⫺0.61
⫺0.38

29.35
21.15ⴱ

32
13

0.01
0.06

0.036
0.055

⫺0.06
⫺0.04

0.08
0.17

33.13
14.26

33
13

⫺0.37
⫺0.23

0.051
0.080

⫺0.47
⫺0.39

⫺0.27
⫺0.07

54.69ⴱⴱ
21.06ⴱ

33
13

0.46
0.40

0.020
0.030

0.42
0.34

0.50
0.46

45.22
19.44

20
14

0.41
0.29

0.067
0.080

0.28
0.14

0.54
0.45

25.97
5.99

18
12

0.44
0.19

0.034
0.043

0.37
0.10

0.50
0.27

17.47
11.99

31
14

1.18
0.94

0.055
0.082

1.07
0.78

1.29
1.10

47.76ⴱ
7.22

␤

R2⫺0.43ⴱⴱ

11.35%

0.25

9.02%

0.27

1.21%

0.17

2.78%

⫺0.08

4.37%

⫺0.11

3.86%

⫺0.63ⴱⴱ

41.73%

⫺0.24

9.89%

⫺0.25ⴱ

4.98%

⫺0.19

6.18%

⫺0.46ⴱⴱ

17.08%

0.02

0.09%

⫺0.08

0.05%

0.02

0.07%

⫺0.12

2.17%

⫺0.25ⴱ

7.29%

0.01

0.20%

Theoretical framework
Things–People dimension0
1
Data–Ideas dimension
0
1
Realistic0
1
Investigative
0
1
Artistic
01Social01Enterprising
0
1
Conventional0
1
Average RIASEC d
0
1

22
23

0.98
0.84

0.054
0.053

0.87
0.74

1.09
0.94

35.27ⴱ
29.80

22
23

⫺0.02
⫺0.15

0.050
0.049

⫺0.11
⫺0.25

0.08
⫺0.06

18.15
41.66ⴱⴱ

22
24

0.96
0.70

0.047
0.045

0.87
0.62

1.05
0.79

47.83ⴱⴱ
26.92

22
23

0.22
0.24

0.052
0.050

0.12
0.14

0.32
0.34

6.91
41.65ⴱⴱ

22
24

⫺0.36
⫺0.37

0.035
0.033

⫺0.43
⫺0.43

⫺0.29
⫺0.30

27.41
43.55ⴱⴱ

22
24

⫺0.64
⫺0.65

0.054
0.052

⫺0.75
⫺0.76

⫺0.53
⫺0.55

30.41
25.13

22
23

0.06
0.00

0.043
0.042

⫺0.02
⫺0.08

0.14
0.08

13.77
32.68

22
24

⫺0.22
⫺0.43

0.061
0.059

⫺0.34
⫺0.54

⫺0.10
⫺0.31

19.11
55.21ⴱⴱ

22
24

0.43
0.45

0.025
0.023

0.39
0.40

0.48
0.49

27.14
38.01ⴱ

Note. k ⫽ number of effect sizes; d ⫽ weighted mean effect sizes; SE ⫽ standard error; CI ⫽ confidence interval; QW ⫽ within-category residual
variability statistic; ␤ ⫽ standardized regression coefficient, controlled for the other inventory variables; R2 ⫽ percentage of variance accounted for. In
the coding for item development, 1 represents an overlap of male and female scores of less than 75% or cases in which more than 33% of the items have
response differences larger than 15%; 2 represents an overlap of male and female scores from 75% to 85% or 10% to 33% of the items have response
differences larger than 15%; 3 represents an overlap of male and female scores larger than 85% or in which no more than 10% of the items have response
differences larger than 15%. For scoring, 1 ⫽ raw score and 2 ⫽ combined-sex norm. Theoretical framework was coded as 0 ⫽ RIASEC model; 1 ⫽ otherinterest models.
ⴱp ⬍ .05. ⴱⴱ p ⬍ .01.

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SU, ROUNDS, AND ARMSTRONG

876

Table 6Moderator Analysis on Sample Variables by Interest
Interest

k

QW

␤AGE

2
RAGE

␤COHORT

2
RCOHORT

2
RTOTALThings–People dimension
Data–Ideas dimension
Realistic
Investigative
Artistic
SocialEnterprising
Conventional
Average RIASEC d
Science
Mathematics
Engineering

67
67
68
67
68
68
67
68
68
29
27
39

88.51ⴱ
108.00ⴱⴱ
131.45ⴱⴱ
75.51
111.26ⴱⴱ
61.38
105.45ⴱⴱ
99.93ⴱⴱ
94.75ⴱⴱ
37.12
29.48
67.54ⴱⴱ

⫺0.24ⴱ
⫺0.03
⫺0.10
0.12
0.20
0.45ⴱⴱ
⫺0.02
0.12⫺0.36ⴱⴱ
⫺0.34⫺0.12⫺0.62ⴱⴱ

3.44%
5.40%
0.02%
1.20%
0.41%
23.81%
6.86%
5.49%
5.00%
5.67%
3.38%
19.26%⫺0.12⫺0.50ⴱⴱ
⫺0.16
0.01
0.58ⴱⴱ
⫺0.06⫺0.54ⴱⴱ
⫺0.20⫺0.29ⴱ
⫺0.17
0.11⫺0.30ⴱ

0.00%
24.00%
1.37%
0.33%
22.70%
8.45%
27.95%
6.79%
1.14%
0.07%
3.23%
0.45%

4.31%
24.14%
2.00%
1.34%
25.65%
23.81%
28.12%
7.94%
10.85%
7.67%
4.13%
25.11%

Note. k ⫽ number of effect sizes; QW ⫽ residual variability statistic; ␤AGE ⫽ standardized regression coefficient for age, controlled for cohort;
22
␤COHORT ⫽ standardized regression coefficient for cohort, controlled for age; RAGE
⫽ percentage of variance accounted for by age alone; RCOHORT⫽
2
percentage of variance accounted for by cohort alone; RTOTAL⫽ percentage of variance accounted for by age and cohort.
ⴱp ⬍ .05. ⴱⴱ p ⬍ .01.

crepancy between interest data and real employment compositionindicates that there may be reasons other than sex differences ininterests that can account for gender disparity in science andmathematics.
In addition to the inter-individual perspective, interest scores fordifferent types of careers can also be examined within an individual to determine the career area with the highest interest. From thisintra-individual comparison perspective, it is expected that individuals will choose the field in which they have the strongest
interest, creating the possibility that individuals with high levels of
STEM interests may still pursue careers in other fields because
they have even greater interests in other areas. These intraindividual differences in interests may also contribute to genderdisparity in the STEM fields, because the STEM areas are often
perceived as a better match for individuals with things-oriented
interests, and our results suggest that women are more likely thanmen to prefer working with people over working with things, andmen are more likely than women to prefer working with things
over working with people. Therefore, although an individual female may have high levels of STEM interests, she may still leave
STEM areas because she has stronger interests in other peopleoriented areas.
The hypothesis that intra-individual effects may be a factor
contributing to women leaving scientific fields is somewhat supported by previous research. For example, in their 3-year interview
study, Seymour and Hewitt (1997) found that perceptions that
non-STEM academic majors offered better education options andbetter matched their interests was the most common (46%) reason
provided by female students for switching majors from STEM
areas to non-STEM areas. The second most frequently cited reason
given for switching to non-STEM areas was a reported loss ofinterest in the women’s chosen STEM majors. Additionally, 38%of female students who remained in STEM majors expressed
concerns that there were other academic areas that might be abetter fit for their interests. Preston’s (2004) survey of 1,688
individuals who had left sciences also showed that 30% of thewomen endorsed “other fields more interesting” as their reason forleaving. On the basis of findings from previous research and thepresent study, we suggest that interest may be a key factor inunderstanding individuals’ occupational choices as well as thegender disparity in the STEM fields.

Issues of Construct Validity

Figure 3.

Funnel plot from trim-and-fill analysis.

Our study showed that the application of some item development strategies can substantially reduce sex differences in vocational interests. This result is not surprising, given that the mostfrequently used procedure for reducing gender disparity was basedon analyzing male and female item response rates and removing
items with large sex differences. However, not all RIASEC interests were impacted in the same way by using this approach.
Obtained results showed that item development moderated only
the effect sizes of Realistic, Investigative, and Enterprising interests and the basic interests of engineering and science, indicatingthat efforts to reduce sex differences in vocational interest inven-

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

tories have been made primarily with scales traditionally favoringmale respondents. In comparison, the reduction of sex differences
has been minimal for the Artistic, Social, and Conventional interests. The implications of using these criteria to develop more
sex-balanced interest measures are double-edged. Implementing
sex-balanced techniques was found to be effective in reducing sex
differences observed in vocational interest measures. Interest inventories applying these techniques with the resulting report ofscale scores encourage women to explore opportunities in nontraditional fields such as engineering and science. Conversely, eliminating items with large differences in response rates between men
and women might result in changes in the construct that aninventory is intended to measure.
Inventory authors have taken different perspectives regarding
eliminating items that show sex differences. For example, in thedevelopment of the Career Occupational Preference System Interest Inventory—Revised Edition, R. R. Knapp and Knapp (1979)
removed all items with a response difference larger than 15%. This
strategy led to an effect size of .15 for Realistic interests (averaged
from Technology–Professional, Technology–Skilled, and Outdoor
scale scores). In contrast, about one third of the items in the Strong
Interest Inventory had response differences of 16% or larger, yetthese items were retained through revisions (see Hansen & Campbell, 1985; Harmon et al., 1994). As a result, the Strong Interest
Inventory (Campbell, 1974; Donnay et al., 2005; Hansen & Campbell, 1985; Harmon et al., 1994) has an average effect size of .75
for Realistic interests, and the most recent edition of the measure(Donnay et al., 2005) has an effect size of 1.16 on the Realistic
scale. Such a large discrepancy between inventories is of concern:
If an inventory shows a trivial sex difference for Realistic interest,
is it still measuring the Realistic construct? Do the inventory
results necessarily reflect individuals’ expressed interests? Should
inventory respondents be informed that their interest scores could
vary depending on which inventory they take? Do individuals havethe right to know how their suggested career options may be
influenced by different strategies in item development? Researchers and interest inventory authors need to be aware of theseimportant issues of construct validity when using sex-balanced
techniques in item development.
Because inventory authors have taken different perspectives on
test validation and have used different strategies for item development, the constructs measured by scales with the same namefrom different inventories may not converge, and individuals’
interest scores from different inventories may vary. Several studies
have provided evidence for unsatisfactory agreement among interest inventories (e.g., Russell, 2007; Savickas, Taber, & Spokane,2002). For example, Russell (2007) showed that the hit rate in the
cross-classification of Holland RIASEC codes between the SelfDirected Search (Holland et al., 1994) and the UNIACT-R (American College Testing Program, 1995) is only 50.16%. That meansthat if an individual takes the Self-Directed Search and the
UNIACT-R, his or her chance of being identified as two different
Holland interest types is almost as large as that of being assigned
the same type, and he or she is very likely to receive different
suggestions for occupational options. As noted previously, theSelf-Directed Search and the UNIACT were developed on thebasis of two different approaches to test validation (G. D. Gottfredson & Holland, 1978; Prediger & Cole, 1975). It appears thatthe attempt to remove sex differences from interest scores in the877

UNIACT-R has resulted in scales that do not mirror the original
RIASEC constructs but instead measure a narrower range ofinterests than the construct domains because of the removal of
items with large sex differences.
It is important that information about the construct validity of an
inventory and the procedure, justification, and potential impact of
the test validation approach used by an inventory be available totest takers. Individuals can then use such information to makechoices about interest inventories and to assist in interpreting theirown interest scores. The Standards for Educational and Psychological Testing (American Educational Research Association et al.,1999) has recommended that test developers clearly describe andjustify the construct the test is intended to measure and document
the extent to which the content domain of a test represents thedefined construct. With regard to the relationship between validity
and test consequences (e.g., sex differences and other subgroupdifferences), the standards state the importance of distinguishing
between issues of validity and issues of social policy and clarify
that unequal outcomes at the group level do not necessarily havedirect bearing on questions of test fairness; however, the standards
also recommend using a testing alternative that minimizes outcomes differences across relevant subgroups with “other things
being equal.”Multidimensionality of RIASEC Interests
The present study showed that inventories using interest models
different from Holland’s RIASEC model had smaller sex differences in Realistic interests. The primary difference between otherinterest frameworks and the RIASEC model is the number ofinterest categories. Interest frameworks other than the RIASEC
model include multiple interest categories or basic interest scales
that can be indirectly mapped onto the RIASEC types. The content
of these interest categories is usually fairly homogeneous compared with the broad RIASEC types. For example, the Kuder
Occupational Interest Survey (F. Kuder & Zytowski, 1991) Vocational Interest Estimation contains two scales that were classified
into the Realistic type: Outdoor and Mechanical. For the Mechanical scale, the effect size was 1.14; the Outdoor Scale, however,
had a much smaller effect size of 0.18. When averaged, it produceda smaller effect size than that typically found in inventories using
the RIASEC model because mechanical and technical items areusually more heavily weighted than outdoor items in Realistic
scales. The range of constructs covered by multiple interest categories that are classified into a single RIASEC scale could beunderstood as narrower facets within a broad interest type.
The concept of facet has been widely adopted in personality psychology to refer to subdomains of the Big Five personality
traits. Studies have shown that, compared with broad personality
factors, personality facets are more useful for understanding subgroup differences and for predicting certain behaviors (e.g.,
Connor-Smith & Flachsbart, 2007; Foldes, Duehr, & Ones, 2008;
Kamp & Hough, 1986; Mount & Barrick, 1995). Similar to thebroad personality factors in the five-factor model, Holland’s(1997) RIASEC interest types are multidimensional (e.g., Einarsdóttir & Rounds, 2009; Fouad & Walker, 2005). Each RIASEC
type is likely to include several facets that cover different parts of
the whole content domain, and these facets can reveal substantial
group differences that are not visible at the scale level (Fouad &This document is copyrighted by the American Psychological Association or one of its allied publishers.
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878

SU, ROUNDS, AND ARMSTRONG

Walker, 2005). Therefore, examining sex differences at the facet
level may clarify the extent to which men and women differ ininterests. For example, outdoor and mechanic are two facets ofRealistic interests that are different in nature and show very
different effect sizes. Examining sex differences at the facet level
revealed the extreme difference in mechanic that is covered up
when combined with the outdoor items. This facet perspective
provides a further explanation for the moderating effect of item
development. When items with large response differences aredeleted from interest inventories to reduce sex difference, they are
more likely to be mechanic items than outdoor items. This strategy
could lead to a much smaller effect size for the Realistic scale.
However, if most of the Mechanic items are taken out, the remaining items actually measure the outdoor facet instead of the fullrange of interests in the Realistic type that encompasses both
facets.
From another perspective, examining interests at the facet levelcan provide finer differentiation of the interest profile of an individual or a group, a practice suggested for interpreting the Strong
Interest Inventory (Donnay et al., 2005). For example, when awoman receives a relatively low score on the Realistic scale, itdoes not mean that she does not have any of the Realistic interests.
It is possible that she is highly interested in outdoor activities andoccupations. A closer look at the facets of Realistic interests could
help more accurately identify her vocational interests and reveal a
broader range of options for career exploration. Therefore, additional research is needed at the facet level of interests to examine
the potential utility of these measures for identifying career
choices while reducing the impact of sex differences observed withbroad RIASEC measures of interest.
Although theoretical framework of the interest measure was nota significant moderator for the Investigative type, it is worth notingthat the effect size for Investigative interests was significantly
smaller than that for Science and Mathematics. This result implies
that the Investigative type also encompasses diverse facets, including some facets with greater sex differences favoring men, such as
physical sciences and mathematics, as well as others with smaller
sex differences or sex differences favoring women, such as biological/medical sciences and social sciences. Given women’s preference for people-oriented careers over things-oriented careers,
women who pursue scientific careers tend to gravitate toward
fields that allow for more opportunities to work with people, suchas the biological and medical sciences, psychology, and othersocial sciences (Lubinski & Benbow, 2007). Therefore, the effectsize for the Investigative type could favor men or women depending on how the items are weighted. For example, the Vocational
Interest Inventory (Lunneborg, 1981) has an effect size of ⫺.17 for
the Science scale, with women scoring higher. The negative effectsize is not surprising given that many physical sciences items were
replaced by social, medical, and biological sciences items in aneffort to reduce gender disparity when constructing the instrument.
The present study examined only the facets of Realistic andInvestigative interests that are related to the STEM fields. Interest
types other than Realistic and Investigative could be multidimensional as well. For example, G. F. Kuder (1977) believed that
Literacy and Musical interests are different from the Artistic
interests and include all three scales in the Kuder Occupational
Interest Survey (F. Kuder & Zytowski, 1991) instead of a general
Artistic theme. These three areas could be facets of Artistic inter-ests. Future research on the multidimensionality of interests andsex differences at the facet level will greatly advance understanding of vocational interests and will increase the accuracy of test
interpretation and career guidance for individuals.

Age Cohort Change in Sex Differences
Interest is shaped by environmental factors such as family, theschool, and other aspects of the culture (Eccles, 1993). For example, Helwig (1998) showed that children’s choices of occupations
are influenced by parents’ expectations and societal values. Bandura, Barbaranelli, Caprara, and Pastorelli (2001) showed that bycontrolling the type of activities to which their children are exposed, parents shape children’s interests. Educational opportunities for female students also increase their nontraditional vocational interests (Betz & Schifano, 2000). Nevertheless, previous
research has revealed that interests are fairly stable (Campbell,
1971; Hansen, 1984; Swanson, 1999; Tracey & Sodano, 2008).
Several longitudinal studies showed that interests have significant
covariation over Grades 8 through 12 and tend to crystallize overtime (e.g., Tracey & Robbins, 2005; Tracey, Robbins, & Hofsess,2005). This pattern was found to be similar across all ethnic groups
and for males and females. Recently, Low, Yoon, Roberts, andRounds (2005) conducted a meta-analysis examining the stabilityof interests from early adolescence to middle adulthood. Theseauthors found that vocational interests remained reasonably stablefrom age 12 to age 40 for both males and females (␳ ⫽ .55–.83).
The stability of interests had a major increase by the end of highschool and peaked in college years, earlier than was previously
believed to occur. Low et al. (2005) also showed that interests are
more stable than personality traits. These results suggest that it isimportant to attend to both change and stability of interests.
The present study, covering interest inventories from the 1960s
to the present and generations born in the 1930s to the 1980s, again
showed consistency of sex differences in interests, with a few
interest types being subject to the influence of age. The age effectwas generally small. The only noteworthy impact of age on vocational interests was that for older samples, sex differences were
smaller for Social interests and engineering interests, which contributes overall to a smaller sex difference in the Things–People
dimension, although the general tendency of men to prefer working with things and women to favor working with people never
becomes trivial in the older samples. Readers need to be cautious
when interpreting the results of these changes in interests because
they are cross-sectional. However, because we conducted moderator analysis on age with cohort effect controlled for, we haveeliminated the alternative explanation that these changes happened
because of sociocultural shift and, thus, we can have more confidence that these changes can be attributed to age differences.
With regard to cohort effect for interests, past research has
demonstrated considerable stability of interests over time. For
example, Hansen (1988) examined the Strong Vocational Interest
Blank scores for general samples of women and men from the
1930s to the 1980s and illustrated tenacious stability of occupational interests for both women and men and “resilience” of sex
differences over time. Using cohort as a moderator, the present
study again showed that interests generally have been very stable
over the past four decades. Three exceptions were notable:
Younger cohorts had smaller sex differences in Artistic interests

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SEX DIFFERENCES IN INTERESTS: META-ANALYSIS

and engineering interests, and effect sizes of sex differences inEnterprising interests actually changed from positive (stronger inmen) to nonsignificant over time. Betz and Fitzgerald (1987)
identified a typically found sex difference favoring men for Enterprising scales. Developing meta-analytic evidence appears to nolonger support this observation. The decrease of sex differences inEnterprising interests over time may be explained by the concomitant people element of Enterprising interests with the significant
increase in women’s opportunities in Enterprising occupations.
Twenge’s (2001) cross-temporal meta-analysis has shown thatwomen’s assertiveness or dominance, a personality trait that isclosely related to Enterprising interests, has been increasing
steadily since the late 1960s and is predicted by social statistics,
such as women’s educational attainment, labor-force participation,and median age at first marriage. These results from past studies
and the present study indicate that the changes in working environment and women’s status and roles may have helped theexpression of women’s Enterprising interests. In comparison, Realistic and Investigative interests (including more specific STEM
interests in science and mathematics) were relatively stable to age
and cohort and were not affected by these sample variables.

Practical and Policy Implications
Findings from the present study on sex differences of interests
not only constitute important knowledge in their own right but also
hold implications for assessment policy and practice. Because
RIASEC interests are multidimensional, it is important for inventory developers to select items proportionally from all facets of one
interest type to reflect the breadth of the defined construct domain.
Attention to social consequences of interest assessment and theapplication of technologies to remove sex differences from interest
inventory should be considered under the overarching issue of
construct validity (Messick, 1989).
As suggested by previous literature and the present study, interests stabilize at an early age. Given the essential role of family
and other environmental influences in the socialization process ofchildren and their interest development, it may be important forparents, educators, and counselors to be involved early on, wheninterests seem more malleable. Efforts to increase girls’ interests in
the STEM fields to bridge the gender gap may also need to be
initiated at formative years when children are developing genderroles and perceptions of appropriate careers.
The sex difference information provided for inventories that are
currently in print can be used to guide selection of inventories.
Practitioners and administrators who wish to choose interest inventories showing small sex differences or who wish to encourage
female students to explore nontraditional areas, including the
STEM fields may, consider inventories such as the Career Assessment Inventory—Enhanced Version (Johansson, 2003) and the
UNIACT-R (American College Testing Program, 1995). The
present study also suggests that caution needs to be exercised ininterpretation of inventory results: Both sex differences in vocational interests and individual differences within the same sex
should be taken into consideration to assist individuals in making
the most suitable career decisions.

879

Limitations and Future Directions
In the present study, we examined the magnitude and pattern ofsex differences in vocational interests. It is important to note thatgroup differences do not speak for every individual within a group,
and it is not the purpose of the present study to reify the stereotypes that men and women are interested in their “traditional”
areas. The present study provided evidence that intragroup differences were substantially larger than intergroup differences.
Although it is beyond the scope of the present study to provide
a detailed exploration of the environmental, social, and biologicalfactors that have contributed to the development of these sex
differences, the findings presented here highlight the importance ofunderstanding how sex differences in interests develop. Interested
readers are referred to reviews such as those by Watt and Eccles(2008), Ceci and Williams (2007), and Ceci et al. (2009) for a
discussion on the biological, psychological, and social processes
and gendered vocational interests or career choices. It is crucial for
researchers, practitioners, and policy makers to better understandthe course of vocational interest development and the factors
contributing to individual differences in interests within the samesex. Why do some women become more interested in the STEM
fields than others? Which stage in the developmental process iscritical for the development of science and engineering interests?
What factors may thwart or promote the development of scienceand engineering interests? To the extent that future research cananswer these questions, it becomes possible to provide appropriate
intervention for increasing girls’ interest in the STEM areas or
attracting women to work in the STEM fields.
Another limitation of the present study is that we did not
examine possible variation in the sex differences between different
racial/ethnic groups. Few interest inventories report results separately for racial/ethnic groups by sex. Available information about
any specific racial/ethnic group was too limited to warrant amoderator analysis. Future research conducted with samples from
specific racial/ethnic groups and other cultures can further contribute to the scientific knowledge about sex differences in interests and may promote understanding about how these sex differences are developed from a cultural perspective.
Additionally, because of the nature of the available database, thecell sizes for our moderator analysis on item development were
unequal. Many more inventories that have undergone minimal orslight item selection and revision were included than inventories
that have had major item revisions to reduce sex differences.
Unequal cell sizes may render the significance testing results frommoderator analysis on item development nonrobust. When reading
the results, readers should note this fact and attend to the actual
magnitude of effect sizes and their trend of change.
Although we have identified a number of important moderatorsin the assessment process that influence the magnitude of sex
differences, in future studies, researchers may also wish to consider additional moderators for sex differences in vocational interests. Although moderators in the present study accounted for a fairamount of variance in the effect sizes, there was still nontrivial
variance left unexplained. In particular, the two sample variables,
age and cohort, explained only a limited proportion of the variance. Given the large individual differences within the same sex, it
is important to examine other variables (e.g., education, genderrole identity) that influence the development and level of interests.

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SU, ROUNDS, AND ARMSTRONG

880
Summary and Conclusions

The present study makes several important contributions to theliterature. First, it is the first comprehensive meta-analysis on sex
differences in vocational interests. We synthesized evidence frominterest inventories over four decades and found large sex differences in vocational interests, with men preferring working withthings and women preferring working with people. These sex
differences are remarkably consistent across age and over time,
providing an exception to the generalization that only small sex
differences exist. Second, this study provides a systematic reviewof the sex differences in the STEM interests that has not previously
appeared in the literature. The pattern of sex differences in the
STEM interests revealed by the present study closely resembles
the composition of men and women in corresponding occupations
and contributes to the understanding of the gender disparity in the
STEM fields. The results suggest that the relatively low numbersof women in some fields of science and engineering may result
from women’s preference for people-oriented careers over thingsoriented careers.
Finally, we found that effect size of sex differences varied
widely among interest inventories. The sex differences were foundto be moderated by the item development process, suggesting thatinterest inventories can be designed either to reduce or to magnify
the estimates of the magnitude of these sex differences in interests.
Educators and counselors need to be aware of which inventories
produce the largest and smallest differences, given the crucial roleof vocational interests in people’s career development and thewide use of interest inventories in helping people identify theirinterests and choose their careers. Educators and counselors alsoneed to be careful in choosing assessment tools and in interpreting
the results of such measures so as not to restrict the occupational
choice of individuals—for both men and women.

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Received February 10, 2009
Revision received June 19, 2009
Accepted August 6, 2009 䡲

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