Who I Am and What I Want to Do: Linking Self-Construals and Career Interests

Abstract

Two studies examined how four self-construals (independent, interdependent, relational, and physical) predict career interests. We hypothesized that (1) independent self-construal would predict career interests in science, law, and art, (2) interdependent self-construal would predict career interests in business and human relations, (3) relational self-construal would predict career interests in family and social services, and (4) physical self-construal would predict career interests in manual labor, trade skills, and athletics. In Study 1 (n = 240), participants completed online surveys of the four self-construals and the Basic Interest Markers (BIMs). The results showed that the four self-construals predicted corresponding career interests while controlling for sex. Physical self-construal predicted corresponding career interests the most consistently. Study 2 (n = 149) included a Holland Code survey rather than the BIM. Results showed that independent, interdependent, and physical self-construals predicted corresponding Holland themes while controlling for sex. Implications for Super’s career development approach are discussed.

Keywords

self-construal physical relational independent interdependent career interests Holland Code

Introduction

In 2023, the U.S. Bureau of Labor Statistics (BLS) reported that employment totaled 8.7 million jobs on the last business day of October, but 3.6 million had also quit their job (Bureau of Labor Statistics, 2023). Many people struggle to find a well-suited career for themselves, but it is difficult for them to anticipate which careers will fit them better than others. Historically, division of labor was predetermined by family trade (Bui & Miller, 2017) and by one’s sex assigned at birth (Marlowe, 2007), but with advancements in technology and in social movements, simple demographic indicators are no longer sufficient to explain career aspirations. Consequently, there is a growing need to identify psychological factors that predict career interests. We propose that the way people define themselves (i.e., self-construal) is one of these important factors, but to date has been overlooked. The purpose of our two studies was to examine how variations in self-construal predict interests in corresponding career areas while accounting for a person’s sex assigned at birth.

Sex Differences in Career Interests

Sex assigned at birth is distinct from gender identity in that sex assigned at birth is often based on physical characteristics and hormones, whereas gender identity is a psychological process of identifying as male, female, nonbinary, or other variations. Although many people’s gender identity and sex assigned at birth are the same, there are also many whose gender identity differs from their sex assigned at birth. The focus of our research is on sex assigned at birth.

Differences in career and vocational interests have long been observed based on people’s sex assigned at birth (Thorndike, 1911), and many are still apparent today. This is not to say that they are as strikingly different as they once were. From 1990 to 2010, there were significant reductions in these differences (Gati & Perez, 2014), and there are usually more similarities between men and women in career interests than there are differences (Hyde, 2005). There have also been notably larger increases of women in particular job sectors, such as the legal profession (Whitmarsh & Wentworth, 2012), than before. Despite these strides toward equality, however, several differences remain.

One of the primary differences in men’s and women’s interests reflects careers that are centered on People or Things (Morris, 2016). Specifically, women prefer careers that involve working with people, whereas men prefer careers that involve working with objects and numbers (Lippa, 1998; see Su et al., 2009 for a meta-analysis). These orientations in turn predict which major students declare in college (Coyle, 2020; Graziano et al., 2012), their retention in the major (Woodcock et al., 2013), and their later career performance (Wee et al., 2021). The binary definition of sex that pervades most of the literature, however, does not allow for more extensive analysis across gender and gender identity. Similarly, the People-Things distinction should not be considered bipolar, and there are several instances where men and women may be drawn to contra-sex role careers (Svedholm-Hakkinen et al., 2018) or careers that involve both elements (Tay et al., 2011). These contra-sex role career pursuits may be influenced by hormonal patterns. For example, females who have experienced high prenatal androgen exposure are more likely to pursue careers that involve things and data than their counterparts (Beltz et al., 2011). This suggests that noted differences in career pursuits may be due to underlying differences other than sex assigned at birth, involving factors that are nonbinary.

We propose that assessing key individual difference factors that are linked to career interests may serve as a more fruitful approach to understanding these noted sex differences. Past research has already identified the link between personality traits and vocational interests, and their mutual influence on each other (Hoff et al., 2020; Wille & De Fruyt, 2014). For example, the General Factor of Personality (van der Linden et al., 2021) and subscales of the Big Five traits (Wiernik, 2016) are associated with career interests involving People and Things. Other approaches employ social cognitive models to explain sex differences, such as distinguishing between instrumentality/expressiveness (Ludwikowski et al., 2018) or masculinity/femininity (Weisgram et al., 2011) to predict career interests.

There have been calls for incorporating the most recent work in social and personality psychology on the self into career counseling (e.g., Blustein & Noumair, 1996; Hartung & Subich, 2011), but that has not yet occurred. We suggest that there is an overlooked set of individual differences that have already been utilized to explain the underlying trends in sex differences, but these factors have not yet been applied to career interests. We propose that individual differences in the self-construal may serve as an explanatory model for sex differences in career interests. The following section outlines how variations in self-construal provide unique utility in explaining these trends.

The Role of Self-Construal

The way people define themselves (i.e., their self-construal) is foundational in understanding their behavior and decision processes. The seminal work by Markus and Kitayama (1991) proposed that people will draw upon specific social experiences to develop a self-construal that is highly independent (or private) or interdependent (or public/collective), and a person’s culture serves as a guide for how those experiences shape the self. Consequently, people will form either a highly independent self-construal or a highly interdependent self-construal based on whether their culture places either an individualized or group emphasis of social experiences.

These self-construals in turn influence the ways people respond to their social environments (see Cross et al., 2011; Yan & Hong, 2007 for reviews). For example, people with highly independent self-construal are less sensitive to different social contexts (Cousins, 1989; Kanagawa et al., 2001; Rhee et al., 1995; Suh, 2002), engage in more contrast-based cognitive processing (Bry et al., 2008), emphasize self-consistency (Church et al., 2008), prefer socially disengaging emotions (Kitayama et al., 2004), engage in more self-promotion (see Heine, Lehman, Markus, & Kitayama, 1991 for a review), and emphasize primary control more (Lam & Zane, 2004) than people with a highly interdependent self-construal.

These differences in turn predict how people behave. For example, highly interdependent people engage in more group-oriented (Gardner et al., 2004), cooperative (Chen et al., 2001), and imitative behaviors (van Baaren et al., 2003) than highly independent people. These differences in self-construal may therefore attract people to career areas that allow these cognitive, emotional, motivational, and behavioral elements to thrive.

The interdependent self-construal may be further distinguished as having relational and collective subcategories, which provide additional insights into gender differences. A collective-interdependent version involves defining the self as being inextricably linked to one’s ingroups, whereas a relational-interdependent self-construal (we will use relational self-construal from this point forward) involves defining the self in terms of one’s close relationships (Cross et al., 2000; Kashima et al., 1995). Cross and Madson (1997) were the first to suggest that relational self-construal may be used to explain several gender differences in attitudes and behaviors, especially within Western cultures. Rather than focusing on larger groups, people with a highly relational self-construal define themselves and structure their social experiences around individual relationships (e.g., with one’s spouse, friends, and siblings). As a result, highly relational people are more inclined than others to be sensitive to relational information (Cross et al., 2002), base their well-being on the quality of close relationships (Cross et al., 2002), focus on other-enhancement (Gore & Cross, 2011), pursue goals for the sake of their relationships (Gore & Cross, 2006; Jiang et al., 2017), and engage in prosocial social media use (Ferenczi et al., 2017). Their behavior in turn focuses on promoting their relationships, adjusting to the needs of their relationships, and focusing on joint gains.

Although women often score higher than men on measures of relational self-construal (Wills & Petrakis, 2019), relational self-construal predicts feminine gender roles over and above biological sex (Gore & Dean, 2024). This may also be the case with career interests. As was the case with the other self-construals, relational self-construal may predict career areas where the emphasis is on the development, nurturing, and enhancement of relationships. It may also be particularly helpful in predicting interest levels in careers that women are frequently drawn to but also allow for predictions of career pursuits for highly relational men.

Recent work has identified another self-construal, physical self-construal, which may explain why men are drawn to particular careers. Physical self-construal is the tendency to define the self based on one’s physical body and its capabilities (Gore & Dean, 2024). Similar to Cross and Madson’s (1997) seminal work on utilizing self-construal to explain gender differences, the development of physicality as an identified basis of self-construal also stemmed from the extensive literature on gender differences (e.g., Ellis et al., 2008). Although the research on physical self-construal is only now developing, recent work by Gore and colleagues has shown that it is an orthogonal construct from the other self-construals, and it is associated with masculine gender roles (Gore & Dean, 2024). It also predicts behavioral intentions and reported behaviors that correspond to the self-definition while controlling for sex, including parenting practices (Rivera & Gore, 2023). Thus, we suggest that physical self-construal should be included with the other three forms of self-construal in an analysis of career interests, especially due to its connection with gender norms, and also because many career fields involve physically demanding tasks.

Measuring Career Interests

Although people pursue careers for a variety of reasons, their interests in particular careers are best assessed by how interested they are in the specific activities that occur within each vocation (Day & Rounds, 1997). Unlike other career assessments, Basic Interest Markers (BIMs) were developed primarily for the advancement of research in vocational interests, providing an expansive and thorough coverage of numerous career activities (Liao et al., 2008). Such activities can be further classified into a taxonomy of interest clusters, including social service, business, law-politics, and physical activity.

We suggest that these activities and clusters correspond to the primacy of the individual, relationships, ingroups, and physicality that are represented in the four self-construals. For example, people with a highly independent self-construal would be drawn to careers that allow them to display their unique abilities in competitive environments, such as law and creative arts. People with a highly interdependent self-construal would be drawn to careers that allow them to work with others as a collective unit, such as business. People with a highly relational self-construal would be drawn to activities that allow them to connect with others, such as in service careers. And, people with a highly physical self-construal would likely be interested in careers that involve physical exertion, such as outdoor agriculture and manual labor. To date, however, this has not been examined. We therefore examined the link between BIM markers and self-construals in Study 1.

Interest inventories and strength assessments have often been used by career counselors and other professionals to help individuals who are seeking employment (Owens et al., 2016). One of the most common interest assessments focuses on Holland’s six proposed themes (Holland, 1959, 1997): Realistic, Investigative, Artistic, Social, Enterprising, and Conventional. These themes define the type of work an individual would be doing on a daily basis and are valid across gender (Morgan et al., 2015). Realistic professions regularly involve hands-on problems and solutions, and they often include plants, animals, wood, tools, and machinery. Investigative occupations frequently involve working with ideas, and they often include the sciences. Artistic careers involve working with forms, designs, and patterns, and require abstract thinking and creativity. Social careers involve working with groups people, communicating with co-workers, and teaching others. Enterprising occupations frequently include developing businesses, carrying out projects, and making business deals. Conventional occupations frequently involve following set procedures, data entry, and other computer functions (Round et al., 1999).

Despite evidence that these dimensional scales contain some gender biases, sex differences remain even when accounting for the biases (Einarsdóttir & Rounds, 2009). These differences are consistent with the People-Things dimensions (Johnson et al., 2017; Lippa, 1998), with the largest differences on the Artistic, Realistic, and Social themes. Similar to the BIM markers, we suggest that self-construal may explain why people are drawn to these particular themes. For example, people with a highly independent self-construal would be drawn to the Investigative and Artistic themes, whereas people with a highly physical self-construal would be drawn to the Realistic theme. To date, however, no one has examined how self-construal may explain these differences. We therefore examined the link between Holland Codes and self-construals in Study 2.

Rationale and Hypotheses

There are notable sex differences in career interests across several dimensions, but the reasons for these differences are often left unexplained. Recent research has shown that some sex differences can be explained by self-construals, but to date no one has examined how self-construals may explain this regarding career interests. In two studies, we sought to be the first to explore how four self-construals (independent, interdependent, relational, and physical) predict career interests across a variety of domains (Study 1) and predicting interests based on Holland themes (Study 2). We predicted that independent self-construal would be positively associated with career interests in areas emphasizing uniqueness, innovation, and competition, such as science, law, and art. We predicted that interdependent self-construal would be positively associated with career interests in areas emphasizing group harmony and cooperation, such as business. We predicted that relational self-construal would be positively associated with career interests in areas emphasizing connections with others, such as personal and social services. Finally, we predicted that physical self-construal would be positively associated with career interests in areas emphasizing physicality, such as manual labor and athletics.

Study 1

For Study 1, we hypothesized the following associations between self-construals and career interests, and we expected all associations would remain significant while controlling for sex.

(1) Independent self-construal will have a positive association with Arts (e.g., performing arts and creative writing), Applied Mathematics (e.g., information technology and mathematics), and Law-Politics interests.

(2) Interdependent self-construal will be positively associated with Business interests (human relations, management, and professional advising).

(3) Relational self-construal will be positively associated with Personal Services interests (e.g., family activity) and Social Services interests (e.g., teaching).

(4) Physical self-construal will have a positive association with the Physical Activity interests (e.g., physical/risk taking and athletic coaching) and Technical interests (e.g., manual labor and protective services).

Method

Participants and Procedure

This study recruited 240 students from undergraduate psychology classes at a mid-south university. Most of the participants were young adults (M_age = 23.23, Range = 18–55, 4 unspecified), female assigned at birth (n = 207, 87.5% females; n = 30 males, 12.5%; n = 3 unspecified), and White/European-American (n = 199, 82.9% White/European-American; n = 21 Black/African-American, 8.8%; n = 10 Latin-American, 4.2%; n = 1 Asian-American, 0.4%; n = 5 Native American, 2.1%; n = 4 unspecified).

Participants were asked to complete an online survey. All participants were given a consent statement to read and were informed that participation in the study was completely voluntary. Participants were asked a series of questions within the survey regarding their definition of self and career interest area. After completing the survey, participants were instructed to read the online debriefing form. For participating in the study, participants received outside activity credit toward course completion. Study 1 was reviewed and approved by the University IRB (Protocol 2511).

Materials

Descriptive and reliability statistics are reported in Table 1. Data can be found at https://osf.io/dp4my/.

Table 1.

Descriptive and Reliability Statistics (Study 1).

	M	SD	α
Self-construal
Independent	4.07	0.42	.74
Interdependent	3.65	0.46	.77
Relational	3.76	0.65	.88
Physical	3.08	0.63	.88
BIM subscale
Creative Arts	2.60	1.19	.94
Performing Arts	2.21	1.05	.92
Creative Writing	2.58	1.16	.93
Law	2.68	1.26	.97
Politics	2.09	1.10	.93
Professional Advising	2.66	1.04	.92
Human Relations	3.03	1.05	.91
Office Work	2.52	1.03	.93
Business	2.15	1.12	.96
Management	1.94	0.96	.92
Finance	2.04	1.06	.94
Sales	1.91	1.00	.96
Family Activity	4.03	0.76	.84
Personal Service	2.30	0.89	.90
Teaching	2.85	0.97	.91
Social Services	3.75	0.90	.92
Athletic Coaching	2.44	1.15	.88
Physical/Risk-Taking	2.72	1.05	.89
Outdoor Agriculture	2.14	0.91	.88
Manual Labor	1.68	0.76	.91
Engineering	1.73	0.91	.95
Protective Service	2.03	1.07	.94
Technical Writing	1.58	0.87	.95
Information Tech	2.04	0.98	.94
Mathematics	1.76	1.00	.96

Sex

Participants were asked “What is your sex?” and answered based on one of four options (male, female, other, and prefer not to answer).

Independent/ Interdependent Self-Construal

The Self-Construal scale (Gudykunst et al., 1994; Gudykunst & Lee, 2003) was used to measure participants’ self-image in terms of independence and interdependence. The scale is composed of 28 items, with 14 items for independent self-construal (e.g., “I prefer to be self-reliant rather than be dependent on others”) and 14 for interdependent self-construal type (e.g., “I maintain harmony in the groups in which I am a member”). Respondents rated items on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Past research has shown that the measure demonstrates sufficient internal consistency and validity, across multiple cultures (Gudykunst et al., 1996; Gudykunst & Lee, 2003).

Relational Self-Construal

The Relational Interdependent Self-Construal (RISC) scale (Cross et al., 2000) was used to measure how participants incorporate relationships into how they define themselves (e.g., “My close relationships are an important reflection of who I am”). Respondents rated items on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Past research has shown that the measure demonstrates sufficient internal consistency and validity (Cross et al., 2000).

Physical Self-Construal

The Physical Self-Construal scale (Rivera & Gore, 2023) was used to measure how participants’ bodies and physical abilities are incorporated into how they define themselves (e.g., “When I think of myself, I often think of my physical body”). Respondents rated items on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Past research has shown that the measure demonstrates sufficient internal consistency and validity (Gore & Dean, 2024).

Career Interests

Basic Interest Markers scale contains 337 items across 27 specific areas of career interests (Liao et al., 2008). The participants were asked how interested they were in performing each task. The career interests represented in the measure included such areas as Business (e.g., “Develop business systems”), Family Activity (e.g., “Take care of children at home”), and Manual Labor (e.g., “Stack lumber in piles”). Respondents rated items on a 5-point Likert scale (1 = not interested at all, 5 = extremely interested). Past research has shown that each scale demonstrates sufficient internal consistency and validity (Liao et al., 2008). Using Jamovi (2024), a confirmatory factor analysis, extracting eight factors and utilizing Varimax rotation yielded a model that fits the data well, χ² (128) = 243, p < .01, and RMSEA = 0.06. The factor loadings also demonstrated that the items loaded on the expected interest clusters (see Table 2).

Table 2.

Factor Loadings for the BIM (Study 1).

	Factor
	1	2	3	4	5	6	7	8
BIM subscale
Creative Arts	.11	.80	.20	.13	−.01	.07	−.01	−.01
Performing Arts	.17	.72	−.02	.02	.16	.05	.11	.13
Creative Writing	.11	.72	.13	.29	−.14	.27	.08	.11
Law	.16	.16	.03	.11	.15	.79	.12	.05
Politics	.04	.26	.03	.16	.12	.37	−.04	−.11
Professional Advising	.42	.09	.20	.07	.05	.04	.21	.19
Human Relations	.35	.04	.07	.06	.11	.08	.02	.14
Office Work	.56	.06	.25	.35	−.28	.10	.16	.18
Business	.91	.19	.06	.17	.17	.04	−.07	.04
Management	.65	.18	.15	.34	.07	.19	.10	.00
Finance	.78	.00	.16	.12	.11	.17	.12	.03
Sales	.64	.26	.19	.06	.25	.01	.21	.07
Family Activity	.01	.08	−.09	.14	.05	−.03	−.04	.67
Personal Service	.23	.04	.21	.23	.23	.02	.28	.33
Teaching	.06	.21	.05	.54	.03	.03	.04	.08
Social Services	−.04	.21	−.04	.37	−.10	.19	−.23	.05
Athletic Coaching	.17	−.03	.09	.10	.63	.06	.17	.01
Physical/Risk-Taking	.09	.14	.26	−.01	.64	.32	−.18	.02
Outdoor Agriculture	.17	.17	.63	.15	.18	.12	.07	−.08
Manual Labor	.33	.10	.76	.08	.15	−.01	.20	−.04
Engineering	.04	.15	.39	.07	.26	.09	.04	−.26
Protective Service	.20	.09	.37	−.03	.04	.05	.02	.04
Technical Writing	.36	.21	.35	.16	.07	.11	.52	−.15
Information Tech	.47	.18	.33	.20	−.05	.19	.49	−.19
Mathematics	.05	.03	.23	.05	.07	.08	.21	−.11

Results

To test the hypotheses for Study 2, we used SPSS Version 27.0 (IBM Corp, 2020). A series of simultaneous linear regression analyses were conducted with the four self-construal scores and sex entered as the independent variables, and the career interest scores entered separately as the dependent variables. We used the “check_model” function within the R package “performance” to test the normality assumption for each model (Lüdecke et al., 2021). Based on the non-normality of some of the data, we performed additional bootstrap analyses using R 4.4.0 (R Core Team, 2021) and the lmboot package (0.0.1). The data, the results, and the R Code to reproduce the analyses are available at the Open Science Framework. The traditional and bootstrap analyses mostly yielded the same results, except the negative coefficients of Relational Self-Construal predicting Technical Writing and Physical/Risk-Taking were not significant. The overall results showed partial support for the hypotheses (see Table 3).

Table 3.

Regression Analyses of Self-Construals Predicting BIM Subscales, Controlling for Sex (Study 1).

		Self-Construal Predictors
		Independent	Interdependent	Relational	Physical	Sex
BIM subscale	Distri
Creative Arts	Bi	.06 (.17 [−.19, .53])	−.10 (−.26 [−.62, .06])	−.02 (−.04 [−.29, .18])	.05 (.09 [−.17, .34])	−.03 (−.11 [−.57, .36])
Performing Arts	Pos	.07 (.16 [−.16, .48])	.01 (.02 [−.31, .32])	−.07 (−.11 [−.33, .12])	.05 (.09 [−.12, .31])	−.02 (−.02 [−.44, .36])
Creative Writing	Nor	.19*(.53[.17, .86])	−.10 (−.26 [−.56, .05])	.06 (.12 [−.14, .34])	−.05 (−.09 [−.31, .15])	.02 (.09 [−.32, .49])
Law	Bi	.18*(.55[.19, .94])	−.15(−.41[−.78, −.02])	−.01 (−.02 [−.29, .26])	−.02 (−.03 [−.27, .25])	−.12(−.50[−.05, −.94])
Politics	Pos	.09 (.23 [−.10, .56])	−.04 (−.11 [−.47, .27])	−.04 (−.07 [−.34, .19])	−.05 (−.09 [−.32, .14])	−.18*(−.62[−.15, −1.06])
Technical Writing	Pos	−.13(−.26[−.49, −.04])	.07 (.14 [−.16, .44])	−.15*(−.19 [−.42, .04])	.16*(.22[.01, .43])	−.29*(−.71[−.32, −1.11])
Information Tech	Pos	−.09 (−.22 [−.46, .03])	−.02 (−.05 [−.31, .25])	−.04 (−.06 [−.29, .17])	.09 (.13 [−.07, .37])	−.36*(−1.09[−.71, −1.45])
Mathematics	Pos	−.01 (−.02 [−.29, .25])	.05 (.11 [−15, .37])	−.07 (−.11 [−.32, .11])	.10 (.16 [−.05, .36])	−.28*(−.85[−.45, −1.26])
Professional Advising	Nor	−.07 (−.21 [−.53, .11])	.06 (.15 [−.15, .46])	−.05 (−.07 [−.31, .16])	.15(.26[.05, .47])	−.09 (−.20 [−.57, .17])
Human Relations	Nor	.09 (.22 [−.10, .55])	.18*(.41[.11, .75])	−.07 (−.11 [−.32, .10])	.07 (.12 [−.09, .33])	−.06 (−.21 [−.57, .15])
Office Work	Bi	−.06 (−.15 [−.52, .16])	.10 (.22 [−.07, .51])	−.02 (−.03 [−.26, .20])	−.03 (−.05 [−.27, .19])	−.03 (−.07 [−.39, .33])
Business	Pos	−.02 (−.07 [−.41, .26])	.02 (.05 [−.29, .35])	−.07 (−.11 [−.35, .13])	.02 (.03 [−.21, .28])	−.29*(−.95[−1.37, −.46])
Management	Pos	−.01 (−.03 [−.30, .25])	−.01 (−.03 [−.32, .24])	−.01 (−.02 [−.22, .19])	.07 (.11 [−.11, .32])	−.20*(−.60[−1.00, −.18])
Finance	Pos	.02 (.04 [−.30, .37])	.00 (.01 [−.33, .35])	−.07 (−.12 [−.33, .11])	.01 (.02 [−.21, .25])	−.30*(−.99[−1.41, −.56])
Sales	Pos	−.04 (−.07 [−.38, .21])	.03 (.04 [−.26, .34])	−.04 (−.06 [−.28, .17])	.10 (.15 [−.09, .38])	−.31*(−1.02[−1.41, −.62])
Family Activity	Neg	.09 (0.14 [−.11, .40])	.07 (.13 [−.08, .34])	.18*(.22[.02, .41])	.05 (.07 [−.09, .22])	.20*(.49[.23, .79])
Personal Service	Nor	−.13(−.29[−.57, .04])	−.03 (−.04 [−.28, .23])	.02 (.04 [−.14, .21])	.21*(.30[.14, .47])	.06 (.25 [−.08, .56])
Teaching	Nor	.01 (.01 [−.33, .31])	−.05 (−.10 [−.39, .18])	−.04 (−.08 [−.28, .14])	.15(.23[.04, .42])	−.06 (−.15 [−.52, .17])
Social Services	Neg	.11 (.23 [−.03, .52])	.01 (.02 [−.27, .30])	.07 (.10 [−.12, .33])	−.07 (−.10 [−.31, .10])	.24*(.66[.25, 1.08])
Athletic Coaching	Nor	−.10 (−.28 [−.57, .02])	−.04 (−.09 [−.41, .24])	.01 (.02 [−.23, .24])	.36*(.67[.46, .88])	−.27*(−.84[−1.26, −.40])
Physical/Risk-Taking	Nor	.15(.38[.07, .69]	−.04 (−.09 [−.41, .27])	−.13*(−.21 [−.44, .02])	.35*(.59[.36, .83])	−.16(−.46[.08, .86])
Outdoor Agriculture	Pos	−.16(−.33[−.61, −.06])	.00 (−.01 [−.25, .22])	−.17(−.24[−.42, −.06])	.25*(.35[.17, .52])	−.09 (−.28 [−.61, .04])
Manual Labor	Pos	−.25*(−.44[−.63, −	.16(.24[.04, .44])	−.15(−.17[−.31, −.05])	.31*(.36[.21, .52])	−.21*(−.48[−.74, −.19])
Engineering	Pos	−.12(−.24[−.46, −.02])	.08 (.15 [−.07, .38])	−.19*(−.27[−.42, −.13])	.25*(.36[.21, .51])	−.50*(−1.26[−1.59, −.93])
Protective Service	Pos	−.04 (−.10 [−.41, .21])	−.07 (−.17 [−.50, .14])	−.09 (−.14 [−.39, .09])	.17*(.29[.07, .47])	−.18*(−.55[−.99, −.11])

Note. **p < .01; *p < .05. Distri. = Distribution of residuals (Bi. = Bimodal, Pos. = Positive Skewed, Nor. = Normal, and Neg. = Negative). First coefficient is standardized beta from traditional regression analysis. Coefficients in parentheses represent the standard beta from the bootstrap analysis and the 95% confidence interval of that coefficient.

Hypothesis 1 was only partially supported, in that Independent Self-Construal only positively predicted Creative Writing and Law activities. Hypothesis 2 was also partially supported, in that Interdependent Self-Construal only positively predicted Human Relations activities. Hypothesis 3 was only partially supported, in that Relational Self-Construal only positively predicted Family Activities. Hypothesis 4 was the only one that was fully supported: Physical Self-Construal positively predicted all activities in the Physical Activity and Technical clusters, and was often a stronger predictor than biological sex.

Sex was also a significant predictor of career interests across several BIM subscales. Specifically, men’s interest scores were higher than women’s in Law, Politics, Technical Writing, Information Technology, Mathematics, Business, Management, Finance, Sales, Athletic Coaching, Physical/Risk-Taking, Manual Labor, Engineering, and Protective Service. Women’s interest scores were higher than men’s in Family Activity and Social Services.

To summarize, we found partial support that self-construal can predict Basic Interest Markers in career domains. People with a highly independent self-construal focus on ways to enhance their uniqueness and therefore are attracted to careers in art and law. People with highly interdependent and relational self-construals focus on the betterment of their ingroups and their relationships, respectively, and are therefore drawn to careers that allow them to benefit the organization (e.g., human resources) or their family. People with a highly physical self-construal focus on their physical ability to accomplish tasks, and they are therefore drawn to career areas that either allow their entire body to be engaged or they are drawn to careers that require a lot of hands-on experience with physical objects. In most cases, physical self-construal was a stronger predictor of these interests than sex, suggesting that it may be a helpful way to anticipate career interests in areas with large sex differences.

This was the first study to examine links between self-construal and career interests, but there are still some unanswered questions. The BIM scale is used primarily for research purposes; it is not used for recommendations in career counseling nor vocational assessment. We therefore sought to address this by including a common assessment of career interests in Study 2 that is commonly used in vocational counseling settings: Holland themes.

Study 2

Study 2 expands upon Study 1 by examining the link between self-construal and Holland themes. We therefore hypothesized the following associations and that all associations would remain significant while controlling for sex.

(1) Independent self-construal will predict interest in the Investigative and Artistic themes.

(2) Both interdependent self-construal and the relational self-construal will predict interest in the Conventional, Enterprising, and Social themes.

(3) Physical self-construal will predict interest in the Realistic theme.

Method

Participants and Procedure

This study recruited 149 students from undergraduate psychology classes at the same university as in Study 2. Most of the participants were young adults (M_age = 20.22, Range = 18–43, 2 unspecified), female assigned at birth (n = 132, 88.6% females; n = 17 males, 11.4%; n = 0 unspecified), and White/European-American (n = 126, 84.6% White/European-American; n = 15 Black/African-American, 10.1%; n = 5 Latin-American, 3.4%; n = 1 Asian-American, 0.7%; n = 2 Native American, 1.3%; n = 0 unspecified).

Participants in this study were asked to complete an online survey. All participants were given a consent statement to read and informed that participation in the study was completely optional. Participants answered a series of questions within the survey regarding their definition of self and interests based on the Holland themes. After completing the survey, participants were directed to read the online debriefing form. For being a part of this study, participants received outside activity credit for the class. Study 2 was reviewed and approved by the University IRB (Protocol 2951).

Materials

Descriptive and reliability statistics are provided in Table 4. Data can be found at https://osf.io/dp4my/.

Table 4.

Descriptive and Reliability Statistics (Study 2).

	M	SD	α
Self-construal
Independent	4.10	0.39	.61
Interdependent	3.79	0.35	.67
Relational	3.74	0.65	.88
Physical	2.99	0.66	.90
Holland Code
Realistic	2.10	0.83	.92
Investigative	3.12	0.90	.91
Artistic	2.96	0.91	.89
Social	3.74	0.68	.79
Enterprising	2.83	0.83	.84
Conventional	2.54	0.90	.92

Sex

Participants were asked “What is your sex?” and answered based on one of four options (male, female, other, and prefer not to answer).

Self-Construal

Study 2 used the same self-construal scales as in Study 1.

Career Interests

Items from the O*NET Interest Profiler (Rounds et al., 1999) were used to measure participants’ interests within six different interest areas that connect to specific occupations. These interest areas include Realistic (e.g., “Build kitchen cabinets”), Investigative (e.g., “Do laboratory tests to identify diseases”), Artistic (e.g., “Write books or plays”), Social (e.g., “Give career guidance to people”), Enterprising (e.g., “Manage a retail store”), and Conventional (e.g., “Keep inventory records”). Respondents rated how interesting a certain task is to them using a 5-point Likert Scale (1 = Strongly like, 5 = Strongly dislike). Past research has shown that each scale demonstrates sufficient internal consistency and validity (Rounds et al., 1999). Using Jamovi (2024), a confirmatory factor analysis, extracting six factors and utilizing Varimax rotation yielded a model that fits the data well, χ² (1372) = 2197, p < .01, and RMSEA = 0.06. The factor loadings also demonstrated that the items loaded on the expected interest clusters (see Table 5).

Table 5.

Factor Loadings for the O*NET Interest Profiler (Study 2).

	Factor
	1	2	3	4	5	6
Items
Realistic 1	.097	.756	.080	.105	−.153	.054
Realistic 2	.131	.797	−.019	.074	−.032	.004
Realistic 3	.207	.704	.197	.162	−.164	.059
Realistic 4	.075	.597	.221	.153	.121	.058
Realistic 5	.094	.666	.194	.185	−.164	.098
Realistic 6	.320	.485	−.110	.065	.071	.088
Realistic 7	.221	.718	.147	.123	.047	.001
Realistic 8	.219	.745	.085	.098	.148	.040
Realistic 9	.226	.790	.131	.058	.027	.188
Realistic 10	.070	.612	.278	.088	.122	−.029
Investigative 1	−.006	.157	.722	−.135	−.053	.119
Investigative 2	.079	.150	.740	.133	.092	−.094
Investigative 3	.139	.248	.704	.089	−.088	.075
Investigative 4	.247	.161	.616	.332	.109	−.063
Investigative 5	.156	.084	.724	.014	.039	.006
Investigative 6	.185	.312	.452	.121	.155	.025
Investigative 7	.153	.159	.541	.364	.183	.034
Investigative 8	.109	.088	.768	.045	.037	.037
Investigative 9	.184	.078	.710	.219	−.001	.131
Investigative 10	.086	−.058	.809	−.015	.190	.116
Creative 1	.178	−.006	.068	.491	.128	−.023
Creative 2	.047	.086	−.006	.721	−.007	−.074
Creative 3	−.002	.098	.040	.736	.130	−.107
Creative 4	−.054	.148	.163	.625	−.005	.182
Creative 5	.077	.317	.065	.623	.100	.268
Creative 6	.007	.169	.126	.704	.043	.093
Creative 7	.170	.132	.139	.580	.158	.100
Creative 8	.128	−.002	.028	.611	.064	−.010
Creative 9	.124	.045	−.029	.682	.037	.095
Creative 10	.137	.189	.176	.608	.127	.280
Social 1	.124	.117	.166	.142	.215	.132
Social 2	.024	−.161	.056	.087	.714	−.029
Social 3	.223	−.011	.001	.117	.654	.034
Social 4	.024	.114	.215	.007	.637	.103
Social 5	.082	−.092	.162	.221	.454	.164
Social 6	.152	.172	.015	−.042	.462	.140
Social 7	.033	.109	.090	.277	.506	.134
Social 8	−.058	−.128	.123	.070	.714	.029
Social 9	−.078	.026	−.141	.071	.517	.110
Social 10	.305	.153	−.109	.110	.408	.247
Enterprising 1	.146	.336	.136	.004	−.035	.493
Enterprising 2	.355	.105	−.016	−.002	.104	.633
Enterprising 3	.078	.111	.080	.251	.219	.464
Enterprising 4	.241	.184	.325	.161	.107	.414
Enterprising 5	.366	.304	.148	−.132	−.007	.455
Enterprising 6	.245	.256	−.053	−.046	.088	.336
Enterprising 7	.028	.179	.122	.207	.009	.725
Enterprising 8	.099	−.091	.021	.059	.224	.755
Enterprising 9	.179	.018	.029	.001	.118	.851
Conventional 1	.558	.298	.235	.149	−.186	.062
Conventional 2	.690	.055	.174	.136	.073	.048
Conventional 3	.372	.348	.240	.221	−.052	.020
Conventional 4	.529	.067	.263	.137	−.163	.072
Conventional 5	.786	.089	.115	.129	.108	.051
Conventional 6	.730	.156	.184	−.001	.135	.061
Conventional 7	.762	.211	.101	.114	.053	.092
Conventional 8	.798	.104	.124	.069	.038	.048
Conventional 9	.819	.178	.131	.090	.067	.117
Conventional 10	.649	.178	.003	.216	.166	.163

Results

Table 6.

Regression Analyses of Self-Construals Predicting Holland Codes, Controlling for Sex (Study (2).

	Self-Construal Predictors
	Independent	Interdependent	Relational	Physical	Sex
Holland Code
Realistic	.03	−.04	−.08	.37**	−.13*
Investigative	.18*	.08	−.19*	.16⁺	−.03
Artistic	.13⁺	.10	.09	.05	−.10
Social	.09	.20*	.00	.01	.08
Enterprising	.08	.07	−.09	.10	−.01
Conventional	.15⁺	.07	−.13	.06	−.05

Note. **p < .01, *p < .05, and ⁺p < .10.

In support of Hypothesis 1, Independent Self-Construal positively predicted the Investigative and Artistic interest areas. It also unexpectedly positively predicted the Conventional interest area. Hypothesis 2 was only partially supported, in that Interdependent Self-Construal was only associated with the Social interest area. In contrast to Hypothesis 2, Relational Self-Construal was not positively associated with any interest area, but it was negatively associated with the Investigative area. In support of Hypothesis 3, Physical Self-Construal was positively associated with the Realistic interest area. There was only one instance where Sex was a significant predictor of a Holland theme: men’s interest scores on the Realistic theme subscale were higher than women’s. Sex did not predict any other Holland Code.

To summarize, Study 2 provides further support that self-construal can be used to predict the degree to which people are interested in particular careers. In accordance with Study 1 and our hypothesis, the tendency to define oneself based on one’s physical body and its abilities is an especially strong predictor of career areas that require manual labor or manipulation of physical objects (the Realistic theme). Although Realistic careers tend to attract more men than women, we found that physical self-construal not only predicts these interests while controlling for sex, but it is also a stronger predictor than sex. We also found that the other three self-construals also predicted corresponding interest areas whereas sex did not, suggesting that some previously noted sex differences in career interests may be attributed to differences in self-construal.

General Discussion

Sex differences in career interests have been well-documented in prior research, but there has been little emphasis on explaining the underlying reasons for those differences. This is the first set of studies to demonstrate that one of the reasons for those differences is due to variation in self-construals and how those may be more useful in predicting career interests than sex assigned at birth.

More specifically, both Studies 1 and 2 showed that physical self-construal consistently predicted career interests that emphasize physicality and working with objects. This included manual labor, physical/risk-taking, athletic coaching, and the Realistic Holland theme. In addition, both studies showed that independent self-construal predicted interests in careers that emphasize creativity (namely, creative writing in Study 1 and the Artistic Holland theme in Study 2), and interdependent self-construal predicted career interests involving working with others (namely, Human Relations in Study 1 and the Social Holland theme in Study 2). Relational self-construal was the least often associated with career interests, but it was associated with family activities, suggesting it may be more helpful in predicting interests in life domains other than traditional careers.

Implications

Our results provide added insights into the People-Things and Ideas-Data distinctions in the career interests literature (Etzel et al., 2023; Junkuhn & Nagy, 2022; Prediger, 1982; Su & Rounds, 2015). We replicated much of what had been found by others, namely, that women are drawn to vocations that involve working with people, whereas men are drawn to those that involve working with things (Kuhn & Wolter, 2022; Lippa, 1998; Morris, 2016; Su et al., 2009). We also found, however, that the four self-construals predict interests while controlling for biological sex, and in some cases they predicted more so than sex. Past research has already shown that individual differences in masculinity/femininity (Weisgram et al., 2011) and expressiveness/instrumentality can explain sex differences in career pursuits (Ludwikowski et al., 2018). Our research expands upon this, suggesting that individual differences in self-construal also explain some sex differences, and they may also account for contra-sex role career pursuits.

Assessments of self-construal may allow career counselors and vocational specialists to identify fields that best fit their clients’ self-definitions. This is especially the case for the independent and physical self-construals, which predicted career interests that emphasize the Ideas and Things domains, respectively. It may be that past findings suggesting men rate higher on these interest areas (Graziano et al., 2012) are due in part to the dual emphasis on independence and physicality in men’s self-definition and masculine gender roles (Gore & Dean, 2024). This may also allow for early identification of highly independent and/or physical women, who are interested in working with Things or Ideas.

These considerations would be especially important for career counselors who adopt Super’s (1963, 1990) career development approach. Part of this process is to help clients understand the role of the developing self in the process of selecting careers. By including self-construal scales as part of the self-assessment process, counselors could better match highly physical and highly independent clients with corresponding careers in physically demanding and creative domains, respectively. Frequent assessments of self-construal across the lifespan would also allow for indications of self-concept changes based on developmental and environmental changes. Such possibilities could therefore be the focus for future applications of this research. Others should attempt to address the limitations of our two studies.

Limitations and Future Directions

Despite the insights gained from our results, there are some limitations that must be addressed in future research. Most of our sample consisted of young adults in college, which is a developmental stage that involves considerable variability in career interests (Ertl et al., 2023; Paessler, 2015), especially in the Realistic and Conventional domains (Ion et al., 2019), and in the People dimension (Hoff et al., 2018). Although the rank-order stability across Holland Codes tends to remain stable throughout adulthood (Schultz et al., 2017), the interest in these areas changes substantially during transitional periods (e.g., high school to post-secondary). Thus, future research should consider examining the associations of self-construals with career interests across multiple developmental stages.

The assessment of sex in Studies 1 and 2 allowed for more than just binary responses, but it did not assess gender identity. In addition, we were unable to examine the interactions of sex with self-construal on career interests due to our limited sample sizes. As a result, we were unable to examine several other ways that sex assigned at birth, gender and gender identity influence, and interact with self-construals to predict, career interests. Future research should provide a more extensive assessment of sex and gender. It should also obtain a larger, more diverse, and more representative sample to allow for additional analyses with self-construal and career interests.

The primary focus of this research was to demonstrate how self-construal predicts career interests while controlling for sex assigned at birth. This is not to say that other covariates were uninvolved in the process. Parents’ career fields (Wong et al., 2011), race (Jang et al., 2019; Jones et al., 2021), and sexual orientation (Ellis et al., 2012) also influence how interested people are in career areas, and their likelihood of pursuing those interests. In addition, sex differences in career interests are more pronounced for some groups more than others; education (Hoff et al., 2024) and cultural values (Tao et al., 2022) moderate the size of the differences between men and women. Future research should therefore assess and control for these variables to better understand the extent to which self-construal predicts career interests.

Conclusions

Across two studies, we found that independent and physical self-construal are consistent predictors of corresponding career interests, and these results remained significant while controlling for sex. With recent technological and societal advancements, it is important to identify factors that predict career interest and success that do not rely on the traditional markers predetermined placement on career tracks. By assessing self-construals, career counselors may be better equipped at predicting which career areas a client may be drawn to. In doing so, people can more easily find the career that fits them best and in turn discover joy in the work they do.

Footnotes

Acknowledgment

We thank Dr Homer White for his help with the statistical analyses.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jonathan S. Gore

Author Biographies

Ms. Gulliford graduated with her Bachelor’s of Science in Psychology from Eastern Kentucky University, and then continued her education and successfully earned her Master’s degree from Lynn University in Psychology. In the future, she aims to establish and lead a consulting firm that specializes in providing strategic guidance to nonprofit organizations. This passion was sparked by her involvement in assisting with the startup of a nonprofit dedicated to supporting homeless individuals in their reintegration into society. As she progresses in her career, she is particularly interested in conducting research on best practices for nonprofit success and organizational leadership. Her goal is to contribute to the growth and sustainability of nonprofit organizations through effective leadership and innovative strategies.

Dr. Jonathan Gore received his Ph.D. in Psychology at Iowa State University and is an active member of the Southeastern Society of Social Psychologists and Kentucky Psychological Foundation. Currently, he is Associate Chair and a professor in the Psychology Department with a research focus on goal motivation, self-concept, and culture. Since joining the faculty at EKU, Dr. Gore has published over 75 articles and book chapters on these topics as well as topics developed over the years with his students. When he’s not spending quality time with his family, he enjoys making music and writing epic fantasy novels.

Dr. Sara Incera is an Associate Professor in the Psychology Department at Eastern Kentucky University. She obtained her undergraduate degree in Psychology at the Universidad de Salamanca (Spain) and her Ph.D. in Psychology at Cleveland State University (USA). At EKU she teaches classes about Cognitive Psychology and Research Methods. She is the faculty mentor for a group supporting research on campus (Data Initiatives), as well as the director of her own psychology lab (Multilingual Laboratory). Her research interests include new methodological approaches, critical reading, bilingualism, foreign accents, and language acquisition across the lifespan. She loves hiking and she particularly enjoys the Red River Gorge, a beautiful area located in the foothills of the Appalachian Mountains.

References

Beltz

A. M.

Swanson

J. L.

Berenbaum

S. A.

(2011). Gendered occupational interests: Prenatal androgen effects on psychological orientation to Things versus People. Hormones and Behavior, 60(4), 313–317. https://doi.org/10.1016/j.yhbeh.2011.06.002

Blustein

D. L.

Noumair

D. A.

(1996). Self and identity in career development: Implications for theory and practice. Journal of Counseling and Development, 74(5), 433–441. https://doi.org/10.1002/j.1556-6676.1996.tb01889.x

Bry

Follenfant

Meyer

(2008). Blonde like me: When self-construals moderate stereotype priming effects on intellectual performance. Journal of Experimental Social Psychology, 44, 751–757.

Bui

Miller

C. C.

(2017). The jobs you’re most likely to inherit from your mother than father. New York Times. Retrieved from: https://www.nytimes.com/interactive/2017/11/22/upshot/the-jobs-youre-most-likely-to-inherit-from-your-mother-and-father.html

Bureau of Labor Statistics . (2023). Job openings and labor turnover survey news release—2023 M10 results. Bureau of Labor Statistics. Retrieved from: https://www.bls.gov/news.release/jolts.htm

Chen

Lee-Chai

A. Y.

Bargh

J. A.

(2001). Relationship orientation as a moderator of the effects of social power. Journal of Personality and Social Psychology, 80(2), 173–187. https://doi.org/10.1037/0022-3514.80.2.173

Church

A. T.

Anderson-Harumi

C. A.

del Prado

A. M.

Curtis

G. J.

Tanaka-Matsumi

Valdez Medina

J. L.

Mastor

K. A.

White

F. A.

Miramontes

L. A.

Katigbak

M. S.

Katigbak

M. S.

(2008). Culture, cross-role consistency, and adjustment: Testing trait and cultural psychology perspectives. Journal of Personality and Social Psychology, 95(3), 739–755. https://doi.org/10.1037/0022-3514.95.3.739

Cousins

S. D.

(1989). Culture and self-perception in Japan and the United States. Journal of Personality and Social Psychology, 56(1), 124–131. https://doi.org/10.1037//0022-3514.56.1.124

Coyle

T. R.

(2020). Sex differences in tech tilt: Support for investment theories. Intelligence, 80(1), 101437. https://doi.org/10.1016/j.intell.2020.101437

10.

Cross

S. E.

Bacon

P. L.

Morris

M. L.

(2000). The relational-interdependent self-construal and relationships. Journal of Personality and Social Psychology, 78(4), 791–808. https://doi.org/10.1037/0022-3514.78.4.791

11.

Cross

S. E.

Hardin

E. E.

Gercek-Swing

(2011). The what, how, why, and where of self-construal. Personality and Social Psychology Bulletin, 15(2), 142–179. https://doi.org/10.1177/1088868310373752

12.

Cross

S. E.

Madson

(1997). Models of the self: Self-construals and gender. Psychological Bulletin, 122(1), 5–37. https://doi.org/10.1037/0033-2909.122.1.5

13.

Cross

S. E.

Morris

M. L.

Gore

J. S.

(2002). Thinking about oneself and others: The relational-interdependent self-construal and social cognition. Journal of Personality and Social Psychology, 82(3), 399–418.

14.

Day

S. X.

Rounds

(1997). “A little more than kin, and less than kind”: Basic interests in vocational research and career counseling. The Career Development Quarterly, 45(3), 207–220. https://doi.org/10.1002/j.2161-0045.1997.tb00465.x

15.

Einarsdóttir

Rounds

(2009). Gender bias and construct validity in vocational interest measurement: Differential item functioning in the Strong Interest Inventory. Journal of Vocational Behavior, 74(3), 295–307. https://doi.org/10.1016/j.jvb.2009.01.003

16.

Ellis

Herschberger

Field

Wersinger

Pellis

Geary

Karadi

(2008). Sex differences: Summarizing more than a century of scientific research (1st ed.). Psychology Press.

17.

Ellis

Ratnasingam

Wheeler

(2012). Gender, sexual orientation, and occupational interests: Evidence of their interrelatedness. Personality and Individual Differences, 53(1), 64–69. https://doi.org/10.1016/j.paid.2012.02.008

18.

Ertl

Hartmann

F. G.

Wunderlich

(2023). Stability of vocational interests during university studies. Journal of Individual Differences, 44(3), 143–162. https://doi.org/10.1027/1614-0001/a000392

19.

Etzel

J. M.

Krey

Nagy

(2023). We’ve come full circle: The universality of People-Things and Data-Ideas as core dimensions of vocational interests. Journal of Vocational Behavior, 145(1), 103897–103919. https://doi.org/10.1016/j.jvb.2023.103897

20.

Ferenczi

Marshall

T. C.

Bejanyan

(2017). Are sex differences in antisocial and prosocial Facebook use explained by narcissism and relational self-construal? Computers in Human Behavior, 77(1), 25–31. https://doi.org/10.1016/j.chb.2017.08.033

21.

Gardner

W. L.

Gabriel

Dean

K. K.

(2004). The individual as “melting pot”: The flexibility of bicultural self-construals. Cahiers de Psychologie Cognitive, 22(1), 181–201.

22.

Gati

Perez

(2014). Gender differences in career preferences from 1990 to 2010: Gaps reduced but not eliminated. Journal of Counseling Psychology, 61(1), 63–80. https://doi.org/10.1037/a0034598

23.

Gore

J. S.

Cross

S. E.

(2006). Pursuing goals for us: Relationally-autonomous reasons in long-term goal pursuit. Journal of Personality and Social Psychology, 90(5), 848–861. https://doi.org/10.1037/0022-3514.90.5.848

24.

Gore

J. S.

Cross

S. E.

(2011). Conflicts of interest: Relational self-construal and decision-making in interpersonal contexts. Self and Identity, 10(2), 185–202. https://doi.org/10.1080/15298861003684390

25.

Gore

J. S.

Dean

(2024). I am my body: The physical self-construal [Manuscript in preparation]. Department of Psychology, Eastern Kentucky University.

26.

Graziano

W. G.

Habashi

M. M.

Evangelou

Ngambeki

(2012). Orientations and motivations: Are you a “people person,” a “thing person,” or both? Motivation and Emotion, 36(4), 465–477. https://doi.org/10.1007/s11031-011-9273-2

27.

Gudykunst

W. B.

Lee

C. M.

(2003). Assessing the validity of self-construal scales: A response to Levine et al. Human Communication Research, 29(2), 253–274. https://doi.org/10.1093/hcr/29.2.253

28.

Gudykunst

W. B.

Matsumoto

Ting-Toomey

Nishida

Kim

Heyman

(1994, July). Measuring self construals across cultures. Sydney, Australia: Paper presented at the annual meeting of the International Communication Association.

29.

Gudykunst

W. B.

Matsumoto

Ting-Toomey

Nishida

Kim

Heyman

(1996). The influence of cultural individualism-collectivism, self-construals, and individual values on communication styles across cultures. Human Communication Research, 22(4), 510–543. https://doi.org/10.1111/j.1468-2958.1996.tb00377.x

30.

Hartung

P. J.

Subich

L. M.

(2011). In Hartung

P. J.

Subich

L. M.

(Eds.), Developing self in work and career: Concepts, cases, and contexts. American Psychological Association. Retrieved from: https://doi.org/10.1037/12348-000

31.

Heine

S. H.

Lehman

D. R.

Markus

H. R.

Kitayama

(1991). Is there a universal need for positive self-regard? Psychological Review, 106(4), 766–794. https://doi.org/10.1037//0033-295x.106.4.766

32.

Hoff

K. A.

Briley

D. A.

Wee

C. J. M.

Rounds

(2018). Normative changes in interests from adolescence to adulthood: A meta-analysis of longitudinal studies. Psychological Bulletin, 144(4), 426–451. https://doi.org/10.1037/bul0000140

33.

Hoff

K. A.

Granillo-Velasquez

K. E.

Hanna

Morris

Nelson

H. S.

Oswald

F. L.

(2024). Interested and employed? A national study of gender differences in basic interests and employment. Journal of Vocational Behavior, 148(1), 103942–104019. https://doi.org/10.1016/j.jvb.2023.103942

34.

Hoff

K. A.

Song

Q. C.

Einarsdóttir

Briley

D. A.

Rounds

(2020). Developmental structure of personality and interests: A four-wave, 8-year longitudinal study. Journal of Personality and Social Psychology, 118(5), 1044–1064. https://doi.org/10.1037/pspp0000228

35.

Holland

J. L.

(1959). A theory of vocational choice. Journal of Counseling Psychology, 6(1), 35–45. https://doi.org/10.1037/h0040767

36.

Holland

J. L.

(1997). Making vocational choices: A theory of vocational personalities and work environments. In Lake magdalene, FL. Psychological Assessment Resources.

37.

Hyde

J. S.

(2005). The gender similarities hypothesis. American Psychologist, 60(6), 581–592. https://doi.org/10.1037/0003-066X.60.6.581

38.

IBM Corp . (2020). IBM SPSS statistics for windows (version 27.0). IBM. [Computer software].

39.

Ion

Nye

C. D.

Iliescu

(2019). Age and gender differences in the variability of vocational interests. Journal of Career Assessment, 27(1), 97–113. https://doi.org/10.1177/1069072717748646

40.

Jang

Pak

Lee

(2019). Meta-analysis: The role of race/ethnicity and gender in career choice. Journal of Asia Pacific Counseling, 9(2), 119–135. https://doi.org/10.18401/2019.9.2.8

41.

Jiang

Canevello

Gore

J. S.

Hahn

J. H.

Crocker

(2017). The association between compassionate goals and relational-interdependent self-construal. Self and Identity, 16(2), 143–170. https://doi.org/10.1080/15298868.2016.1238406

42.

Johnson

J. F.

Trent

J. D.

Barron

L. G.

(2017). Gender, racial, and ethnic differences in job context and work-activity preferences. Military Psychology, 29(6), 542–559. https://doi.org/10.1037/mil0000191

43.

Jones

K. S.

Newman

D. A.

Rounds

(2021). Black-White differences in vocational interests: Meta-analysis and boundary conditions. Journal of Business and Psychology, 36(4), 589–607. https://doi.org/10.1007/s10869-020-09693-5

44.

Junkuhn

Nagy

(2022). Die Bedeutung der Interessenkongruenz für die Schulzufriedenheit in der beruflichen Oberstufe [The importance of interest congruence for school satisfaction in vocational secondary schools]. Zeitschrift für Erziehungswissenschaft, 25(3), 659–686. https://doi.org/10.1007/s11618-022-01067-2

45.

Kanagawa

Cross

S. E.

Markus

H. R.

(2001). “Who am I?” The cultural psychology of the conceptual self. Personality and Social Psychology Bulletin, 27(1), 90–103. https://doi.org/10.1177/0146167201271008

46.

Kashima

Yamaguchi

Kim

Choi

Gelfand

M. J.

Yuki

(1995). Culture, gender, and self: A perspective from individualism-collectivism research. Journal of Personality and Social Psychology, 69(5), 925–937. https://doi.org/10.1037//0022-3514.69.5.925

47.

Kitayama

Karasawa

Mesquita

(2004). Cultural affordances and emotional experience: Socially engaging and disengaging in Japan and the United States. Journal of Personality and Social Psychology, 91, 890–903.

48.

Kuhn

Wolter

S. C.

(2022). Things versus people: Gender differences in vocational interests and in occupational preferences. Journal of Economic Behavior & Organization, 203(1), 210–234. https://doi.org/10.1016/j.jebo.2022.09.003

49.

Lam

A. G.

Zane

N. W. S.

(2004). Ethnic differences in coping with interpersonal stressors: A test of self-construals as cultural mediators. Journal of Cross-Cultural Psychology, 35(4), 446–459. https://doi.org/10.1177/0022022104266108

50.

Liao

H.-Y.

Armstrong

P. I.

Rounds

(2008). Development and initial validation of public domain Basic Interest Markers. Journal of Vocational Behavior, 73(1), 159–183. https://doi.org/10.1016/j.jvb.2007.12.002

51.

Lippa

(1998). Gender-related individual differences and the structure of vocational interests: The importance of the people–things dimension. Journal of Personality and Social Psychology, 74(4), 996–1009. https://doi.org/10.1037/0022-3514.74.4.996

52.

Lüdecke

Ben-Shachar

M. S.

Patil

Waggoner

Makowski

(2021). performance: An R package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6(60), 3139. https://doi.org/10.21105/joss.03139

53.

Ludwikowski

W. M. A.

Armstrong

P. I.

Lannin

D. G.

(2018). Explaining gender differences in interests: The roles of instrumentality and expressiveness. Journal of Career Assessment, 26(2), 240–257. https://doi.org/10.1177/1069072717692743

54.

Markus

H. R.

Kitayama

(1991). Culture and the self: Implications for cognition, emotion, and motivation. Culture and the Self: Implications for Cognition, Emotion, and Motivation, 98(2), 224–253. https://doi.org/10.1037//0033-295x.98.2.224

55.

Marlowe

(2007). Hunting and gathering: The human sexual division of foraging labor. Cross-Cultural Research, 41(2), 170–195. https://doi.org/10.1177/1069397106297529

56.

Morgan

de Bruin

G. P.

de Bruin

(2015). Gender differences in Holland’s circular/ circumplex interest structure as measured by the South African Career Interest Inventory. South African Journal of Psychology, 45(3), 349–360. https://doi.org/10.1177/0081246315572514

57.

Morris

M. L.

(2016). Vocational interests in the United States: Sex, age, ethnicity, and year effects. Journal of Counseling Psychology, 63(5), 604–615. https://doi.org/10.1037/cou0000164

58.

Owens

R. L.

Motl

T. C.

Krieshok

T. S.

(2016). A comparison of strengths and interests protocols in career assessment and counseling. Journal of Career Assessment, 24(4), 605–622. https://doi.org/10.1177/1069072715615854

59.

Paessler

(2015). Sex differences in variability in vocational interests: Evidence from two large samples. European Journal of Personality, 29(5), 568–578. https://doi.org/10.1002/per.2010

60.

Prediger

D. J.

(1982). Dimensions underlying Holland’s hexagon: Missing link between interests and occupations? Journal of Vocational Behavior, 21(3), 259–287. https://doi.org/10.1016/0001-8791(82)90036-7

61.

R Core Team . (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from: https://www.R-project.org

62.

Rhee

Uleman

J. S.

Lee

H. K.

Roman

R. J.

(1995). Spontaneous self-descriptions and ethnic identities in individualistic and collectivistic cultures. Journal of Personality and Social Psychology, 69(1), 142–152. https://doi.org/10.1037//0022-3514.69.1.142

63.

Rivera

Gore

J. S.

(2023). The role of self-construal in child rearing: A relational-physical comparison. Psychological Studies, 68(1), 58–69. https://doi.org/10.1007/s12646-022-00703-5

64.

Rounds

Walker

C. M.

Day

S. X.

Hubert

(1999). O*NET interest profiler: Reliability, validity, and self-scoring. National Center for O*NET Development.

65.

Schultz

L. H.

Connolly

J. J.

Garrison

S. M.

Leveille

M. M.

Jackson

J. J.

(2017). Vocational interests across 20 years of adulthood: Stability, change, and the role of work experiences. Journal of Research in Personality, 71(1), 46–56. https://doi.org/10.1016/j.jrp.2017.08.010

66.

Rounds

(2015). All STEM fields are not created equal: People and things interests explain gender disparities across STEM fields. Frontiers in Psychology, 6(1), 189. https://doi.org/10.3389/fpsyg.2015.00189

67.

Rounds

Armstrong

P. I.

(2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin, 135(6), 859–884. https://doi.org/10.1037/a0017364

68.

Suh

E. M.

(2002). Culture, identity consistency, and subjective well-being. Journal of Personality and Social Psychology, 83(6), 1378–1391. https://doi.org/10.1037//0022-3514.83.6.1378

69.

Super

D. E.

(1963). Self-concepts in vocational development. In Super

D. E.

(Ed.), Career development: Self-concept theory (pp. 1–16). College Entrance Examination Board.

70.

Super

D. E.

(1990). A life-span, life-space approach to career development. In Brown

Brooks

(Eds.), Career choice and development: Applying contemporary theories to practice (2nd ed. pp. 197–261). Jossey-Bass.

71.

Svedholm-Häkkinen

A. M.

Ojala

S. J.

Lindeman

(2018). Male brain type women and female brain type men: Gender atypical cognitive profiles and their correlates. Personality and Individual Differences, 122(1), 7–12. https://doi.org/10.1016/j.paid.2017.09.041

72.

Tao

Glosenberg

Tracey

T. J. G.

Blustein

D. L.

Foster

L. L.

(2022). Are gender differences in vocational interests universal? Moderating effects of cultural dimensions. Sex Roles: Journal of Research, 1(1), 4. https://doi.org/10.1007/s11199-022-01318-w

73.

Tay

Rounds

(2011). People–things and data–ideas: Bipolar dimensions? Journal of Counseling Psychology, 58(3), 424–440. https://doi.org/10.1037/a0023488

74.

The Jamovi Project . (2024). Jamovi (Version 2.5) [Computer Software]. The Jamovi Project. Retrieved from: https://www.jamovi.org

75.

Thorndike

E. L.

(1911). Individuality. Houghton Mifflin.

76.

van Baaren

R. B.

Maddux

W. W.

Chartrand

T. L.

de Bouter

van Knippenberg

(2003). It takes two to mimic: Behavioral consequences of self-construals. Journal of Personality and Social Psychology, 84(5), 1093–1102. https://doi.org/10.1037/0022-3514.84.5.1093

77.

van der Linden

Dunkel

C. S.

De Zeeuw

E. J.

Pelt

D. H. M.

(2021). The general factor of personality (gfp) and vocational interests: A test of social effectiveness at the behavioral and genetic level. Journal of Business and Psychology, 1(2), 1. https://doi.org/10.1007/s10869-021-09779-8

78.

Wee

Newman

D. A.

Song

Schinka

J. A.

(2021). Vocational interests, gender, and job performance: Two person–occupation cross level interactions. Personnel Psychology, 74(2), 323–368. https://doi.org/10.1111/peps.12411

79.

Weisgram

E. S.

Dinella

L. M.

Fulcher

(2011). The role of masculinity/femininity, values, and occupational value affordances in shaping young men’s and women’s occupational choices. Sex Roles: Journal of Research, 65(3–4), 243–258. https://doi.org/10.1007/s11199-011-9998-0

80.

Whitmarsh

Wentworth

D. K.

(2012). Gender similarity or gender difference? Contemporary women’s and men’s career patterns. The Career Development Quarterly, 60(1), 47–64. https://doi.org/10.1002/j.2161-0045.2012.00005.x

81.

Wiernik

B. M.

(2016). Intraindividual personality profiles associated with realistic interests. Journal of Career Assessment, 24(3), 460–480. https://doi.org/10.1177/1069072715599378

82.

Wille

De Fruyt

(2014). Vocations as a source of identity: Reciprocal relations between Big Five personality traits and RIASEC characteristics over 15 years. Journal of Applied Psychology, 99(2), 262–281. https://doi.org/10.1037/a0034917

83.

Wills

Petrakis

(2019). The self in motherhood: A systematised review of relational self-construal and wellbeing in mothers. Advances in Mental Health, 17(1), 72–84. https://doi.org/10.1080/18387357.2018.1476066

84.

Wong

C.-S.

Wong

P.-M.

Peng

K. Z.

(2011). An exploratory study on the relationship between parents’ career interests and the career interests of young adults. International Journal for Educational and Vocational Guidance, 11(1), 39–53. https://doi.org/10.1007/s10775-011-9190-7

85.

Woodcock

Graziano

W. G.

Branch

S. E.

Habashi

M. M.

Ngambeki

Evangelou

(2013). Person and thing orientations: Psychological correlates and predictive utility. Social Psychological and Personality Science, 4(1), 116–123. https://doi.org/10.1177/1948550612444320

86.

Yan

Hong

(2007). A review of self-construal theories. Psychological Science, 30(1), 1272–1275.