SCCT and Underrepresented Populations in STEM Fields

Abstract

The authors review research that has used social cognitive career theory as a frame to investigate factors that may explain science, technology, engineering, and mathematics (STEM) choices and work decisions for women and racial–ethnic minorities, as well as barriers to their entry to STEM careers. The research is reviewed by age-groups. Most of this research has focused on factors associated with early choices (e.g., in high school and younger), but more recent work has focused on choices in college and in the workplace, particularly for women. The authors conclude with a critique and call for more research.

Keywords

social cognitive career theory STEM racial–ethnic minorities women

Although the term “science, technology, engineering, and mathematics (STEM) careers” is used to connote technical or scientific occupations, in reality, the term encompasses a broad set of educational majors and careers that differ quite a bit in the proportion of women and racial–ethnic minorities occupying them. For example, according to the National Science Foundation (NSF, 2015), the number of racial–ethnic minorities completing bachelor’s degrees in psychology, social sciences, biological, and computer sciences has increased over the past two decades. Also, the share of women in biosciences and social sciences (with the exception of economics) has increased between 49% and 58%, depending on the subfield and level of degree in STEM. However, since 2000, underrepresented racial–ethnic minorities’ graduation rates have flat-lined in engineering and physical sciences, and their numbers have dropped specifically in mathematics and statistics (NSF, 2015). Between 1993 and 2012, underrepresented minority women earned bachelor’s degrees at higher rates in psychology and social sciences than in engineering, computer sciences, and mathematics (NSF, 2015). Over the past 10 years, underrepresented racial–ethnic minorities’ completion of doctoral degrees in science and engineering has steadily been at roughly 7%.

There are even more challenges for these underrepresented groups in the active STEM workforce. According to the 2010 Census Bureau, women comprise approximately 52% of the U.S. population, African Americans 12%, Latinos 16%, Asians 5%, and all other racial–ethnic groups 3%. If the occupational landscape was equal, similar representation would hold for all occupations, for example, all scientists, lawyers, physicians, and dietitians would be roughly 52% female, 12% African American, and so on. A. Byars-Winston, Fouad, and Wen (2015) examined census data from 1970 to 2010 to determine the distribution of women and racial–ethnic minorities within 35 benchmark occupations across four decades. They were investigating whether, as women and racial–ethnic minorities entered the labor force in greater numbers than prior to 1970, they were absorbed equally across occupational groups in proportion to their distribution in the populations. They found, as expected, more women were in the labor force in 2010 than in 1970 and that the racial–ethnic representation of the work force had increased as well. They also found three STEM occupations (engineers, scientists, and pharmacists) in which women and racial–ethnic minorities had not been absorbed, and three that had absorbed women or racial–ethnic minorities. Specifically, White women and Asian men and women increased their participation in the occupations of economist and physician. White, Black, and Hispanic women increased their participation in the occupation of veterinarian. Thus, although there has been an increase of representation in some STEM occupations, women and racial–ethnic minorities continue to be underrepresented in many STEM fields.

Employment status for women and racial–ethnic minorities in STEM occupations is of particular concern, as it uncovers another level of disparity in comparison to White and Asian males. Underrepresented women and racial–ethnic minority engineers and scientists experience higher rates of unemployment than their White male counterparts (NSF, 2015). These groups are also underemployed with a higher percentage of underrepresented racial–ethnic minorities working part-time (NSF, 2015). For example, data from 2013 show that racial–ethnic minority men (9%) and women (20%) were employed part-time, compared to White men (10%) and women (26%). Of all underrepresented groups, White women were the most likely to have part-time employment (25%). Variations between racial–ethnic groups’ reasons for not working or not working full-time reflect cultural expectations and the distribution of age in the STEM fields. Unemployed racial–ethnic minority women engineers and scientists reported significantly more family responsibilities as a reason for not working full-time (NSF, 2015). Overall, both White men and women cited no need or no desire to work as a reason for not working full-time. This may be related to racial–ethnic differences in wealth accumulation, with Whites having, on average, 6 times the wealth of Blacks and Hispanics (McKernan, Ratcliffe, Steuerle, & Zhang, 2013), thus allowing some White men and women to choose to work less than full-time.

Within STEM-related occupations, women and underrepresented racial–ethnic minorities also differ in rank, tenure, and other job characteristics compared to their White male peers. Since 1993, one area where women rose in representation is in full-time full professorship academic positions, which grew dramatically from 10% to approximately 25% in science, engineering, and medicine (NSF, 2015). Racial–ethnic differences are also observed in the numbers of full-time full professorships between 1993 and 2013 in these same fields. Racial–ethnic minority men’s and women’s representation in full-time full professorship positions increased from only 4% to 6%. On a positive note, salaries for doctoral degree recipients with comparable work experience are similar for men, women, and racial–ethnic minorities (NSF, 2015).

As an occupation, engineering has garnered the most attention from stakeholders and researchers, primarily because engineering is one of the most sex-segregated professional occupations in the United States today. About 20% of engineering graduates are women and 18% are racial–ethnic minorities, the successful result of over three decades of intense early education interventions. However, women are less represented within the occupation itself (NSF, 2015). A. M. Byars-Winston, Branchaw, Pfund, Leverett, and Newton (2015) found that in 2010, White, Black, Hispanic, Asian, and Native American women were 8%, 0.9%, 0.6%, 2.6%, and 0.03% (11% total) of engineers, respectively. Black, Hispanic, and Native American men were 3%, 4%, and 0.4% of engineers, respectively. Asian men were the only group overrepresented in the occupation of engineering (11.6%) relative to their percentage in the population.

Social Cognitive Career Theory (SCCT) as a Framework for Understanding Underrepresentation in STEM Fields

The underrepresentation of women and racial–ethnic minorities has been a concern of vocational psychologists for the past four decades and was one of the first areas of investigation from the social cognitive perspective applied to vocational psychology. In 1981, Hackett and Betz proposed using Bandura’s self-efficacy theory (1977, 1997) to conceptualize why women do not pursue particular careers. They followed up their proposal with a study finding that college women had lower self-efficacy to complete the educational requirements or the job duties of traditionally male occupations (such as physician or engineer) than of traditionally female occupations (e.g., social worker, teacher; Betz & Hackett, 1981). They then went on to focus more specifically on the role of math self-efficacy in career choices of women, finding, for example, that college women were lower in math self-efficacy than college men and that math-self efficacy expectations were stronger predictors of a math-related career choice than was math performance (Hackett & Betz, 1989). As they noted in their 2006 retrospective,

higher levels of self-efficacy are postulated to lead to “approach” versus “avoidance” behavior, and we could see the usefulness of conceptualizing women’s underrepresentation in math as a problem of low expectations of math efficacy as well as one of math anxiety. (Betz & Hackett, p. 4)

A decade later, Hackett teamed with Lent and Brown to formulate the SCCT (1994); they postulated that self-efficacy operated in concert with outcome expectations to lead to interests, which in turn lead to career choices. The SCCT (Lent, Brown, & Hackett, 1994, 2000) has continued to be the major theoretical framework investigating factors that have contributed to the underrepresentation of women and racial–ethnic minorities in STEM fields. This has continued to be an area of investigation because there have been consistent race and gender disparities at the educational and occupational levels in STEM professions, even 35 years after Betz and Hackett began to study it. SCCT has also been used as a frame to examine all of the empirical studies in the past 40 years that have examined gender differences in STEM careers (Kanny, Sax, & Riggers-Piehl, 2014), primarily because the model explicitly incorporates gender as a person input and explicitly includes contextual influences at proximal and distal levels. Kanny, Sax, and Riggers-Piehl (2014) found five general areas of research: studies that focused on individual characteristics (e.g., socioeconomic status [SES], race), structural barriers in middle and high school education (e.g., classroom climates), psychological factors (e.g., self-efficacy, sense of belonging), family influences (e.g., gender role socialization and self-concept), and perceptions of STEM careers.

Interestingly, even though SCCT was the frame that Kanny et al. (2014) used to synthesize the literature, they did not explicitly review research that employed the SCCT model. We aim to review that literature in this article. Specifically, we will review research that has used the SCCT frame to investigate factors that may explain STEM choices and work decisions for women and racial–ethnic minorities. Most of this research has focused on factors influencing early choices (e.g., in high school and younger), but more recent work has focused on choices in college and in the workplace, and so we will review the research by educational (or postgraduate) levels.

The SCCT interest and choice models encompass a variety of constructs such as self-efficacy in a particular domain, outcome expectations, and interests as well as contextual factors that influence career choices. These models would predict that building self-efficacy for math and science and fostering the development of positive and realistic outcome expectations for entering an STEM career would lead to realistic and investigative interests that would, in turn, lead to STEM career goals and preparation for, and entry into, an STEM occupation. But, as we review below, women and racial–ethnic minorities are not choosing to enter—or stay in—STEM careers at the same rate as men and racial majority persons. Their lower rates of entrance into STEM fields may not be related to a lack of interest or intention. Hanson (2004), using longitudinal data, revealed that African American women had equal or higher interests and intentions to pursue majors leading to a science field compared to White women. Hanson suggests that both racism and sexism may act as deterrents from science involvement for African American women. Thus, researchers have examined a variety of other factors that may explain the gender and racial–ethnic disparities in STEM occupational involvement, including low self-efficacy, negative outcome expectations, and contextual influences, either distal or more proximal, to the choice.

Within the SCCT framework, contextual factors, such as SES or mentoring, may affect a person’s career trajectory as facilitators or hindrances and may be the key factors in understanding the underrepresentation for women and racial–ethnic minorities in STEM careers. For underrepresented groups in STEM, systemic barriers (e.g., racism or sexism) may affect the entrance and persistence in a career field via their effects on self-efficacy and outcome expectations. Perceived racism may serve as a psychological barrier for people of color, whereas sexism can be a barrier for women. Women of color may encounter both racism and sexism as challenges to persistence. Research with African Americans has shown that perceived and actual experiences of racism hinder their immediate educational opportunities and later career outlooks (Mattison & Aber, 2007; Rollins & Valdez, 2006). For example, urban adolescents who experience racism in academic/work environments have negative outcome expectations for future careers (Chaves et al., 2004). Racism may also be a direct predictor of career choice in addition to being mediated by self-efficacy beliefs or outcome expectations. Using an SCCT framework allows us to understand the complexity of factors and opportunities for intervention presented along a career trajectory. SCCT can also be an asset to those working in direct practice, as it points directly to areas where intervention can facilitate the decision-making process.

Middle and High School

We start with a focus on middle school and high school because it is a critical period in terms of career development. It is the period when adolescents are introduced to specific, and perhaps advanced, coursework in science and mathematics. At a fundamental level, the most important coursework in these years is mathematics. As Shoffner and Dockery (2015) note “because math ability is a requirement for further studies in [STEM] … continued interest in and pursuit of mathematics is of crucial importance” (p. 127). Poor high school mathematics preparation can be remediated in college, but it also sets up a barrier to continued success in an STEM college major. Thus, learning experiences in middle school should be geared toward fostering positive math self-efficacy, such as providing positive math performance activities (Navarro, Flores, & Worthington, 2007), positive role models in STEM careers (Ericksen & Schultheiss, 2009), and encouraging parents (especially mothers) to be supportive of math involvement (S. Turner & Lapan, 2002; S. L. Turner, Stewart, & Lapan, 2004).

The fit of the overall SCCT model has been evaluated with racial–ethnic minority middle and high school boys and girls (Fouad & Smith, 1996; Flores, Navarro, & DeWitz, 2008; Garriott et al., 2014; Navarro, et al., 2007; S. Turner & Lapan, 2002). In general, studies found support for parental support and learning experiences as being related to math and science self-efficacy, and for the relationship of math and science self-efficacy and outcome expectations to math/science interests and intentions to pursue STEM goals. Once it was established that the model fits these diverse groups of students, attention turned to critical junctures in which to intervene. Is it best to bolster self-efficacy? Is it best to create positive but realistic outcome expectations? Foster the development of interests? Or is it best to focus on external barriers and supports? Each of these has been studied in an attempt to increase the consideration of an STEM career for girls and underrepresented racial–ethnic minorities.

During high school, individuals are faced with making decisions to pursue advanced coursework in math and science, which prepares them for the next educational level, attending college. In a review of K–12 STEM interventions, Valla and Williams (2012) stated that high school interventions target skill development, which provides a gateway into entering advanced studies in math and science coursework. At this developmental stage, students expand their scope of academic knowledge and face increased challenges in math and science content, building math and science self-efficacy. In fact, this appears to be the optimal way to build self-efficacy. Lopez and Lent (1992) demonstrated the importance of helping students to have successful experiences in math and sciences, as performance accomplishments were found to be the most powerful sources of self-efficacy. In terms of racial–ethnic minorities, Betz (2007) noted that discrepant access to learning experience may account for variance in career choices. In addition, since self-efficacy is related to academic performance and persistence (Lent, Brown, & Larkin, 1984), Moakler and Kim (2014) argued that more attention should be paid to the development of math and science confidence, via positive learning experiences and other sources of efficacy information, to help retain women and racial–ethnic minorities in STEM fields.

Research has also focused on how parents influence outcome expectations and goal intentions of middle school students. Navarro, Flores, and Worthington (2007) examined Mexican American youth’s self-efficacy and outcome expectations toward math/science using a modified SCCT model. In comparison to their male counterparts, Mexican American middle school girls perceived higher social supports for math and science involvement from teachers, peers, and parents. However, despite the support they received, girls expressed less self-efficacy in their math/science skills than did males. In this study, a positive relationship was found between academic accomplishments and self-efficacy for both boys and girls, further supporting the link between success experiences and self-efficacy beliefs. Math and science outcome expectations were shown to be positively associated with self-efficacy in those domains. This research suggests that career development interventions with Mexican American youth should focus on learning experiences at the middle school level because they are predicted to play a large role in the students’ self-efficacy and outcome expectations. Garriott et al. (2014) also found that math/science domain-specific successful accomplishments were associated with high school students’ self-efficacy and outcome expectations in those areas. Interventions at the middle school level may be particularly useful in helping students transition to high school. Earlier research with a largely Hispanic seventh- and eighth-grade sample demonstrated that self-efficacy is directly related to career interests and indirectly related to outcome expectations (Fouad & Smith, 1996).

Other research has also examined the importance of parental, peer, and teacher support in relation to developing self-efficacy, outcome expectations, and interests. Research shows that support from parents is directly related to math/science interests and to self-efficacy and outcome expectations (Ferry, Fouad, & Smith, 2000; S. L. Turner et al., 2004). Studies examining parent support with Native American, Mexican American, African American, and multiethnic middle school students reveal a relationship between self-efficacy and perceived parental support (Alliman-Brissett & Turner, 2010; Navarro et al., 2007; S. Turner & Lapan, 2002). Research has shown a positive association between maternal support and math and science outcome expectations with a group of multiethnic sixth grade students (S. L. Turner et al., 2004). In an examination of parental support, four types of learning experiences, self-efficacy and outcome expectations predicting STEM interests among racial–ethnic high school students, Garriott et al. (2014) found that paths from parental support to performance accomplishments, vicarious learning, and verbal persuasion directly predicted self-efficacy, and performance accomplishments directly predicted outcome expectations. They also found that performance accomplishments mediated the path from parental support to self-efficacy and outcome expectations. The authors suggested that interventions to enhance STEM interests among students of color include parental interventions to help them encourage their children’s math and science accomplishments.

Finally, contextual barriers have been studied to examine how they relate to persistence in pursuing math or science classes. Fouad et al. (2010) examined five types of environmental supports and barriers: parents, school, financial/environmental, social, and individual. They assessed the barriers and supports for middle school, high school, and college students and examined differences by gender and educational level. Using cross-sectional data, they found no gender differences in total number of barriers experienced, but did find differences by educational level. All groups experienced more supports than barriers, but increasingly experienced barriers in math at each level. They also found differences in both barriers and supports for math and science, suggesting that these two areas are not perceived similarly by students. In math, teachers were a support and a barrier, and lack of opportunities and lack of role models were barriers. In science, teachers could be either a support or a barrier, but test anxiety was a barrier, particularly for high school females, and lack of help from parents and having friends not interested in science was perceived as a barrier. Novakovic and Fouad (2012) found that anticipated work–family planning was a barrier for high school girls in considering a more gender nontraditional career.

The research on SCCT with middle and high school students provides rather consistent evidence that successful learning experiences help to promote the development of self-efficacy and outcome expectations and that self-efficacy in math and science is important in career development, specifically around supporting vocational choices, interests, goals, and actions starting in adolescence. The evidence also suggests that interventions to promote math and science career interests with underrepresented racial–ethnic minorities should attempt to build parental supports. Collectively, these studies support Lent et al.’s (1994) social cognitive model that person inputs (e.g., gender and/or race/ethnicity) play a significant role in both self-efficacy and outcome expectations, especially for those in STEM fields. They further suggest that efforts to build self-efficacy and outcome expectations via performance accomplishments, vicarious influences, and parental supports can, to some degree, promote increased math and science interests and intentions among middle and high school girls and racial and ethnic minority students. More research is needed to understand career choices and the intersectionality of contextual factors with developmentally and racially diverse adolescents.

Middle and high school are identified as a critical period of developing self-efficacy in math and science for continued success in college. Empirical evidence indicates a relationship between math and science self-efficacy, social support, and performance accomplishments. Strong support was found for using an SCCT model with middle and high schoolers from underrepresented racial–ethnic minorities within the STEM domain (Fouad & Smith, 1996; Flores, Navarro, & DeWitz, 2008; Garriott et al., 2014; Navarro et al., 2007; S. Turner & Lapan, 2002). Additionally, parents and teachers can be particularly important in supporting and encouraging middle and high school students to enter STEM careers. Skill development during high school is crucial, as the next important juncture is college when major decisions are made for entrance into the STEM field. Future research can build on these findings though examining intersections of race-ethnicity and gender to create interventions to increase science/math-related self-efficacy, outcome expectations, interests, goals, and actions.

College

Once a student successfully completes high school with a goal to pursue an STEM major (and at least a minimum level of high school preparation to do so), several factors predict actually pursuing that goal from an SCCT perspective. Several cross-sectional studies examined the fit of the SCCT interest and choice models among college students. Lent et al. (2005) found support for the model for both freshmen men and women enrolled in Historically Black Colleges and Universities (HBCU) and Predominantly White Colleges and Universities. They assessed whether engineering major and coping self-efficacy, engineering outcome expectations, technical interests, and social supports and barriers predicted choice of major in engineering, concluding that “the present findings suggest that social cognitive variables may be helpful in understanding the educational choice goals of engineering students” (p. 91). Flores et al. (2014) assessed the same constructs (with the same instruments) in a group of engineering students in a Hispanic-serving institution (Latino/Latinas were 42% of the college population). They, too, found support for the model across both genders and White and Latino students. Lent, Lopez, Sheu, and Lopez (2011) tested the model with computing science majors, and found that, for the most part, the model fits the data for men and women, Whites and African Americans, and beginning and advanced students.

Thus, the interest and choice segments of SCCT were supported across genders and in a variety of racial–ethnic groups and in two different STEM majors. In 2013, Lent et al. proposed that the elements of the various segmental models of SCCT could be integrated. The integrative (or holistic) model combines the interest, choice, and performance models with the satisfaction model to predict persistence. They tested the integrated model with over 1,300 freshmen engineering students in four universities (including two HBCUs); 15% of the participant group was African American and 33% were women. As the choice model would predict, they found that interests were predicted by self-efficacy and outcome expectations and that the relationship of social support to interests was mediated by self-efficacy and outcome expectations. The integrative model was also supported in that interests predicted satisfaction, which predicted persistence. However, there was not a direct path from interests to persistence, which the authors noted may indicate “interests may help to draw people toward particular educational/vocational environments; satisfaction with the environment may then be an important part of what keeps them coming back” (p. 28). The integrative model was further tested by Navarro, Flores, Lee, and Gonzalez (2014) in a Hispanic serving institution; this study was also longitudinal, allowing the authors to test the prediction of persistence across a year. For the most part, their findings supported the model and replicated Lent et al.’s (2013) findings that the influence of interest on persistence was mediated by satisfaction.

Lent and his colleagues also did a number of longitudinal studies, testing the integrative model. Lent et al. (2008) and Lent, Sheu, Gloster, and Wilkins (2010) found support for the temporal relationships between self-efficacy, outcome expectations, interests, and goals across two semesters for White and African American engineering students. More recently, Lent et al. (2015) have followed up with the students they first assessed as freshmen to assess adjustment and choice stability over time, using the integrative model. Similar to the other research we have reviewed, overall, the findings supported the SCCT model. Lent et al. (2015) found that self-efficacy was the strongest direct predictor of academic satisfaction and persistence intentions after four semesters and that self-efficacy was linked to academic satisfaction via interests. They also found the model did not differ by gender or race/ethnicity. Lent et al. (2015) studied whether social cognitive variables at Time 1 predicted grades and actual persistence at the end of 3 years, finding direct paths from Time 1 intentions, satisfaction, self-efficacy, and support to persistence at Semester 6. Self-efficacy and Scholastic Aptitude Test scores (objective ability) predicted grades, suggesting that academic ability (in addition to self-efficacy) is related to the grades students attained, but not to persistence.

Although these studies have been large, multi-institution studies to test the entire model at multiple points in time, other studies have focused on one or two of the SCCT constructs for college students. For example, research on a sample taken from the National Center for Education Statistics Longitudinal Study indicates that odds of underrepresented racial–ethnic minority women enrolling in an STEM major increase (1.3 for women and 1.5 for men) if their perception of preparedness for academic success in college is positive (Alhaddab & Alnatheer, 2015). Differences in intersecting identities reveal that White women were more likely to indicate that they plan to pursue an STEM major than racial–ethnic minority women, regardless of their perceptions of preparedness.

Studying a large national database that has collected data on freshmen in 2003, Moakler and Kim (2014) found that math self-confidence and ability and having parents in an STEM career were strong predictors of major choice in STEM disciplines, but women were less likely than men to choose an STEM major. Interestingly, their analysis did not find racial–ethnic differences in STEM major choice. Finally, Herrera and Hurtado (2011) examined factors that predicted maintaining interests in an STEM career from freshmen to senior year, finding that underrepresented racial–ethnic minority students were more likely to maintain their interests in an STEM major if they came to college for a specific career, worked with faculty on research, and had a high grade point average in high school.

Research has also examined relative strengths of various sources of self-efficacy with college student samples (in addition to the research we presented earlier with middle and high school students). Role models have been shown to play an important part in women’s decisions to enter math based fields during college (Ceci, Ginther, Kahn, & Williams, 2015). It has also been demonstrated that performance accomplishments may be significantly related to students’ self-efficacy in math/science (Betz & Schifano, 2000; Luzzo, Hasper, Albert, Bibby, & Martinelli, 1999). For example, Luzzo et al. randomly assigned students to an intervention focused on vicarious learning or on developing performance accomplishments; those in the performance accomplishments group had higher postintervention math self-efficacy. As we noted earlier, Lopez and Lent (1992) also found that performance accomplishments were the strongest source of math self-efficacy.

In addition to having confidence in skills to succeed in an STEM discipline, SCCT would predict that college students must also receive social supports and educational opportunities to strengthen their identity as future scientists. A. M. Byars-Winston and Fouad (2008) found that coping efficacy and parental support positively predicted math and science goals, while career barriers were negative predictors of goals. This may help to explain why underrepresented racial–ethnic minorities are as likely as Whites to major in STEM-related disciplines but are more likely to change majors to non-STEM fields of study due to the influence of perceived barriers (Chubin, May, & Babco, 2005; Culotta, 1992). Another factor may be a sense of belonging, defined as feeling a sense of belonging and being a member of the campus community as a whole. Lacking a sense of belonging has been found to be associated with lower research skill self-efficacy and academic persistence for underrepresented racial–ethnic minority students majoring in engineering and science (Blake-Beard, Bayne, Crosby, & Muller, 2011; Carlone & Johnson, 2007; Foor, Walden, & Trytten, 2007; Hurtado, Cabrera, Lin, Arellano, & Espinosa, 2009; Johnson, 2012).

Interventions to increase STEM participation and persistence have focused on mentorship, learning communities, and precollege programming at the undergraduate and graduate level. Hardin and Longhurst (2016) reported on changes in social cognitive measures after an introductory chemistry class, finding that women had lower self-efficacy and interests than did men and that men had stronger intentions to pursue a career in a science related area than did women. However, other researchers found that some campus programs helped to foster positive self-efficacy and outcome expectations among women and underrepresented students. Hurtado, Cabrera, Lin, Arellano, and Espinosa (2009) found that a structured science program that students viewed as collaborative and empowering was related to students’ stronger science identity and self-efficacy. Szelényi, Denson, and Inkelas (2013) demonstrated that women students attending an STEM-related living and learning community in college had more positive outcome expectations and higher academic self-efficacy than women who did not attend the living and learning community over a 3-year period. Chemers, Zurbriggen, Syed, Goza, and Bearman’s (2011) research with undergraduate and graduate underrepresented racial–ethnic minority students revealed that higher science self-efficacy is related to higher sense of identity as a scientist which, in turn, is related to higher commitment to a science-based career. Their study tested the effectiveness of several support activities with undergraduate and graduate students, including mentoring, community involvement, and hands-on experiences; all were shown to have a positive effect on STEM career commitment with underrepresented racial–ethnic minorities.

Other researchers have examined immersion experiences to better understand ways to increase self-efficacy and outcome expectations. Chaudhary, Coups, Hudson, and Tomlinson-Clark (2015) explored the effectiveness of laboratory versus nonlaboratory research programs to increase self-efficacy and persistence toward an STEM career using pre/postdata with 117 undergraduates. Their findings revealed an interaction between gender and type of intervention. For example, women’s research self-efficacy improved more than did men’s in the laboratory research program. Additionally for women, family support and involvement played a larger role in choosing a scientific career than these variables did for men in the study. Both women and men in the laboratory-based programs versus the nonlaboratory-based programs showed an increase in STEM career interests and choice of STEM careers.

Mentorship is one of the critical factors that can build self-efficacy in STEM disciplines, although not all mentoring relationships are the same. For example, the quality of a mentor–mentee relationship is more important than the frequency of contact (Chemers, Zurbriggen, Syed, Goza, & Bearman, 2011). Blake-Beard, Bayne, Crosby, and Muller (2011) found that having a mentor of one’s own race or gender was seen as important by women and underrepresented racial–ethnic minorities, but that the sex or race of the mentor did not affect academic outcomes. Research mentorship has been tested through a variety of interventions to increase math/science self-efficacy and outcome expectations. A. M. Byars-Winston et al. (2015) tested an adapted SCCT model to assess mentees’ perceptions of research mentorship and how the relationship influenced their postgraduate career choices. They found that career knowledge and research skills had a strong association with participants’ perception of the mentorship effectiveness, mentor effectiveness was related to research self-efficacy, which was related to positive outcomes in pursuing a research career (enrolled in a PhD program). One interpretation of this finding is that mentors assist mentees to better understand their skills and therefore boost self-efficacy in the research skill domain.

In sum, SCCT has been instrumental in investigating undergraduate women and underrepresented minorities’ career interests, choice, and persistence while pursing STEM majors. These studies support the use of the SCCT model, particularly the integrative model, to explain college students’ educational intentions, choices and persistence in math and science coursework regardless of race or gender (Flores et al., 2014; Lent, Lopez, Sheu, & Lopez, 2011; Lent et al., 2013). Perceived preparedness is a factor that increases intentions to pursue an STEM major for racial–ethnic women and men higher than others (Alhaddab & Alnatheer, 2015). Research has also focused on the college climate and belongingness. Underrepresented minorities lacking a sense of belonging are less likely to feel confident in their research skills and persist in STEM majors (Blake-Beard et al., 2011; Carlone & Johnson, 2007; Foor et al., 2007; Hurtado et al., 2009; Johnson, 2012). As such, interventions to increase STEM persistence in college have included learning communities, mentorship, and precollege programming. This research highlights the process of intention and choice for underrepresented minorities in STEM majors. Through continuation of theoretically driven research, we will better understand how these factors facilitate or hinder academic pursuits and how to enhance interventions.

Graduate School and Workforce

It is now a cliché to say that the pipeline of women and underrepresented racial–ethnic minorities entering STEM careers is “leaky.” However, it is still an accurate description of the fact that fewer women and racial–ethnic minorities are preparing for, entering, or remaining in STEM careers at each progressive step. Researchers have begun to focus on factors that promote retention in (and departures from) STEM careers, particularly for women. For example, Nolan, Buckner, Marzabadi, and Kuck (2008) used an SCCT framework to identify male and female chemists’ perceptions of the mentoring they received during their postgraduate schooling. They found that men reported more mentoring at every step of their careers (from undergraduate level and forward). Specifically, men were more likely than women to have learned about research from a professor. Men also had more positive graduate school experiences than did women, including the amount and quality of mentoring they received from their advisors and others during and after graduate school.

Bernstein (2011) designed an online program to help STEM female graduate students build resilience to barriers and develop supports. Developed from an SCCT perspective, the program, CareerWise, was developed to help build skills in working with advisors, balancing work–nonwork domains, negotiating departmental and university climates, and coping with delays in progress toward graduation. Bekki, Smith, Bernstein, and Harrison (2013) reported on the results of a randomized controlled trial of the program, which included a wait-listed control group. They found that the program was an effective intervention to bolster resilience, problem-solving, and coping efficacy.

Most of the postgraduate research has focused on factors related to women’s persistence in STEM careers. It is still important, of course, to understand what promotes entry into an STEM college major, and what helps students to complete an STEM college degree. However, understanding those factors will make little difference if women begin an STEM career only to leave it after a short period of time. Glass, Sassler, Levitte, and Michelmore (2013) followed STEM and non-STEM female college graduates who had participated in the National Longitudinal Survey of Youth from 1979 to 2008. They assessed the 258 women in STEM careers compared to the 842 women in professional/managerial careers. Overall, the women in STEM careers did not differ from those in non-STEM careers on most demographic characteristics (e.g., marriage, having children), but were much more likely to leave their STEM jobs. Half had left an STEM job, while only 20% of those in professional/managerial careers had left their jobs. However, STEM women were not leaving to focus on their families. They stayed in the work force, just no longer in an STEM job or career. Rather, they seemed to gravitate toward careers in non-STEM areas (50%), although 21% moved into managerial careers.

Glass et al.’s (2013) findings are replicated in a number of other studies, particularly with women in engineering. Hunt (2010) found that the gender differences in persistence in STEM careers were almost entirely due to the large movement out of engineering by women, and indeed, two thirds of Glass et al.’s sample started their careers in computing, engineering, or engineering technology. Fouad and Singh (2012) surveyed over 5,000 women who had graduated with a bachelor’s in engineering between 1980 and 2010 and found that 40% had left the field. They found no differences across age cohorts. The loss of women who have prepared themselves to enter these careers is a potential loss to them individually, of course, but, considering the millions of dollars that have been focused on early STEM interventions (CoSTEM, 2011), this also has policy implications as the significant investment in their training has not been realized.

Two teams of researchers used a qualitative approach to understand differences between those who stayed in engineering versus those who left. Using a strengths-based qualitative approach, Buse, Bilimoria, and Perelli (2013) interviewed 31 engineers (10 who had left the field) from an SCCT lens and focused on early learning experiences, self-efficacy, career identity, outcome expectations, and environmental barriers or supports. They found that those who persisted were motivated by challenges they experienced, a sense of identity as an engineer, a perception that the environment supported their having a family, and their adaptability to work in a male dominated field. Those who chose to opt out of the field felt forced out of the profession, could not identify ways to navigate challenges in the workplace, and were married with children. Although this work is not generalizable, it does highlight how workplace structure, and identification with the profession, may influence women engineers’ persistence.

Fouad, Fitzpatrick, and Liu (2011) interviewed 11 former and 14 current women engineers with an interview designed to explicitly uncover environmental supports and barriers that may have influenced them to stay in, or leave, engineering. They found five overall domains captured these women’s experiences: coping with workplace inequities, work–family balance, self-evaluation and identity (including values and commitment to engineering), reasons for leaving engineering (for former engineers), and compromising future advancement (current engineers). The authors noted that self-efficacy, as a distinct construct, did not emerge in their findings, and suggested a larger scale study could examine that more explicitly.

Fouad and Singh and their colleagues (Singh et al., 2013; Fouad, Singh, Cappaert, Chang, & Wan, 2016) included three domains of self-efficacy and outcome expectations in their larger study of women who graduated with a bachelor’s in engineering: engineering tasks, multiple roles, and organizational culture. Singh et al. extended the SCCT choice theory with constructs from turnover theory, proposing that self-efficacy and outcome expectations predict job satisfaction and organizational commitment, which, in turn, predict intentions to leave the profession. They tested the model with over 2,000 women currently working as engineers, finding support for the extended model. Fouad, Singh, Cappaert, Chang, and Wan (2016) examined differences between women who left engineering and those who stayed, finding no differences in self-efficacy, outcome expectations, or interests, but finding differences between the two groups on environmental support in the form of supportive supervisors and supportive organizational policies.

Similar to college, mentoring plays a role in the persistence of women and underrepresented minorities in graduate school and the workforce. Studies show that up to half of the women entering the STEM field after college leave after a short period of time (Fouad & Singh, 2012; Glass, Sassler, Levitte, & Michelmore, 2013; Hunt, 2010). Particular to female graduate students, resilience building and maintaining adequate academic support are effective in helping them persist in graduate school (Bernstein, 2011). Environmental support, self-identity, and work–life balance are factors that can support women’s decisions to persist in STEM occupations (Buse, Bilimoria, & Perelli, 2013; Fouad, Fitzpatrick, & Liu, 2011). It is important to understand more about the factors promoting the retention and persistence of women and underrepresented minorities in graduate education and the workforce. Future research needs to focus specifically on the factors for retention and persistence with more inclusion of underrepresented racial–ethnic minorities.

Conclusion and Recommendations

The studies reviewed in this article demonstrate that the SCCT framework is stable for a wide range of populations in predicting math and science choices. It has been shown to be stable across racial–ethnic minorities and Whites, for both genders, for middle school, high school, and college students, as well as for adults. The stability of the model allows us to turn attention to where to intervene to promote entry for women and racial–ethnic minorities. The second part of our title is “Moving the Needle” because, overall, women and racial–ethnic minorities have continued to be underrepresented in many STEM occupations despite some 40 years of trying to understand why.

In 2009, President Obama established the White House Office of Science and Technology Policy, which published its first report in 2010. Among their findings was that federal funding for research on STEM education is spread across a number of agencies, including the Department of Education and the NSF. Funding goes toward supporting math and science teachers as well as toward research to help understand factors related to lesser entry into STEM careers for women and underrepresented minorities. Many of the larger, multisite, longitudinal studies reviewed earlier were funded through these mechanisms. However, the President’s Council of Advisors on Science and Technology concluded that not only is the funding spread across a number of different agencies, but the funding is not coordinated or strategic. They critiqued the lack of solid evaluation of programs that can then be replicated and scaled to a national level. The Council recommended the creation of a federal coordinating committee, which was established in 2010. Their 2012 report outlined a 5-year plan, which included a number of recommendations to increase interests and opportunities in STEM for children and adults “traditionally underrepresented in STEM.”

Clearly, from an SCCT perspective, we need to continue to focus on distal contextual supports and barriers that promote high math and science self-efficacy and realistic outcome expectations in middle school and early high school. As we noted earlier, the most critical early intervention is to ensure success in math classes that can then serve as a gateway into advanced classes that can prepare students for entry into college and preparation for an STEM career. Research can focus on the environmental barriers that prevent successful experiences in those classes, such as teacher and peer attitudes. Research can help us point to key interventions to help parents and teachers be supportive. Longitudinal and experimental research can also help identify which interventions are most successful. Are some learning experiences better than others at fostering self-efficacy and outcome expectations? Are some learning experiences better at some points than others? And are some learning experiences better for some groups than others? As Shoffner and Dockery (2015) point out, stronger evaluative research is needed on learning experiences overall.

Research with college students shows that the integrated SCCT model fits the data for many groups. Self-efficacy at Time 1 tends to predict satisfaction and persistence in later semesters. We need to turn attention to understanding the key points of intervention to help build students’ self-efficacy beliefs. Are some contextual supports (professors, financial aid, mentors, or research experiences) more important for some groups than others? Are there key intervention points? For example, if it is found that women move out of STEM majors at a particular point in their college tenure, will some interventions effectively prevent this attrition?

The same set of questions apply to women in the workforce, for whom it has been shown that environmental barriers and supports appear to be more highly related to turnover than self-efficacy and outcome expectations (Singh et al., 2013). Are there key points when interventions can make a difference in retention? Do some organizational practices differentially influence women or racial–ethnic minorities at critical junctures? We have argued elsewhere (Singh & Fouad, 2011) that too much focus on retaining women in the engineering workforce has been on “fixing the women” when, instead, the focus should be on environmental barriers and supports.

We have addressed women and racial–ethnic minorities throughout this article, but of course, gender or race/ethnicity is only part of individuals’ identities. More research is needed on what is termed intersectionality—for example, race/ethnicity and gender and how those intersect with social class, acculturation, sexual orientation, ability, and religion, to name only a few other identities. Finally, the best studies we have reviewed in this article are complex and very well done. They were large sample, multisite, longitudinal investigations that used well-validated and domain-specific instruments. It is critical that SCCT researchers emulate these to help us understand how to substantially move the needle to increase women’s and underrepresented minorities’ entry into—and retention within—STEM careers.

Footnotes

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.

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