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Abstract

  1. Top of page
  2. Abstract
  3. Method
  4. Results
  5. Discussion
  6. References
  7. Biographies

This longitudinal study examined academic self-efficacy and performance among Science/Technology/Engineering/Math (STEM) majors who are underrepresented in STEM education and occupations; i.e., women, specific ethnic minorities, and low-socioeconomic status (SES) individuals. Performance on academic tests and self-perceptions of academic skills were assessed at admission and graduation from a STEM mentoring program. At admission, women perceived themselves as academically weaker than men despite similar academic performance. However, by graduation, women's academic self-efficacy was equivalent to men's. In addition, students with double STEM-minority statuses, by ethnicity and SES, had lower academic self-efficacy and performance than d id students with single STEM-minority status. Exploratory analyses of change over time by ethnic/SES groups showed varying patterns of change that depended on the outcome variable. This study's finding of an increase in academic self-efficacy for women and students with STEM-minority status by both ethnicity and SES at graduation from a mentoring program is perhaps an indication of the positive impact of mentoring. The mixed findings at program completion for students with single versus double STEM-minority status call for attention to the complex relationship between social disadvantage, academic self-efficacy, and academic performance.

Women in the United States are underrepresented in many science, technology, engineering, and mathematics (STEM1) educational fields and occupations (e.g., Babco & Bell, 2004; Xie & Shauman, 2003). For example, a National Science Foundation (NSF, 2013) report noted that women earned less than 30% of undergraduate degrees in engineering and computer sciences, even though their participation in most fields has risen and in fact since the late 1990s they received at least 57% of all undergraduate degrees. A similar pattern of STEM underrepresentation is evident for some U.S. ethnic minorities whereby less than 15% of undergraduate degrees in engineering, math, and physical science were earned by African American, Latina/o, or Native American (ALNA2) students (NSF, 2013). There are also indications that socioeconomically disadvantaged students are less likely to major in STEM than students from higher socioeconomic status (SES) families (Shaw & Barbuti, 2010).

Sociocultural factors play a critical role in the limited human diversity found in STEM fields in the United States (Ceci, Williams, & Barnett, 2009). Evidence to support this proposition includes the variability in the proportion of men and women by historical epochs and by country (Valian, 2007). The variability in the demographic diversity across STEM fields within the United States and elsewhere is another cue to the role of cultural factors in STEM participation (Babco & Bell, 2004; NSF, 2013).

Self-efficacy is emerging as one such sociocultural factor (e.g., Shaw & Barbuti, 2010). Academic self-efficacy, defined as confidence in one's ability to accomplish academic tasks, affects educational and occupational interests and expectations. Judgments about oneself, including competence in various domains, entail learning from vicarious experience in a social context as well as verbal persuasion from powerful others. In the case of women and ALNAs, negative stereotypes lower self-assessments of STEM-related abilities as well as their performance in STEM tasks, ultimately compromising STEM educational and occupational aspirations (for a review of issues for women in STEM, see the report by the American Association of University Women [AAUW], 2010). In contrast, STEM self-efficacy and commitment are boosted by the positive persuasion of, and learning experiences with, supportive mentors, particularly for students underrepresented in STEM (Leslie, McClure, & Oaxaca, 1998; Stout, Dasgupta, Hunsinger, & McManus, 2011).

This study focused on the academic self-efficacy and performance of female and male undergraduates in STEM fields as well as those who were low-SES and/or ALNAs. Specifically, this study assessed these students’ academic self-efficacy and performance longitudinally, at entrance to and completion of a STEM mentoring program.

There are many reasons why studying academic self-efficacy and performance in STEM-underrepresented groups is important. The United States needs an abundant and diverse STEM-educated labor force to keep pace with advances in science and technology locally and globally (Xie & Shauman, 2003). Female, ALNA, and low-SES persons stand out as a conspicuous and untapped resource for expanding and diversifying the pool of U.S. STEM professionals (NSF, 2013). Innovation would be enhanced by expanding the participation of diverse individuals in STEM, as noted in the 2010 AAUW report: “With a more diverse workforce, scientific and technological products, services, and solutions are likely to be better designed and more likely to represent all users.” Because STEM occupations are high status and also lucrative, increasing the participation of female, ALNA, and low-SES individuals in STEM education would also expand the social and economic opportunities of individuals from these disadvantaged groups.

Academic Self-Efficacy and Performance

Self-perceived competence is “a pivotal factor in career choice and development” because “unless people believe they can produce desired outcomes by their actions, they have little incentive to act or to persevere in the face of difficulties” (Bandura, Barbaranelli, Caprara, & Pastorelli, 2001, p. 187). Perceived self-efficacy, interest, performance, and persistence in a field stand in reciprocal relationship, reinforcing each other over time (Nauta, Epperson, & Kahn, 1998; Nauta, Kahn, Angell, & Cantarelli, 2002; Zeldin, Britner, & Pajares, 2008). The role of academic self-efficacy may be particularly important in U.S. women's interest and persistence in STEM because in the United States, such careers are considered masculine pursuits (Bernstein & Russo, 2008). Studies find that women who are unsure of their science and math skills are less likely to persist in STEM career paths, as compared to women who are more confident in such skills (AAUW, 2010).

Successful academic performance also predicts interest and persistence in STEM, especially for students who are minorities in STEM. For instance, in one study, high school and college first-year grade point average (GPAs) were the best predictors of educational persistence among students who majored in science or engineering (Mendez, Buskirk, Lohr, & Haag, 2008). In other studies, women's persistence in science majors and careers was associated with high school math grades (Camp, Gilleland, Pearson, & Putten, 2009), college entrance exam scores (e.g., Fassinger, 1990), or college GPA (Camp et al.). Successful academic performance has also been linked to higher level career aspirations among women in STEM majors (Nauta, Epperson, & Kahn, 1998).

Occupational self-efficacy theory (see Betz, 1997) also provides insights into why some U.S. ethnic minorities are underrepresented in STEM. First, limited academic preparation is a significant factor in deterring many ALNAs from pursuing STEM education and occupations (Betz, 1997; Museus, Palmer, Davis, & Maramba, 2011). For instance, lower college GPAs are associated with higher attrition rates from STEM majors as well as lower enrollments in graduate school (e.g., DeBerard, Spielmans, & Julka, 2004). Taking fewer math courses makes one less prepared and less competitive for STEM higher education, which to a significant degree accounts for fewer ALNAs enrolling in STEM graduate programs (for reviews, see Betz, 1997; Poirer, Tanenbaum, Storey, Kirshstein, & Rodriguez, 2009). Second, given that academic performance contributes to academic self-efficacy, minority and low-income students may be doubly at risk (Betz, 1997): They encounter more obstacles to navigating academic milestones that are required for entry into graduate school and STEM occupations, and diminished academic self-efficacy may in turn lead them to avoid STEM courses and occupations (Lent et al., 2005). Academic self-efficacy may also account for the association between successful academic performance and later interest and achievement in STEM (O'Brien, Martinez-Pons, & Kopala, 1999). Thus, diminished confidence and vocational self-perceptions may be obstacles to ALNAs’ entry into STEM occupations, by undermining their academic performance and also by affecting their choice of STEM occupations as well as their persistence in STEM majors.

There is growing recognition that the role of academic self-efficacy in disparities in STEM participation needs to be tested over time and across a diversity of respondents (Shaw & Barbuti, 2010). As noted by Ishitani (2006), a limitation of the extant literature is that few studies used a longitudinal design; even fewer included socioeconomically or ethnically diverse samples (see Leslie et al., 1998). One exception is a longitudinal study that found ethnic minority students’ academic self-efficacy to predict intentions to pursue a scientific career (Estrada, Woodcock, Hernandez, & Schultz, 2011).

Mentoring in Relation to Academic Self-Efficacy and Performance

Mentoring can be a critical source of emotional support, modeling, and guidance that promotes academic engagement and achievement (see Martin & Dowson, 2009) and bolsters confidence (Liang, Tracy, Taylor, & Williams, 2002). Research on college students in STEM consistently finds that women feel more isolated and receive less mentoring than do men (see Burke & Sunal, 2010). Consequently, “women can benefit remarkably from access to support networks” that involve mentoring (Sheffield, 2006, p. 192). Given that loss of confidence may be an important factor in women's decision to drop out of STEM fields (Huang, 2003), and that mentoring relationships often bolster women's self-confidence (Downing, Crosby, & Blake-Beard, 2005; Paglis, Green, & Bauer, 2006), mentoring is often a key feature in programs geared toward supporting women's interest and persistence in STEM (Barton, 2006; Liang et al., 2002). Inadequate mentoring also is an important determinant of the decision by ALNAs not to pursue graduate studies in STEM (see review by Poirer et al., 2009). Based on extensive research on academic self-efficacy, Betz (1997, p. 127) argued that interventions to enhance STEM-minority students’ expectations of efficacy “may be an important buffer to the lack of support or, worse, overt discrimination.”

Some programs to promote the persistence of ALNAs in STEM also feature mentoring as their centerpiece (e.g., Burke & Sunal, 2010). A few of these mentoring programs have been evaluated with comparative longitudinal designs. For example, Wesley Schultz et al. (2011) found that ethnic minorities involved in the Research Initiative for Science Excellence were more likely than a matched comparison group to persist in their intentions to pursue a scientific research career, although the key factor in persistence intentions was undergraduate research experience, not engagement with a mentor. More typically, however, mentoring programs are evaluated with retrospective and/or qualitative methods that are unable to document changes in academic confidence and persistence in STEM.

The Current Study

This study longitudinally examined the academic self-perceptions and performance of students who are underrepresented (i.e., by sex, SES, and/or ethnicity) in STEM disciplines. One gap in the literature on STEM minorities is that it has focused primarily on single rather than on multiple STEM-minority dimensions. By contrast, in this study we examined the academic self-perceptions and performance of STEM students who embodied multiple STEM-minority identities. Also, the students were selected into a mentoring program because of their academic promise. Academically promising STEM-minority students are an important group for researchers interested in academic self-efficacy. As achievers in fields in which they are minorities, these students may offer a window on academic and psychological resilience (e.g., Syed, Azmitia, & Cooper, 2011).

A goal of this study was to explore whether a diversity of socially disadvantaged but academically promising female and male STEM students exhibited the gendered patterns of academic self-efficacy and performance documented in the literature. As described earlier, studies of college students in STEM disciplines have revealed sex differences in academic self-confidence, favoring men, although such differences are less often observed on measures of actual academic performance (e.g., Friedman, 1989). Thus, our first hypothesis was that a gendered pattern of academic self-efficacy and performance would be evident in our sample at entry in the mentoring program.

A second goal was to examine patterns of academic self-efficacy and performance by multiple dimensions of STEM underrepresentation, as called for by Betz (1997). Few studies, most of them qualitative, have been conducted on students with three or more STEM-minority statuses. Therefore, our analyses of the academic performance and self-efficacy of students with three intersecting STEM-minority statuses (by sex, ethnicity, and SES) were exploratory. The literature on ethnic minorities underrepresented in STEM points to limited academic preparation and lower academic self-efficacy as important barriers to their pursuing STEM education (Museus et al., 2011). In addition, low-SES students receive less assistance with school-related tasks due their parents’ limited education, time, and financial resources, and they also have limited access to role models with a college degree (Engle & Tinto, 2008). Thus, we hypothesized that academic self-efficacy and performance would be most compromised in students who were ALNAs and also low SES, as compared to students with only one of those STEM-disadvantaged statuses.

A third goal of this study was to follow our STEM-underrepresented students’ perceived and actual academic performance over time, from admission to graduation from a mentoring program. Based on studies on the mentoring of women in science (e.g., Downing et al., 2005), as well as studies on mentoring's effects on academic confidence (for a review, see Johnson, 2007), we hypothesized that participation in a STEM mentoring program provides a greater boost to women's academic self-efficacy than to men's. We also explored whether students with a double STEM disadvantage, in terms of ethnicity and SES, would show greater improvement in academic self-perceptions and performance between entry into and graduation from a mentoring program, relative to students with either ethnicity or SES STEM disadvantage.

Method

  1. Top of page
  2. Abstract
  3. Method
  4. Results
  5. Discussion
  6. References
  7. Biographies

Participants

The sample for this study was students majoring in STEM disciplines and participating in the McNair Scholars Program at a large (26,500 students), public, Mountain West University. The McNair Program is one of six U.S. Department of Education TRIO Programs aimed at supporting high academic-potential STEM majors who are minorities (U.S. Department of Education, 2002). To be eligible for the program, STEM majors have to be from one or more of the following STEM-underrepresented groups: female; low income, first generation in college; or ALNA. McNair students are also required to have a 3.00 GPA or higher, or demonstrate the potential for achieving that criterion by graduation, and to be in their junior or senior year. The initial sample for this study included 175 students enrolled in the McNair program over a 10-year period. In a typical year, 10–12 participants were enrolled in the fall of their junior year, and 3–6 additional participants were enrolled in the summer of their junior or senior year.

Students in this study were recruited into the McNair Program by two means. The primary (fall) recruitment strategy involved mailings to juniors and seniors who met at least one of the selection criteria as underrepresented students in STEM at the public Mountain West university. A second strategy involved sending invitations for participation in the Summer McNair program to eligible students at nearby colleges and universities. Summer-start McNair scholars were in residence at the main campus for 3 months, and then participated in the McNair Program by means of mentoring and research activities at both their home campus and at the host campus. In terms of program exposure, approximately 80% of McNair scholars completed 2 years in the program and the remainder completed 1 year.

At admission, the participants’ mean age was 23 years (range = 18–47). Most participants described themselves as socioeconomically (SES) disadvantaged (80% were first generation in college, and 80% were from low-income families). First-generation status and low family income were related to one another, ϕ = .59, p < .0001. Given that first-generation status and low-income status were concordant for 88% of the participants, a low-SES category was assigned whether the individual was first generation, low income, or both. Sixty-one percent of respondents were women. Forty-one percent described themselves as Latina/o, 8% African American, 7.4% Native American, and 5.7% as “multiple” or “other” ethnicity; the remainder (38.3%) described themselves as nonLatina/o European American. STEM ethnic-minority status was related to both SES factors: first generation, ϕ = .41, p < .001; and low family income, ϕ = .47, p < .001. To examine the role of intersecting ethnicity and SES statuses, three groups of students were created: (1) ALNA students without low SES (n = 32), (2) students of nonLatino/a European-American descent with low SES (n = 62), and (3) ALNA students with low SES (n = 75 with double STEM-disadvantage status).

Measures

Academic performance was measured in three ways: by means of a measure of critical thinking, via practice Graduate Record Examination (GRE) scores, and via cumulative GPA at graduation.

Critical thinking

The Watson-Glaser Critical Thinking Appraisal (WGCTA; Watson & Glaser, 1994) measures five aspects of critical thinking: inferences, recognition of assumptions, deduction, interpretation, and evaluation of arguments. The 40 items yield a total score with a range from 0 to 40. WGCTA scores are predictive of GPA and achievement and other cognitive test scores (Geisinger, 1994). The WGCTA has adequate alpha and retest reliabilities (for a review, see Geisinger, 1994). In this sample, the internal consistency of the WGCTA was high (α = .94). With regard to validity, in this sample the WGCTA total score was correlated with undergraduate GPA values (r [142)]= .27), and with the practice GRE Verbal (r[108] = .51) and Analytical (r = .53) scores, all p < .001.

GRE scores

GRE scores were obtained via practice tests were administered from one of the published test preparation books, using a timed, group-testing format.

GPA

Cumulative GPA upon graduation was obtained from students’ transcripts.

Academic self-efficacy was measured with Harter's (1992) scale, supplemented by items related to self-perceived academic and study skills developed for this study. Harter's measure provided a general assessment of academic self-efficacy whereas the latter questions focused on skills specific to science and math as well as study strategies.

Self-efficacy

The What I Am Like scale (WIAL; Harter, 1992) is a measure of self-competence for adolescents and young adults. The WIAL was chosen because (i) it assesses multiple domains of self-efficacy (described below), (ii) the Scholastic and Intelligence scales have been used in previous research on academic self-efficacy (e.g., Bouchey & Harter, 2005), and (iii) it also measures the importance of various domains to the individual. The WIAL uses a forced-alternative format in which individuals select one of two statements that best represents their self-appraisals. For example, one item from the Scholastic scale is this: “Some students feel confident that they are mastering their coursework BUT Other students do not feel so confident.” A sample item from the Intelligence scale is: “Some students feel like they are just as smart or smarter than other students BUT Other students wonder if they are as smart.” Once the most descriptive statement is chosen, the respondent decides whether it is sort of true of me or really true of me. Scores can range from 1 to 4, with higher scores reflecting stronger belief in the chosen self-appraisal.

Four WIAL content scales were administered: Acceptance (i.e., self-perceived social skills), Creativity, Scholastic (i.e., mastery of coursework), and Intelligence. In this sample the Scholastic and Intelligence scales were highly correlated (r = .78; p < .0001) and thus were combined into a single score we called Academic Self-Efficacy. This scale is consistent with how Bandura et al. (2001) defined academic self-efficacy in terms of mastery of academic subjects and coursework. In addition, the Global Self-Worth scale assesses general self-concept in relation to being pleased with oneself and liking the kind of person one is. Cronbach's alpha reliabilities exceeded .85 in the standardization sample, and ranged from .76 to .86 in the current sample. Related to construct validity, previous research has found that the academic self-efficacy scales are correlated with grades in math and science as well as self-reflected appraisals from parents and teachers (Bouchey & Harter, 2005).

Self-perceived academic and study skills

Confidence in academic and study skills also was measured via structured and open-ended items developed for this study. Respondents rated themselves as weak (1), average (2), or strong (3) in terms of science and math skills as well as specific study skills. The latter included organization, note taking, test anxiety, and test taking. In addition, participants completed three open-ended questions regarding concerns they had about preparing for graduate school, obstacles to their success in graduate school, and skills and characteristics they thought would help them succeed in graduate school. Content analysis (Weber, 1990) was used to code the open-ended responses. First, two individuals independently coded a random selection of 20 responses to identify themes related to academic weaknesses and strengths. Coding discrepancies were resolved by consensus. Nine obstacles were mentioned, four of which were noted by at least 5% of respondents: finances (57%), competitive admissions (30%), academic skills (26%), and time/stress management (21%). Eight personal strengths related to pursuing graduate school in the future were mentioned; determination or hard work (83%) and research experience (9%) were noted most often. These codes were then used for the remaining responses, with 15 of the responses coded by a different pair of raters to determine interrater reliability (kappa = .86).

Graduate school plans

Information about postgraduate plans was collected via questions developed for this study. These yes/no questions asked whether students had (i) selected a graduate discipline, (ii) applied to graduate programs, (iii) been accepted in graduate programs, and (iv) received graduate school funding.

The pretest battery of measures (including the WGCTA, WIAL, and measures of self-perceived academic and study skills) was group administered in September for fall cohorts and in May for summer cohorts. Posttest measures (including the WGCTA and WIAL) were administered individually within two months of graduation (which coincided with completion of the McNair Program), typically 21 months after students had entered the program. Regardless of their time of entrance to the McNair program, all McNair students completed (i) workshops on technical writing, GRE preparation, and orientation to graduate school; (ii) yearly academic plans for postgraduate work to clarify career goals and skills; (iii) networking activities such as informational interviews with graduate students, visits to other research labs, and McNair conferences; and (iv) a research project with a faculty mentor. Mentors received guidance on how to build supportive relationships with protégés and how to foster protégés’ career development. This study's procedures were approved by our University's Institutional Review Board.

Plan of Analysis

Multivariate ANOVAs were used to test for sex differences and for ethnic/SES differences in academic self-efficacy and academic performance. MANOVA was used to discern “a parsimonious interpretation of a system of outcome variables” (Huberty & Morris, 1989, p. 304). In this study, the indicators of academic performance constituted one system of conceptually interrelated variables, and the measures of academic self-efficacy and confidence in study skills constituted another system. In follow-up univariate tests of hypotheses involving group differences, one-tailed tests were used.

Results

  1. Top of page
  2. Abstract
  3. Method
  4. Results
  5. Discussion
  6. References
  7. Biographies

Preliminary Analyses

Missing data at Time 1

Seven cases were omitted from the analyses because they had incomplete data for at least two of the primary dependent variables: undergraduate GPA, GRE scores, and self-efficacy ratings on the WIAL. Preliminary analyses were conducted, using χ2 and t tests, to determine patterns of missing baseline data because 40% of participants were missing data on at least one dependent variable, primarily self-reported academic and study skills (n = 112 completed). Of the 168 students remaining in the sample, baseline data were complete for 92% of students for GPA (n = 154), 99% for the WIAL and WGCTA (n = 167), and 74% for the GRE (n = 124); 99 participants had complete data for all four of these measures. Analyses revealed a nonrandom pattern of missing data related to the sex of the participant, χ2(1, N = 168) = 5.76, p = .02, with men having more missing data than women on the study skills measure. To maximize statistical power, univariate analyses of group differences were computed separately for (i) the WIAL, (ii) measures of academic performance, and (iii) self-reported academic and study skills. For this reason, the degrees of freedom differ for each group comparison.

Attrition at Time 2

The sample at time of graduation from the McNair Program (Time 2) comprised 48% of the sample at program admission (Time 1). Individuals who participated in the study at Time 1 and Time 2 were similar, on all but two variables, to individuals who completed only Time 1 measures. Students who completed the posttest, compared to those who did not, were significantly lower in self-rated science skills and also believed they had weaker test-taking skills (p < .05). Following procedures described in Miller and Wright (1995), these two variables were entered into a probit regression analysis. The resulting lambda score, which uses a set of variables to estimate the likelihood of the participant having missing Time 2 data, was used as a covariate in analyses of McNair program outcomes. Next, missing data were imputed using a multiple imputation strategy recommended by Graham (2012). We used the fully conditional MCMC method to impute missing data for normally distributed dependent variables. The MCMC method can handle both continuous and categorical predictor variables as well as arbitrary missing data patterns. This method assumes an iterative approach that fits a single variable using all other variables in the model as predictors and then imputes missing data for the single variable being fit. The method continues for each variable in the model to the maximum number of iterations specified, which was 20 in this (SPSS 21.0).

Intersection of sex and SES/ethnicity

Exploratory analyses were conducted to determine if there was an interaction between the STEM-minority status variables, with self-perceptions on the WIAL and the indicators of academic performance as the dependent variables. In each MANOVA, sex and ethnicity-by-SES group were the independent variables. With the set of four WIAL scales as the dependent variable in the first MANOVA, the omnibus Sex by SES/Ethnicity interaction was not significant, F(8,161) = .23. With cumulative GPA and the practice GRE scores as the dependent variables in the second MANOVA, the omnibus Sex by SES/Ethnicity interaction also was not significant, F(8,72) = 1.10. Given the lack of Sex by SES/Ethnicity interaction effects, and the fact that some small cell sizes reduced power to less than .38, subsequent analyses focus on either sex differences or SES-by-ethnic group differences.

Women's and Men's Academic Self-Efficacy and Performance at Program Entry

Given that past studies indicate that women have lower academic self-confidence than men (e.g., Leslie et al., 1998), one-tailed tests were used to examine differences between women and men on measures of academic self-efficacy, creativity, test-taking abilities, test anxiety, and academic preparedness. First, a multivariate ANOVA was conducted with sex of participant as the between subjects effect for Academic Self-Efficacy and on the WIAL as well as the self-perception measures of confidence in study skills and test taking. The main effect of sex was significant, F(5,108) = 8.46, p < .001. The Roy-Bargman step-down tests were significant for Academic Self-Efficacy, ηp2 = .036, and for the items related to self-perceived study and test-taking skills. Female students had significantly lower self-perceptions of their study skills, test-taking skills, and test anxiety than male students (see Table 1); a lower score on test anxiety indicates that it is perceived as an area of weakness. Univariate F tests for the other WIAL scales as well as individual items related to self-perceived academic skills are reported in Table 1. With p set at .01 to correct for the number of tests, female and male students were found to differ in self-perceived Creativity, ηp2 = .056, but had similar perceptions of global self-worth and academic preparedness in science and math.

Table 1. STEM Women's and Men's Academic Performance and Self-Perceptions at Entry into the McNair Mentoring Program
 WomenMen 
 Mean (SD)Mean (SD)F
Note
  1. WGCTA = Watson-Glazer Critical Thinking Appraisal; GRE = Graduate Record Examination practice test; WIAL = What I Am Like scale. Sex differences on the measures of academic performance were tested with two-tailed tests; sex differences on the measures of academic self-perceptions were tested with one-tailed tests.

  2. *p < .05; **p < .01; ***p < .001.

Academic performance   
Critical thinking (WGCTA)29.50 (5.55)29.28 (5.76)n.s.
GRE—verbal460 (90)441 (95)n.s.
GRE—quantitative526 (113)557 (122)n.s.
GRE—analytical503 (143)483 (173)n.s.
Cumulative GPA3.22 (.53)3.27 (.48)n.s.
Academic self-perceptions   
WIAL: academic self-efficacy3.07 (.62)3.35 (.52)8.58**
WIAL: acceptance3.05 (.73)2.96 (.70)n.s.
WIAL: creativity2.83 (.65)3.16 (.68)9.80***
WIAL: global self-worth3.31 (.53)3.28 (.56)n.s.
Academic skills: science2.64 (.54)2.73 (.46)n.s.
Academic skills: math2.27 (.62)2.32 (.67)n.s.
Study skills: organization2.70 (.52)2.43 (.50)5.57*
Study skills: note taking2.64 (.57)2.17 (.70)12.49***
Study skills: test anxiety2.07 (.61)2.52 (.51)4.16***
Study skills: test taking2.21 (.62)2.46 (.79)2.96*

The null hypothesis of no sex difference in academic performance was supported by the omnibus MANOVA testing female-male differences in critical thinking, GRE practice scores, and cumulative GPA at graduation, F(5,77) = .04 (see Table 1). To put these scores in context, the McNair students were in the 30th percentile in critical thinking, the 50th percentile on the GRE-Verbal, and at the 30th percentile on the other two GRE tests.

Responses to open-ended questions about perceived obstacles to pursuing graduate school reinforce the above quantitative findings. Women were more likely than men (39% vs. 13%) to view their academic skills as an obstacle for pursuing graduate school, χ2(1, N = 114) = 3.92, p = .05. A higher proportion of men than women perceived time/stress management as an obstacle (37% vs. 18%), χ2(1, N = 114) = 3.87, p = .05. Finally, women and men were equally likely to mention family obligations as an obstacle (13%) and to perceive determination and hard work (83%) as personal assets for graduate school.

Post hoc tests were conducted to determine whether academic self-efficacy and performance predicted the likelihood that the McNair students would apply to graduate or professional school. Undergraduate GPA was strongly (Cohen's d = 1.62) associated with whether participants had applied to graduate school (M = 3.40) or had not (M = 2.67), t(119) = 5.18, p < .0001. Students who were more confident in their test-taking skills were more likely to apply for postgraduate training, d = .60, t(74) = 1.97, p = .054, as were students who rated themselves as having better time management skills, d = .76, t(74) = 2.08, p = .05. Half of the students who did not apply for postgraduate training stated that it was difficult to find the time to do so. None of the other variables were significantly associated with applying for postgraduate training.

Changes in Academic Self-Efficacy: Sex Differences

Repeated-measures ANOVAs were used to test the hypothesis that women's academic self-efficacy would receive a greater boost, between admission to and graduation from the McNair Program, than would men's. Sex of participant was the between subjects effect and time (admission to and graduation from the McNair Program) was the within subjects factor. The lambda score related to differential attrition was entered as a covariate. A significant main effect for time was found, F(1,164) = 31.74, p < .0001. This effect was qualified by a time-by-sex of participant interaction effect for Academic Self-Efficacy, F(1,164) = 10.97, p = .001; this represents a medium effect size, ηp2 = .062. By graduation, women's academic self-efficacy had increased by .61 SDs but men's had remained stable (see Figure 1). This finding cannot be explained by differential program dosage because women and men attended the same number of workshops (90% vs. 88%, respectively). Also, women and men had similar scores on a test of what they gained from those workshops (78% vs. 76%, respectively), spent the same amount of time meeting with their McNair advisers and working with their research mentors, and received similar scores from their research mentors on their research articles (91% vs. 88%, respectively).

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Figure 1. STEM women's and men's academic self-efficacy at entry into and graduation from the McNair Mentoring Program.

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Self-perceived creativity also increased from program admission to graduation, F(1,164) = 16.81, p < .0001, as did perceived global self-worth, F(1,164) = 19.48, p < .0001. Sex-specific changes were observed on Creativity, F(1,164) = 4.07, p = .045, but not in global self-worth. The increase in self-reported creativity for female students, compared to no change for males, represents a small effect size, ηp2 = .045. No differences by time, sex, or their interaction emerged on the WGCTA measure of critical thinking or on the other WIAL scales.

Academic Self-Efficacy and Performance: Differences by SES and Ethnic Minority Status

MANOVAs with Tukey post hoc tests were used to test the hypothesis that academic self-efficacy and performance favor students with single versus multiple STEM minority statuses. For these analyses, STEM minority status was defined in two ways: being an underrepresented ethnic minority in STEM (ALNA) and low SES. Three groups of students were compared on the baseline measures of academic self-efficacy and performance: (1) typical-SES ALNA students, (2) low-SES nonLatina/o European American students, and (3) low-SES ALNA students (i.e., double STEM-disadvantage status).

The first MANOVA focused on Ethnic Group/SES differences in self-efficacy on the WIAL. The omnibus test revealed a trend, F(8,164) = 1.90, p = .06. The Roy-Bargman step-down test was significant for Academic Self-Efficacy, ηp2 = .054, which is a small effect. Tukey post hoc tests showed that McNair students who had two STEM-disadvantage statuses perceived themselves to be less academically competent on the WIAL scale (see Table 2). The three groups were similar on the measures of self-perceived creativity, acceptance, and global self-worth.

Table 2. Academic Performance and Self-Perceptions of STEM Students at Entry into the McNair Mentoring Program, by Ethnicity and SES
 Ethnic minorityEuropean-american
 Typical SESLow SES 
 Mean (SD)Mean (SD)Mean (SD)F
Note
  1. WGCTA = Watson-Glazer Critical Thinking Appraisal; GRE = Graduate Record Examination practice test; WIAL = What I Am Like scale. Group means that differ significantly, using Tukey post hoc tests (p < .05), are indicated with different subscripts.

  2. *p < .05; **p < .01.

Academic assessments    
Critical thinking (WGCTA)29.03 (5.90)28.22a (5.63)30.89b (5.20)3.92*
GRE—verbal417a (84)423a (88)472b (77)5.02**
GRE—quantitative503a (112)496a (113)565b (105)5.00**
GRE—analytical468b (108)411a (144)513c (138)5.84**
Cumulative GPA3.39b (.37)3.11a (.54)3.31b (.50)4.23**
Self-perceptions    
WIAL: academic self-Efficacy3.38a (.46)3.02b (.61)3.27a (.61)3.78*
WIAL: acceptance3.17 (.87)3.03 (.74)2.93 (.58)n.s.
WIAL: creativity3.07(.62)2.89 (.72)3.01 (.66)n.s.
WIAL: global self-worth3.39 (.55)3.26 (.54)3.31 (.54)n.s.
Academic skills: science2.76 (.44)2.58 (.57)2.69 (.47)n.s.
Academic skills: math2.24 (.63)2.31 (.61)2.26 (.68)n.s.
Study skills: organization2.63 (.50)2.59 (.58)2.64 (.49)n.s.
Study skills: note taking2.68 (.58)2.45 (.70)2.42 (.61)n.s.
Study skills: test anxiety2.00 (.58)2.02 (.70)2.21 (.70)n.s.
Study skills: test taking2.21 (.71)2.11a (.69)2.39b (.61)3.15*

The second MANOVA included the measures of academic performance, for which the omnibus main effect was significant, F(10,71) = 2.85, p = .03. The Roy-Bargman step-down tests were significant for each of the GRE scales, ηp2 = .077 to .115, which represent medium effect sizes, as well as on cumulative GPA, ηp2 = .039, and the WGCTA, ηp2 = .048. The significant difference among the three groups in critical thinking (see Table 2) was because of low-SES ethnic minority students performing significantly worse than low-SES European American students. On the practice GRE, low-SES European American students obtained higher scores than either STEM ethnic minority group on all three subtests (see Table 2), with the low-SES ethnic minority students having Analytical scores that were significantly lower than those of the typical-SES STEM ethnic minority students. Differences in cumulative GPA showed the same pattern as did the GRE scores, with students who had STEM minority statuses by both ethnicity and SES having significantly lower average GPAs than the other two groups.

Related to confidence in specific academic and study skills, the three groups were similar on the measures of science and math skills as well as study skills (see Table 2). However, McNair students with two STEM-disadvantage statuses perceived themselves to be less competent in their test-taking skills (see Table 2), whereas low-SES European American students were most confident on the latter measure. Thus, as hypothesized, students with multiple STEM-disadvantage statuses were significantly lower on multiple indices of academic performance as well as in their academic self-efficacy and confidence in their test-taking skills.

Post hoc analyses were conducted to determine if the three ethnic/SES groups also differed in their persistence in STEM as indicated by application to graduate school. Binary logistic regression was used given that the dependent variable was dichotomous: applied versus did not apply to graduate school. Undergraduate GPA and confidence in test-taking skills were entered as covariates in the first step because in earlier analyses these variables were associated with the decision to apply to graduate school. Two dummy variables were entered in the second step: The first represented single versus multiple STEM-disadvantage status, and the second coded for European American versus STEM ethnic minority. Although the covariates explained significant variance in whether or not students applied to graduate school, R2 = .392, p < .0001, neither dummy variable—representing ethnicity and SES—accounted for additional variance, p > .83.

Changes in Academic Self-Efficacy: Differences by SES and Ethnic Minority Status

Exploratory analyses, using repeated-measures ANOVAs with p set at .005, were conducted to assess whether students with a double STEM disadvantage, in terms of ethnicity and SES, showed greater improvement across time in self-perceptions and academic performance, relative to student with either ethnicity or SES STEM disadvantage. The lambda score related to differential attrition was entered as a covariate. A significant group difference was found in self-reported creativity, F(2, 163) = 6.29, p = .002, ηp2 = .071, with a decline observed for the ALNA typical-SES students but an increase found for the other two groups, substantially so for the European-American/low-SES students (Cohen's d > .52). A different pattern emerged on the measure of critical thinking, F(2, 157) = 5.28, p = .005,with the double STEM-disadvantage students improving by .28 SDs and the other two groups either remaining stable or declining slightly. The effect for critical thinking was medium in size, ηp2 = .061. No differential change was found on the other WIAL scales, GRE scores, or cumulative GPA.

Discussion

  1. Top of page
  2. Abstract
  3. Method
  4. Results
  5. Discussion
  6. References
  7. Biographies

Summary and Limitations

This longitudinal study examined academic self-efficacy and performance among ethnically and SES-diverse women and men at entry into and graduation from a mentoring program for underrepresented STEM majors. In support of our first hypothesis, at admission, women perceived themselves as academically weaker than men even though they had similar academic performance scores. Yet by graduation, women's academic self-efficacy was similar to men's. A different pattern emerged when the data were examined through the lens of SES and ethnicity: At program entry, students with multiple STEM minority statuses had lower scores on every measure of academic performance, compared to peers with a single STEM-disadvantage status. However, double-disadvantage students, compared to their single-disadvantage peers, benefited more from the McNair Program in two areas: critical thinking—a domain of academic performance—and self-perceived creativity.

Several cautions are warranted in interpreting this study's findings. One limitation is that because of inadequate statistical power, we were unable to fully test the intersectionality of sex, ethnicity, and SES. However, exploratory analyses indicated that sex differences are additive rather than multiplicative with ethnic/SES differences. Larger samples of ALNAs would also be helpful in disaggregating the ALNA category in its subgroups. This is because the lives of African American, Latina/o, and Native American individuals are different in ways that are relevant to STEM self-efficacy, such as negative stereotypes and access to role models. Also, future studies exploring the role of ethnicity in STEM self-perceptions and performance should include measures of ethnic identity that are more complex than an ethnic group label. An additional limitation is that the study skills measures were not administered to two cohorts of students, resulting in 35% missing data, with men being more likely to have missing data on this measure than women. Because of these nonrandom patterns of missing data, the findings of this study are suggestive.

Yet another limitation pertains to the measure used to assess academic self-efficacy. Although the What I am Like measure provides a comprehensive picture of college students’ self-perceptions, it is not necessarily the ideal choice for assessing academic self-efficacy given (i) limited evidence of its validity for this purpose and (ii) the relatively high mean scores obtained by this at-risk sample. Furthermore, a measure of self-efficacy that is specific to STEM (e.g., a math self-efficacy measure) may be an especially good choice for future studies of STEM students because self-efficacy is domain-specific (Cordero, Porter, Israel, & Brown, 2010). Additional limitations are that this study did not include a comparison group who did not participate in the mentoring program, and that students self-selected into the mentoring program. For these reasons, it cannot be claimed that the McNair students’ performance and self-evaluations at graduation were the result of participation in the mentoring program. At the same time, our study's outcomes at graduation are consistent with those of other McNair Program evaluation studies (Ishiyama & Hopkins, 2003; Lam, Ugweje, Mawasha, & Srivatsan, 2003). As well, longitudinal follow-ups of minority student participants in the R.I.S.E. program, which used a matched comparison group, have found higher rates of graduation and application to graduate programs in STEM as a result of undergraduate research experiences (Estrada et al., 2011). Future studies of mentoring programs for STEM majors could be strengthened by including a control group (e.g., Larose et al., 2011) or a matched comparison group.

Sex Differences in Academic Self-Efficacy and Performance

This study extends to an ethnically and SES-diverse sample of female and male STEM majors prior observations about gendered patterns of academic self-efficacy and performance. Across studies, women tend to be less positive about their academic skills in comparison to equally able men (for reviews, see Dweck, 2007; Spelke & Grace, 2007). Whether the problem is one of women underestimating their academic skills or men overstating their abilities, the consequences are often more negative for those who are too modest rather than those who are too bold. Simply put, women's lower academic confidence may deter them from persisting in education and careers that could be rewarding for them, personally and financially. As a case in point, in this study, STEM students who were less confident in their academic abilities were less likely to apply for postgraduate training. Similarly, in other studies, those (usually women) who were unsure about their academic abilities or who held the belief that their academic achievement was the result of luck or effort rather than skill were less likely to persist in their educational or career path than those who were confident in their academic skills and/or believed that their academic success was because of their talent (AAUW, 2010; Betz, 1997; Dweck, 2007).

Given the relation between academic self-confidence and persistence (AAUW, 2010), interventions to enhance women's self-efficacy may be critical in supporting their persistence in STEM education, and ultimately for increasing their representation in STEM occupations. The good news is that change is possible. For example, research conducted in the United States by Correll (2001, 2004) suggests that the association of mathematical competence with masculinity negatively influences women's mathematics self-assessment, and raises the standard by which girls and women believe they have to perform to aspire to STEM careers. Correll's research also indicates that when women are aware that their abilities are similar to those of men, they no longer judge themselves by higher standards, and express the same aspirations as men. Therefore, promulgating the message that women and men achieve equally well in STEM, especially when given similar opportunities, is likely to support women's confidence, and also encourages their interest and persistence in STEM education and occupations (AAUW, 2010). Mentoring efforts such as the McNair Program are one of many vehicles for exposing STEM women to accurate information about their capabilities and for boosting their self-efficacy and nurturing their commitment to STEM. Given the pervasive negative messages about women and STEM, interventions to support women's self-efficacy need to be multimodal and sustained.

Ethnic and SES Differences in Academic Self-Efficacy and Performance

This study is unique in its longitudinal examination of the academic self-perceptions and performance of students with STEM-disadvantage status by either ethnicity or SES, or both. Consistent with our hypothesis, the findings showed that at admission to the McNair Program, students with STEM-disadvantaged status by both ethnicity and SES, compared to students with only one of these forms of disadvantage, had significantly lower (i) academic self-efficacy, (ii) test taking skills; and (iii) academic performance as indicated by GRE and critical thinking scores as well as cumulative GPA, with effect sizes between .43 (moderate) and .62 (large). At graduation from the program, students with double-minority status showed improvement on a measure of academic performance (i.e., critical thinking) as well as in self-perceived creativity. By contrast, the higher SES ALNA students in the sample evinced small decreases on these measures. It is possible that these findings are artifacts of regression to the mean. The findings may also represent differential fit between students’ needs and mentoring program's features, depending on the students’ specific disadvantage and identities. Based on theory and research on multiple identities (Shih, Sanchez, & Ho, 2010), it is conceivable that a depression in self-efficacy and performance for students who were STEM-ethnic minorities but not SES disadvantaged might result from increased self-awareness, via participation in the mentoring program and more time spent in predominantly European-American academia, of their stigmatized ethnic identity. For European-descent students from low-SES families, participation in the mentoring program and more time in predominantly European-American academia might have made more salient to them their STEM-empowering European-American identity.

Conclusions

This study documents a boost to women's academic self-efficacy, compared to men's, among academically promising, ethnically and SES-diverse STEM majors enrolled in a McNair mentoring program. The convergent evidence in this study, across measures of academic self-efficacy, replicates previous findings with different populations in women in STEM (e.g., AAUW, 2010; Zeldin et al., 2008), providing support for the argument that sex differences in academic self-confidence contribute to women's underrepresentation in STEM. In addition, our findings are consistent with previous research showing that mentoring approaches may be an especially propitious means of engaging women in science occupations (Liang et al., 2002). Furthermore, students with a double STEM-minority status were significantly lower than their single-minority status peers in both academic performance and confidence, yet gained more from the McNair Program in terms of critical thinking skills, suggesting that comprehensive support programs and policies may need to be enacted, starting in high school if not earlier (e.g., Taylor, Erwin, Ghose, & Perry-Thornton, 2001), to enhance the diversity of professionals in STEM disciplines. This study's mixed findings for students with single versus double STEM-disadvantage statuses call for attention to the complex relationship between social disadvantage, academic self-efficacy, and performance. In particular, the impact of STEM mentoring programs may vary depending on program participants’ specific STEM-minority disadvantage and identities, with implication for the design of STEM mentoring programs and research.

  1. 1

    For the purposes of this article, STEM refers to chemistry, computer science and information technology, geosciences, life science, physics, engineering, and mathematics.

  2. 2

    According to the National Science Foundation (NSF, 2013, p. 2), women, persons with disabilities, and three ethnic groups—African Americans, Latinas/os, and Native Americans—are “underrepresented in science and engineering because they constitute smaller percentages of science and engineering degree recipients and of employed scientists and engineers than they do of the population. Asians are not considered underrepresented because they are a larger percentage of science and engineering degree recipients and of employed scientists and engineers than they are of the population.”

References

  1. Top of page
  2. Abstract
  3. Method
  4. Results
  5. Discussion
  6. References
  7. Biographies
  • American Association of University Women (AAUW) (2010). Why so few? Women in science, technology, engineering and mathematics. Washington, DC: AAUW.
  • Babco, E. L., & Bell, N. E. (2004). Professional women and minorities: A total human resources data compendium (15th ed.). Washington, DC: Commission on Professionals in Science and Technology.
  • Bandura, A. (1986). Social foundations of thought and action; A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.
  • Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (2001). Self-efficacy beliefs as shapers of children's aspirations and career trajectories. Child Development, 72, 187206. doi/10.1111/1467–8624.00273
  • Barton, A. C. (2006). Engaging girls in science. In C. Skelton, B. Francis, & L. Smulyan (Eds.), The Sage handbook of gender and education (pp. 211235). Thousand Oaks, CA: Sage.
  • Bernstein, B. L., & Russo, N. F. (2008). Explaining too few women in STEM careers: A psychosocial perspective. In M. A. Paludi (Ed.), The psychology of women at work: Challenges and solutions for our female workforce, Vol. 2: Obstacles and the identity juggle (pp. 133). Westport, CT: Praeger/Greenwood.
  • Betz, N. (1997). What stops women and minorities from choosing and completing majors in science and engineering? In D. Johnson (Ed.), Minorities and girls in school: Effects on achievement and performance (pp. 105140). Thousand Oaks, CA: Sage.
  • Bouchey, H. A., & Harter, S. (2005). Reflected appraisals, academic self-perceptions, and math/science performance during early adolescence. Journal of Educational Psychology, 97, 673686. doi:10.1037/0022-0663.97.4.67
  • Burke, B. A., & Sunal, D. W. (2010). A framework to support Hispanic undergraduate women in STEM majors. In D. W. Sunal, C. S. Sunal, & E. L. Wright (Eds.), Teaching science with Hispanic ELLs in K–16 classrooms (pp. 273312). Greenwich, CT: IAP Information Age.
  • Camp, A. G., Gilleland, D., Pearson, C., & Putten, J. V. (2009). Women's path into science and engineering majors: A structural equation model. Educational Research and Evaluation, 15, 6377. doi: 10.1080/13803610802591725.
  • Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women's underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135, 218261. doi: 10.1037/a0014412; 10.1037/a0014412.supp.
  • Cordero, E. D., Porter, S. H., Israel, T., & Brown, M. T. (2010). Math and science pursuits; A self-efficacy intervention comparison study. Journal of Career Assessment, 18, 363375. doi: 10.1177/1069072710374572.
  • Correll, S. J. (2001). Gender and the career choice process: The role of biased self-assessment. American Journal of Sociology, 106, 16911730. doi: 10.1086/321299.
  • Correll, S. J. (2004). Constraints into preferences: Gender, status, and emerging career aspirations. American Sociological Review, 69, 93113.
  • DeBerard, M., Spielmans, G., & Julka, D. (2004). Predictors of academic achievement and retention among college freshmen: A longitudinal study. College Student Journal, 38, p. 6680.
  • Downing, R. A., Crosby, F. J., & Blake-Beard, S. (2005). The perceived importance of developmental relationships on women undergraduates’ pursuit of science. Psychology of Women Quarterly, 29, 419426. doi: 10.1111/j.1471–6402.2005.00242.x.
  • Dweck, C. S. (2007). Is math a gift? Beliefs that put females at risk. In S. J. Ceci & W. M. Williams (Eds.), Why aren't more women in science? Top researchers debate the evidence (pp. 4756). Washington, D.C.: American Psychological Association.
  • Engle, J., & Tinto, V. (2008). Beyond access: College success for low income, first generation students. Washington, DC: Pell Institute.
  • Estrada, M., Woodcock, A., Hernandez, P. R., & Schultz, P. (2011). Toward a model of social influence that explains minority student integration into the scientific community. Journal of Educational Psychology, 103, 206222. doi:10.1037/a0020743.
  • Fassinger, R. E. (1990). Causal models of career choice in two samples of college women. Journal of Vocational Behavior, 36, 225248 doi: 10.1016/0001-8791(90)90029-2.
  • Friedman, L. (1989). Mathematics and the gender gap: A meta-analysis of recent studies on sex differences in mathematical tasks. Review of Educational Research, 59, 185213 doi: 10.2307/1170414.
  • Geisinger, K. F. (1994). Review of the Watson-Glaser critical thinking appraisal, Form S. Mental Measurements Yearbook, 13, 11211124.
  • Graham, J. W. (2012). Missing data: analysis and design. New York: Springer.
  • Harter, S. (1992). Visions of self: Beyond the me in the mirror. In J. E. Jacobs (Ed.), Developmental perspectives on motivation (pp. 99144). Lincoln, NE: University of Nebraska.
  • Huang, A. S. (2003). Confidence or arrogance? AWIS Magazine, 32(4), 69.
  • Huberty, C. J., & Morris, J. D. (1989). Multivariate analysis versus multiple univariate analyses. Psychological Bulletin, 105(2), 302308 doi:10.1037/0033–2909.105.2.3.
  • Ishitani, T. T. (2006). Studying attrition and degree completion behavior among first-generation college students in the United States. Journal of Higher Education, 77, 861885.
  • Ishiyama, J. T., & Hopkins, V. M. (2003). Assessing the impact of a graduate school preparations program on first-generation, low-income college students at a public liberal arts university. Journal of College Student Retention, 4, 393405.
  • Johnson, W. B. (2007). Student-faculty mentorship outcomes. In T. D. Allen & L. T. Eby (Eds.), The Blackwell handbook of mentoring: A multiple perspectives approach (pp. 189210). Malden, MA: Blackwell.
  • Lam, P. C., Ugweje, O., Mawasha, P. R., & Srivatsan, T. S. (2003). An assessment of the effectiveness of the McNair Program at the University of Akron. Journal of Women and Minorities in Science and Engineering, 9, 7086.
  • Larose, S., Cyrenne, D., Garceau, O., Harvey, M., Guay, F., Godin, F., … & Deschênes, C. (2011). Academic mentoring and dropout prevention for students in math, science and technology. Mentoring and Tutoring: Partnership in Learning, 19, 419439.
  • Lent, R., Brown, S., Sheu, H., Schmidt, J., Brenner, B., Gloster, C., & Treistman, D. (2005). Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically Black universities. Journal of Counseling Psychology, 52, 8492. doi: 10.1037/0022-0167.52.1.84.
  • Leslie, L. L., McClure, G. T., & Oaxaca, R. L. (1998). Women and minorities in science and engineering: A life sequence analysis. Journal of Higher Education, 69, 239276.
  • Liang, B., Tracy, A. J., Taylor, C. A., & Williams, L. M. (2002). Mentoring college-age women: A relational approach. American Journal of Community Psychology, 30, 271288. doi: 10.1023/A:1014637112531.
  • Martin, A. J., & Dowson, M. (2009). Interpersonal relationships, motivation, engagement, and achievement: Yields for theory, current issues, and educational practice. Review of Educational Research, 79, 327365. doi: 10.3102/0034654308325583.
  • Mendez, G., Buskirk, T. D., Lohr, S., & Haag, S. (2008). Factors associated with persistence in science and engineering majors: An exploratory study using classification trees and random forests. Journal of Engineering Education, 97, 5770.
  • Miller, R. B., & Wright, D. W. (1995). Detecting and correcting attrition bias in longitudinal family research. Journal of Marriage and the Family, 57, 921929. doi: 10.2307/353412.
  • Museus, S., Palmer, R. T., Davis, R. J., & Maramba, D. C. (2011). Racial and ethnic minority students’ success in STEM education. Hoboken, NJ: Jossey-Bass.
  • National Science Foundation, National Center for Science and Engineering Statistics. (2013). Women, minorities, and persons with disabilities in science and engineering: 2013. Special report NSF 13–304. Arlington, VA: author. Retrieved from www.nsf.gov/statistics/wmpd/2013/pdf/nsf13304_digest.pdf
  • Nauta, M. M., Epperson, D. L., & Kahn, J. H. (1998). A multiple-groups analysis of predictors of higher level career aspirations among women in mathematics, science, and engineering majors. Journal of Counseling Psychology, 45, 483496. doi: 10.1037/0022-0167.45.4.483.
  • Nauta, M. M., Kahn, J. H., Angell, W.W., & Cantarelli, E. A. (2002). Identifying the antecedent in the relation between career interests and self-efficacy: It is one, the other, or both? Journal of Counseling Psychology, 49, 290301. doi: 10.1037/0022-0167.49.3.290.
  • O'Brien, V., Martinez-Pons, M., & Kopala, M. (1999). Mathematics self-efficacy, ethnic identity, gender, and career interests related to mathematics and science. Journal of Educational Research, 92, 231235. doi: 10.1080/00220679909597600.
  • Paglis, L. L., Green, S. G., & Bauer, T. N. (2006). Does adviser mentoring add value? A longitudinal study of mentoring and doctoral student outcomes. Research in Higher Education, 47, 451476. doi: 10.1007/s11162-005-9003-2.
  • Poirer, J. M., Tanenbaum, C., Storey, C., Kirshstein, R., & Rodriguez, C. (2009). The road to the STEM professoriate for underrepresented minorities: A review of the literature. Report to the National Science Foundation, Alliances for Graduate Education and the Professoriate. Retrieved from http://www.air.org/files/AGEP_Lit_Review_10–26–09.pdf
  • Shaw, E. J., & Barbuti, S. (2010). Patterns of persistence in intended college major with a focus on STEM majors. NACADA Journal, 30(2), 1934.
  • Sheffield, S. L.-M. (2006). Women and science: Social impact and interaction. New Brunswick, NJ: Rutgers University.
  • Shih, M., Sanchez, D. T., & Ho, G. C. (2010). Costs and benefits of switching among multiple social identities. In R. J. Crisp (Ed.), The psychology of social and cultural diversity (pp. 6283). New York: Wiley-Blackwell.
  • Spelke, E. S., & Grace, A. D. (2007). Sex, math, and science. In S. J. Ceci & W. M. Williams (Eds.), Why aren't more women in science? Top researchers debate the evidence (pp. 5767). Washington, D.C.: American Psychological Association.
  • Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011). STEMing the tide: Using ingroup experts to inoculate women's self-concept in science, technology, engineering, and mathematics (STEM). Journal of Personality and Social Psychology, 100, 255270. doi: 10.1037/a0021385.
  • Syed, M., Azmitia, M., & Cooper, C. R. (2011). Identity and academic success among underrepresented ethnic minorities: An interdisciplinary review and integration. Journal of Social Issues, 67, 442468. doi: 10.1111/j.1540-4560.2011.01709.x.
  • Taylor, V., Erwin, K., Ghose, M., & Perry-Thornton, E. (2001). Models to increase enrollment of minority females in science-based careers. Journal of the National Medical Association, 93, 7477.
  • U.S. Department of Education. (2002). A profile of the Ronald E. McNair Postbaccalaureate Achievement Program: 1999–2000. Washington, DC: Office of Postsecondary Education.
  • Valian, V. (2007). Women at the top in science – and elsewhere. In S. J. Ceci, & W. M. Williams (Eds.), Why aren't more women in science? Top researchers debate the evidence (pp. 4756). Washington, D.C.: American Psychological Association.
  • Watson, G. B., & Glaser, E. M. (1994). Watson-Glaser Critical Thinking Appraisal Form S manual. San Antonio, TX: Harcourt Brace.
  • Weber, R. P. (1990). Basic content analysis (2nd ed.). Newbury Park, CA: Sage.
  • Wesley Schultz, P. P., Hernandez, P. R., Woodcock, A., Estrada, M., Chance, R. C., Aguilar, M., & Serpe, R. T. (2011). Patching the pipeline: Reducing educational disparities in the sciences through minority training programs. Educational Evaluation and Policy Analysis, 33, 95114. doi:10.3102/0162373710392371.
  • Xie, Y., & Shauman, K. (2003). Women in science. Cambridge, MA: Harvard University.
  • Zeldin, A. L., Britner, S. L., & Pajares, F. (2008). A comparative study of the self-efficacy beliefs of successful men and women in mathematics, science, and technology careers. Journal of Research in Science Teaching, 45, 10361058. doi: 10.1002/tea.20195.

Biographies

  1. Top of page
  2. Abstract
  3. Method
  4. Results
  5. Discussion
  6. References
  7. Biographies
  • DAVID MACPHEE, Department of Human Development and Family Studies, Colorado State University.

  • SAMANTHA FARRO is now at the Veterans Integrated Service Network 19, Mental Illness Research Education and Clinical Center, Denver VA Medical Center.

  • SILVIA SARA CANETTO, Department of Psychology, Colorado State University.