Skip to main content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Proc Natl Acad Sci U S A. 2012 Oct 9; 109(41): 16474–16479.
Published online 2012 Sep 17. doi: 10.1073/pnas.1211286109
PMCID: PMC3478626
PMID: 22988126

Science faculty’s subtle gender biases favor male students

Associated Data

Supplementary Materials

Abstract

Despite efforts to recruit and retain more women, a stark gender disparity persists within academic science. Abundant research has demonstrated gender bias in many demographic groups, but has yet to experimentally investigate whether science faculty exhibit a bias against female students that could contribute to the gender disparity in academic science. In a randomized double-blind study (n = 127), science faculty from research-intensive universities rated the application materials of a student—who was randomly assigned either a male or female name—for a laboratory manager position. Faculty participants rated the male applicant as significantly more competent and hireable than the (identical) female applicant. These participants also selected a higher starting salary and offered more career mentoring to the male applicant. The gender of the faculty participants did not affect responses, such that female and male faculty were equally likely to exhibit bias against the female student. Mediation analyses indicated that the female student was less likely to be hired because she was viewed as less competent. We also assessed faculty participants’ preexisting subtle bias against women using a standard instrument and found that preexisting subtle bias against women played a moderating role, such that subtle bias against women was associated with less support for the female student, but was unrelated to reactions to the male student. These results suggest that interventions addressing faculty gender bias might advance the goal of increasing the participation of women in science.

Keywords: diversity, lifestyle choices, science education, science workforce

A 2012 report from the President’s Council of Advisors on Science and Technology indicates that training scientists and engineers at current rates will result in a deficit of 1,000,000 workers to meet United States workforce demands over the next decade (1). To help close this formidable gap, the report calls for the increased training and retention of women, who are starkly underrepresented within many fields of science, especially among the professoriate (24). Although the proportion of science degrees granted to women has increased (5), there is a persistent disparity between the number of women receiving PhDs and those hired as junior faculty (14). This gap suggests that the problem will not resolve itself solely by more generations of women moving through the academic pipeline but that instead, women’s advancement within academic science may be actively impeded.

With evidence suggesting that biological sex differences in inherent aptitude for math and science are small or nonexistent (68), the efforts of many researchers and academic leaders to identify causes of the science gender disparity have focused instead on the life choices that may compete with women’s pursuit of the most demanding positions. Some research suggests that these lifestyle choices (whether free or constrained) likely contribute to the gender imbalance (911), but because the majority of these studies are correlational, whether lifestyle factors are solely or primarily responsible remains unclear. Still, some researchers have argued that women’s preference for nonscience disciplines and their tendency to take on a disproportionate amount of child- and family-care are the primary causes of the gender disparity in science (911), and that it “is not caused by discrimination in these domains” (10). This assertion has received substantial attention and generated significant debate among the scientific community, leading some to conclude that gender discrimination indeed does not exist nor contribute to the gender disparity within academic science (e.g., refs. 12 and 13).

Despite this controversy, experimental research testing for the presence and magnitude of gender discrimination in the biological and physical sciences has yet to be conducted. Although acknowledging that various lifestyle choices likely contribute to the gender imbalance in science (911), the present research is unique in investigating whether faculty gender bias exists within academic biological and physical sciences, and whether it might exert an independent effect on the gender disparity as students progress through the pipeline to careers in science. Specifically, the present experiment examined whether, given an equally qualified male and female student, science faculty members would show preferential evaluation and treatment of the male student to work in their laboratory. Although the correlational and related laboratory studies discussed below suggest that such bias is likely (contrary to previous arguments) (911), we know of no previous experiments that have tested for faculty bias against female students within academic science.

If faculty express gender biases, we are not suggesting that these biases are intentional or stem from a conscious desire to impede the progress of women in science. Past studies indicate that people’s behavior is shaped by implicit or unintended biases, stemming from repeated exposure to pervasive cultural stereotypes (14) that portray women as less competent but simultaneously emphasize their warmth and likeability compared with men (15). Despite significant decreases in overt sexism over the last few decades (particularly among highly educated people) (16), these subtle gender biases are often still held by even the most egalitarian individuals (17), and are exhibited by both men and women (18). Given this body of work, we expected that female faculty would be just as likely as male faculty to express an unintended bias against female undergraduate science students. The fact that these prevalent biases often remain undetected highlights the need for an experimental investigation to determine whether they may be present within academic science and, if so, raise awareness of their potential impact.

Whether these gender biases operate in academic sciences remains an open question. On the one hand, although considerable research demonstrates gender bias in a variety of other domains (1923), science faculty members may not exhibit this bias because they have been rigorously trained to be objective. On the other hand, research demonstrates that people who value their objectivity and fairness are paradoxically particularly likely to fall prey to biases, in part because they are not on guard against subtle bias (24, 25). Thus, by investigating whether science faculty exhibit a bias that could contribute to the gender disparity within the fields of science, technology, engineering, and mathematics (in which objectivity is emphasized), the current study addressed critical theoretical and practical gaps in that it provided an experimental test of faculty discrimination against female students within academic science.

A number of lines of research suggest that such discrimination is likely. Science is robustly male gender-typed (26, 27), resources are inequitably distributed among men and women in many academic science settings (28), some undergraduate women perceive unequal treatment of the genders within science fields (29), and nonexperimental evidence suggests that gender bias is present in other fields (19). Some experimental evidence suggests that even though evaluators report liking women more than men (15), they judge women as less competent than men even when they have identical backgrounds (20). However, these studies used undergraduate students as participants (rather than experienced faculty members), and focused on performance domains outside of academic science, such as completing perceptual tasks (21), writing nonscience articles (22), and being evaluated for a corporate managerial position (23).

Thus, whether aspiring women scientists encounter discrimination from faculty members remains unknown. The formative predoctoral years are a critical window, because students’ experiences at this juncture shape both their beliefs about their own abilities and subsequent persistence in science (30, 31). Therefore, we selected this career stage as the focus of the present study because it represents an opportunity to address issues that manifest immediately and also resurface much later, potentially contributing to the persistent faculty gender disparity (32, 33).

Current Study

In addition to determining whether faculty expressed a bias against female students, we also sought to identify the processes contributing to this bias. To do so, we investigated whether faculty members’ perceptions of student competence would help to explain why they would be less likely to hire a female (relative to an identical male) student for a laboratory manager position. Additionally, we examined the role of faculty members’ preexisting subtle bias against women. We reasoned that pervasive cultural messages regarding women’s lack of competence in science could lead faculty members to hold gender-biased attitudes that might subtly affect their support for female (but not male) science students. These generalized, subtly biased attitudes toward women could impel faculty to judge equivalent students differently as a function of their gender.

The present study sought to test for differences in faculty perceptions and treatment of equally qualified men and women pursuing careers in science and, if such a bias were discovered, reveal its mechanisms and consequences within academic science. We focused on hiring for a laboratory manager position as the primary dependent variable of interest because it functions as a professional launching pad for subsequent opportunities. As secondary measures, which are related to hiring, we assessed: (i) perceived student competence; (ii) salary offers, which reflect the extent to which a student is valued for these competitive positions; and (iii) the extent to which the student was viewed as deserving of faculty mentoring.

Our hypotheses were that: Science faculty’s perceptions and treatment of students would reveal a gender bias favoring male students in perceptions of competence and hireability, salary conferral, and willingness to mentor (hypothesis A); Faculty gender would not influence this gender bias (hypothesis B); Hiring discrimination against the female student would be mediated (i.e., explained) by faculty perceptions that a female student is less competent than an identical male student (hypothesis C); and Participants’ preexisting subtle bias against women would moderate (i.e., impact) results, such that subtle bias against women would be negatively related to evaluations of the female student, but unrelated to evaluations of the male student (hypothesis D).

Results

A broad, nationwide sample of biology, chemistry, and physics professors (n = 127) evaluated the application materials of an undergraduate science student who had ostensibly applied for a science laboratory manager position. All participants received the same materials, which were randomly assigned either the name of a male (n = 63) or a female (n = 64) student; student gender was thus the only variable that differed between conditions. Using previously validated scales, participants rated the student’s competence and hireability, as well as the amount of salary and amount of mentoring they would offer the student. Faculty participants believed that their feedback would be shared with the student they had rated (see Materials and Methods for details).

Student Gender Differences.

The competence, hireability, salary conferral, and mentoring scales were each submitted to a two (student gender; male, female) × two (faculty gender; male, female) between-subjects ANOVA. In each case, the effect of student gender was significant (all P < 0.01), whereas the effect of faculty participant gender and their interaction was not (all P > 0.19). Tests of simple effects (all d > 0.60) indicated that faculty participants viewed the female student as less competent [t(125) = 3.89, P < 0.001] and less hireable [t(125) = 4.22, P < 0.001] than the identical male student (Fig. 1 and Table 1). Faculty participants also offered less career mentoring to the female student than to the male student [t(125) = 3.77, P < 0.001]. The mean starting salary offered the female student, $26,507.94, was significantly lower than that of $30,238.10 to the male student [t(124) = 3.42, P < 0.01] (Fig. 2). These results support hypothesis A.

An external file that holds a picture, illustration, etc.
Object name is pnas.1211286109fig01.jpg

Competence, hireability, and mentoring by student gender condition (collapsed across faculty gender). All student gender differences are significant (P < 0.001). Scales range from 1 to 7, with higher numbers reflecting a greater extent of each variable. Error bars represent SEs. nmale student condition = 63, nfemale student condition = 64.

Table 1.

Means for student competence, hireability, mentoring and salary conferral by student gender condition and faculty gender

Male target student
Female target student
Male faculty
Female faculty
Male faculty
Female faculty
VariableMeanSDMeanSDMeanSDMeanSDd
Competence4.01a(0.92)4.1a(1.19)3.33b(1.07)3.32b(1.10)0.71
Hireability3.74a(1.24)3.92a(1.27)2.96b(1.13)2.84b(0.84)0.75
Mentoring4.74a(1.11)4.73a(1.31)4.00b(1.21)3.91b(0.91)0.67
Salary30,520.83a(5,764.86)29,333.33a(4,952.15)27,111,11b(6,948.58)25,000.00b(7,965.56)0.60

Scales for competence, hireability, and mentoring range from 1 to 7, with higher numbers reflecting a greater extent of each variable. The scale for salary conferral ranges from $15,000 to $50,000. Means with different subscripts within each row differ significantly (P < 0.05). Effect sizes (Cohen’s d) represent target student gender differences (no faculty gender differences were significant, all P > 0.14). Positive effect sizes favor male students. Conventional small, medium, and large effect sizes for d are 0.20, 0.50, and 0.80, respectively (51). nmale student condition = 63, nfemale student condition = 64. ***P < 0.001.

An external file that holds a picture, illustration, etc.
Object name is pnas.1211286109fig02.jpg

Salary conferral by student gender condition (collapsed across faculty gender). The student gender difference is significant (P < 0.01). The scale ranges from $15,000 to $50,000. Error bars represent SEs. nmale student condition = 63, nfemale student condition = 64.

In support of hypothesis B, faculty gender did not affect bias (Table 1). Tests of simple effects (all d < 0.33) indicated that female faculty participants did not rate the female student as more competent [t(62) = 0.06, P = 0.95] or hireable [t(62) = 0.41, P = 0.69] than did male faculty. Female faculty also did not offer more mentoring [t(62) = 0.29, P = 0.77] or a higher salary [t(61) = 1.14, P = 0.26] to the female student than did their male colleagues. In addition, faculty participants’ scientific field, age, and tenure status had no effect (all P > 0.53). Thus, the bias appears pervasive among faculty and is not limited to a certain demographic subgroup.

Mediation and Moderation Analyses.

Thus far, we have considered the results for competence, hireability, salary conferral, and mentoring separately to demonstrate the converging results across these individual measures. However, composite indices of measures that converge on an underlying construct are more statistically reliable, stable, and resistant to error than are each of the individual items (e.g., refs. 34 and 35). Consistent with this logic, the established approach to measuring the broad concept of target competence typically used in this type of gender bias research is to standardize and average the competence scale items and the salary conferral variable to create one composite competence index, and to use this stable convergent measure for all analyses (e.g., refs. 36 and 37). Because this approach obscures mean salary differences between targets, we chose to present salary as a distinct dependent variable up to this point, to enable a direct test of the potential discrepancy in salary offered to the male and female student targets. However, to rigorously examine the processes underscoring faculty gender bias, we reverted to standard practices at this point by averaging the standardized salary variable with the competence scale items to create a robust composite competence variable (α = 0.86). This composite competence variable was used in all subsequent mediation and moderation analyses.

Evidence emerged for hypothesis C, the predicted mediation (i.e., causal path; see SI Materials and Methods: Additional Analyses for more information on mediation and the results of additional mediation analyses). The initially significant impact of student gender on hireability (β = −0.35, P < 0.001) was reduced in magnitude and dropped to nonsignificance (β = −0.10, P = 0.13) after accounting for the impact of student composite competence (which was a strong predictor, β = 0.69, P < 0.001), Sobel’s Z = 3.94, P < 0.001 (Fig. 3). This pattern of results provides evidence for full mediation, indicating that the female student was less likely to be hired than the identical male because she was viewed as less competent overall.

An external file that holds a picture, illustration, etc.
Object name is pnas.1211286109fig03.jpg

Student gender difference hiring mediation. Values are standardized regression coefficients. The value in parentheses reflects a bivariate analysis. The dashed line represents the mediated path. The composite student competence variable consists of the averaged standardized salary variable and the competence scale items. Student gender is coded such that male = 0, female = 1. nmale student condition = 63, nfemale student condition = 64. ***P < 0.001.

We also conducted moderation analysis (i.e., testing for factors that could amplify or attenuate the demonstrated effect) to determine the impact of faculty participants’ preexisting subtle bias against women on faculty participants’ perceptions and treatment of male and female science students (see SI Materials and Methods: Additional Analyses for more information on and the results of additional moderation analyses). For this purpose, we administered the Modern Sexism Scale (38), a well-validated instrument frequently used for this purpose (SI Materials and Methods). Consistent with our intentions, this scale measures unintentional negativity toward women, as contrasted with a more blatant form of conscious hostility toward women.

Results of multiple regression analyses indicated that participants’ preexisting subtle bias against women significantly interacted with student gender to predict perceptions of student composite competence (β = −0.39, P < 0.01), hireability (β = −0.31, P < 0.05), and mentoring (β = −0.55, P < 0.001). To interpret these significant interactions, we examined the simple effects separately by student gender. Results revealed that the more preexisting subtle bias participants exhibited against women, the less composite competence (β = −0.36, P < 0.01) and hireability (β = −0.39, P < 0.01) they perceived in the female student, and the less mentoring (β = −0.53, P < 0.001) they were willing to offer her. In contrast, faculty participants’ levels of preexisting subtle bias against women were unrelated to the perceptions of the male student’s composite competence (β = 0.16, P = 0.22) and hireability (β = 0.07, P = 0.59), and the amount of mentoring (β = 0.22, P = 0.09) they were willing to offer him. [Although this effect is marginally significant, its direction suggests that faculty participants’ preexisting subtle bias against women may actually have made them more inclined to mentor the male student relative to the female student (although this effect should be interpreted with caution because of its marginal significance).] Thus, it appears that faculty participants’ preexisting subtle gender bias undermined support for the female student but was unrelated to perceptions and treatment of the male student. These findings support hypothesis D.

Finally, using a previously validated scale, we also measured how much faculty participants liked the student (see SI Materials and Methods). In keeping with a large body of literature (15), faculty participants reported liking the female (mean = 4.35, SD = 0.93) more than the male student [(mean = 3.91, SD = 0.1.08), t(125) = −2.44, P < 0.05]. However, consistent with this previous literature, liking the female student more than the male student did not translate into positive perceptions of her composite competence or material outcomes in the form of a job offer, an equitable salary, or valuable career mentoring. Moreover, only composite competence (and not likeability) helped to explain why the female student was less likely to be hired; in mediation analyses, student gender condition (β = −0.48, P < 0.001) remained a strong predictor of hireability along with likeability (β = 0.60, P < 0.001). These findings underscore the point that faculty participants did not exhibit outright hostility or dislike toward female students, but were instead affected by pervasive gender stereotypes, unintentionally downgrading the competence, hireability, salary, and mentoring of a female student compared with an identical male.

Discussion

The present study is unique in investigating subtle gender bias on the part of faculty in the biological and physical sciences. It therefore informs the debate on possible causes of the gender disparity in academic science by providing unique experimental evidence that science faculty of both genders exhibit bias against female undergraduates. As a controlled experiment, it fills a critical gap in the existing literature, which consisted only of experiments in other domains (with undergraduate students as participants) and correlational data that could not conclusively rule out the influence of other variables.

Our results revealed that both male and female faculty judged a female student to be less competent and less worthy of being hired than an identical male student, and also offered her a smaller starting salary and less career mentoring. Although the differences in ratings may be perceived as modest, the effect sizes were all moderate to large (d = 0.60–0.75). Thus, the current results suggest that subtle gender bias is important to address because it could translate into large real-world disadvantages in the judgment and treatment of female science students (39). Moreover, our mediation findings shed light on the processes responsible for this bias, suggesting that the female student was less likely to be hired than the male student because she was perceived as less competent. Additionally, moderation results indicated that faculty participants’ preexisting subtle bias against women undermined their perceptions and treatment of the female (but not the male) student, further suggesting that chronic subtle biases may harm women within academic science. Use of a randomized controlled design and established practices from audit study methodology support the ecological validity and educational implications of our findings (SI Materials and Methods).

It is noteworthy that female faculty members were just as likely as their male colleagues to favor the male student. The fact that faculty members’ bias was independent of their gender, scientific discipline, age, and tenure status suggests that it is likely unintentional, generated from widespread cultural stereotypes rather than a conscious intention to harm women (17). Additionally, the fact that faculty participants reported liking the female more than the male student further underscores the point that our results likely do not reflect faculty members’ overt hostility toward women. Instead, despite expressing warmth toward emerging female scientists, faculty members of both genders appear to be affected by enduring cultural stereotypes about women’s lack of science competence that translate into biases in student evaluation and mentoring.

Our careful selection of expert participants revealed gender discrimination among existing science faculty members who interact with students on a regular basis (SI Materials and Methods: Subjects and Recruitment Strategy). This method allowed for a high degree of ecological validity and generalizability relative to an approach using nonexpert participants, such as other undergraduates or lay people unfamiliar with laboratory manager job requirements and academic science mentoring (i.e., the participants in much psychological research on gender discrimination). The results presented here reinforce those of Stenpries, Anders, and Ritzke (40), the only other experiment we know of that recruited faculty participants. Because this previous experiment also indicated bias within academic science, its results raised serious concerns about the potential for faculty bias within the biological and physical sciences, casting further doubt on assertions (based on correlational data) that such biases do not exist (911). In the Steinpreis et al. experiment, psychologists were more likely to hire a psychology faculty job applicant when the applicant’s curriculum vitae was assigned a male (rather than female) name (40). This previous work invited a study that would extend the finding to faculty in the biological and physical sciences and to reactions to undergraduates, whose competence was not already fairly established by accomplishments associated with the advanced career status of the faculty target group of the previous study. By providing this unique investigation of faculty bias against female students in biological and physical sciences, the present study extends past work to a critical early career stage, and to fields where women’s underrepresentation remains stark (24).

Indeed, our findings raise concerns about the extent to which negative predoctoral experiences may shape women’s subsequent decisions about persistence and career specialization. Following conventions established in classic experimental studies to create enough ambiguity to leave room for potentially biased responses (20, 23), the student applicants in the present research were described as qualified to succeed in academic science (i.e., having coauthored a publication after obtaining 2 y of research experience), but not irrefutably excellent. As such, they represented a majority of aspiring scientists, and were precisely the type of students most affected by faculty judgments and mentoring (see SI Materials and Methods for more discussion). Our results raise the possibility that not only do such women encounter biased judgments of their competence and hireability, but also receive less faculty encouragement and financial rewards than identical male counterparts. Because most students depend on feedback from their environments to calibrate their own worth (41), faculty’s assessments of students’ competence likely contribute to students’ self-efficacy and goal setting as scientists, which may influence decisions much later in their careers. Likewise, inasmuch as the advice and mentoring that students receive affect their ambitions and choices, it is significant that the faculty in this study were less inclined to mentor women than men. This finding raises the possibility that women may opt out of academic science careers in part because of diminished competence judgments, rewards, and mentoring received in the early years of the careers. In sum, the predoctoral years represent a window during which students’ experiences of faculty bias or encouragement are particularly likely to shape their persistence in academic science (3033). Thus, the present study not only fills an important gap in the research literature, but also has critical implications for pressing social and educational issues associated with the gender disparity in science.

If women’s decisions to leave science fields when or before they reach the faculty level are influenced by unequal treatment by undergraduate advisors, then existing efforts to create more flexible work settings (42) or increase women’s identification with science (27) may not fully alleviate a critical underlying problem. Our results suggest that academic policies and mentoring interventions targeting undergraduate advisors could contribute to reducing the gender disparity. Future research should evaluate the efficacy of educating faculty and students about the existence and impact of bias within academia, an approach that has reduced racial bias among students (43). Educational efforts might address research on factors that attenuate gender bias in real-world settings, such as increasing women’s self-monitoring (44). Our results also point to the importance of establishing objective, transparent student evaluation and admissions criteria to guard against observers’ tendency to unintentionally use different standards when assessing women relative to men (45, 46). Without such actions, faculty bias against female undergraduates may continue to undermine meritocratic advancement, to the detriment of research and education.

Conclusions

The dearth of women within academic science reflects a significant wasted opportunity to benefit from the capabilities of our best potential scientists, whether male or female. Although women have begun to enter some science fields in greater numbers (5), their mere increased presence is not evidence of the absence of bias. Rather, some women may persist in academic science despite the damaging effects of unintended gender bias on the part of faculty. Similarly, it is not yet possible to conclude that the preferences for other fields and lifestyle choices (911) that lead many women to leave academic science (even after obtaining advanced degrees) are not themselves influenced by experiences of bias, at least to some degree. To the extent that faculty gender bias impedes women’s full participation in science, it may undercut not only academic meritocracy, but also the expansion of the scientific workforce needed for the next decade’s advancement of national competitiveness (1).

Materials and Methods

Participants.

We recruited faculty participants from Biology, Chemistry, and Physics departments at three public and three private large, geographically diverse research-intensive universities in the United States, strategically selected for their representative characteristics (see SI Materials and Methods for more information on department selection). The demographics of the 127 respondents corresponded to both the averages for the selected departments and faculty at all United States research-intensive institutions, meeting the criteria for generalizability even from nonrandom samples (see SI Materials and Methods for more information on recruitment strategy and participant characteristics). Indeed, we were particularly careful to obtain a sample representative of the underlying population, because many past studies have demonstrated that when this is the case, respondents and nonrespondents typically do not differ on demographic characteristics and responses to focal variables (47).

Additionally, in keeping with recommended practices, we conducted an a priori power analysis before beginning data collection to determine the optimal sample size needed to detect effects without biasing results toward obtaining significance (SI Materials and Methods: Subjects and Recruitment Strategy) (48). Thus, although our sample size may appear small to some readers, it is important to note that we obtained the necessary power and representativeness to generalize from our results while purposefully avoiding an unnecessarily large sample that could have biased our results toward a false-positive type I error (48).

Procedure.

Participants were asked to provide feedback on the materials of an undergraduate science student who stated their intention to go on to graduate school, and who had recently applied for a science laboratory manager position. Of importance, participants believed they were evaluating a real student who would subsequently receive the faculty participants’ ratings as feedback to help their career development (see SI Materials and Methods for more information, and Fig. S1 for the full text of the cover story). Thus, the faculty participants’ ratings were associated with definite consequences.

Following established practices, the laboratory manager application was designed to reflect high but slightly ambiguous competence, allowing for variability in participant responses (20, 23). In addition, a promising but still-nascent applicant is precisely the type of student whose persistence in academic science is most likely to be affected by faculty support or discouragement (3033), rendering faculty reactions to such a student of particular interest for the present purposes. The materials were developed in consultation with a panel of academic science researchers (who had extensive experience hiring and supervising student research assistants) to ensure that they would be perceived as realistic (SI Materials and Methods). Results of a funneled debriefing (49) indicated that this was successful; no participant reported suspicions that the target was not an actual student who would receive their evaluation.

Participants were randomly assigned to one of two student gender conditions: application materials were attributed to either a male student (John, n = 63), or a female student (Jennifer, n = 64), two names that have been pretested as equivalent in likability and recognizeability (50). Thus, each participant saw only one set of materials, from either the male or female applicant (see Fig. S2 for the full text of the laboratory manager application and SI Method and Materials for more information on all materials). Because all other information was held constant between conditions, any differences in participants’ responses are attributable to the gender of the student. Using validated scales, participants rated student competence, their own likelihood of hiring the student, selected an annual starting salary for the student, indicated how much career mentoring they would provide to such a student, and completed the Modern Sexism Scale.

Supplementary Material

Supporting Information:

Acknowledgments

We thank faculty members from six anonymous universities for their involvement as participants; and Jessamina Blum, John Crosnick, Jennifer Frederick, Jaime Napier, Jojanneke van der Toorn, Tiffany Tsang, Tessa West, James Young, and two anonymous reviewers for valuable input. This research was supported by a grant from the Howard Hughes Medical Institute Professors Program (to J.H.).

Footnotes

The authors declare no conflict of interest.

*This Direct Submission article had a prearranged editor.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1211286109/-/DCSupplemental.

References

1. President’s Council of Advisors on Science and Technology (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. Available at http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-engage-to-excel-final_feb.pdf. (Accessed February 13, 2012)
2. Handelsman J, et al. Careers in science. More women in science. Science. 2005;309:1190–1191. [PubMed] [Google Scholar]
3. United States National Academy of Sciences . Beyond Bias and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering. Washington, DC: National Academies; 2007. [PubMed] [Google Scholar]
4. National Science Foundation . Women, Minorities, and Persons with Disabilities in Science and Engineering. Arlington: National Science Foundation; 2009. [Google Scholar]
5. Bell N. Graduate Enrollment and Degrees: 1999 to 2009. Washington, DC: Council of Graduate Schools; 2010. [Google Scholar]
6. Halpern DF, et al. The science of gender differences in science and mathematics. Psychol Sci. 2007;8(1):1–51. [PMC free article] [PubMed] [Google Scholar]
7. Hyde JS, Linn MC. Diversity. Gender similarities in mathematics and science. Science. 2006;314:599–600. [PubMed] [Google Scholar]
8. Spelke ES. Sex differences in intrinsic aptitude for mathematics and science?: A critical review. Am Psychol. 2005;60:950–958. [PubMed] [Google Scholar]
9. Ceci SJ, Williams WM. Gender differences in math-intensive fields. Curr Dir Psychol Sci. 2010;19:275–279. [PMC free article] [PubMed] [Google Scholar]
10. Ceci SJ, Williams WM. Understanding current causes of women’s underrepresentation in science. Proc Natl Acad Sci USA. 2011;108:3157–3162. [PMC free article] [PubMed] [Google Scholar]
11. Ceci SJ, et al. Do subtle cues about belongingness constrain women’s career choices? Psychol Inq. 2011;22:255–258. [PMC free article] [PubMed] [Google Scholar]
12. Berezow AB (2011) Gender discrimination in science is a myth. Available at http://www.nationalreview.com/articles/256816/gender-discrimination-science-myth-alex-b-berezow?pg=2. (Accessed June 6, 2012)
13. Dickey Zakaib G. Science gender gap probed. Nature. 2011;470:153. [PubMed] [Google Scholar]
14. Devine PG. Stereotypes and prejudice: Their automatic and controlled components. J Pers Soc Psychol. 1989;56(1):5–18. [Google Scholar]
15. Eagly AH, Mladinic A. Are people prejudiced against women? Some answers from research on attitudes, gender stereotypes, and judgments of competence. Eur Rev Soc Psychol. 1994;5(1):1–35. [Google Scholar]
16. Spence JT, Hahn ED. The attitudes toward women scale and attitude change in college students. Psychol Women Q. 1997;21(1):17–34. [Google Scholar]
17. Dovidio JF, Gaertner SL. In: Advances in Experimental Social Psychology. Zanna MP, editor. New York: Elsevier; 2004. pp. 1–51. [Google Scholar]
18. Nosek BA, Banaji M, Greenwald AG. Harvesting implicit group attitudes and beliefs from a demonstration web site. Group Dyn. 2002;6(1):101–115. [Google Scholar]
19. Goldin C, Rouse C. Orchestrating impartiality: The impact of “blind” auditions on female musicians. Am Econ Rev. 2000;90:715–741. [Google Scholar]
20. Foschi M. Double standards for competence: Theory and research. Annu Rev Sociol. 2000;26(1):21–42. [Google Scholar]
21. Foschi M. Double standards in the evaluation of men and women. Soc Psychol Q. 1996;59:237–254. [Google Scholar]
22. Goldberg P. Are women prejudiced against women? Transaction. 1968;5(5):28–30. [Google Scholar]
23. Heilman ME, Wallen AS, Fuchs D, Tamkins MM. Penalties for success: Reactions to women who succeed at male gender-typed tasks. J Appl Psychol. 2004;89:416–427. [PubMed] [Google Scholar]
24. Monin B, Miller DT. Moral credentials and the expression of prejudice. J Pers Soc Psychol. 2001;81:33–43. [PubMed] [Google Scholar]
25. Uhlmann EL, Cohen GL. “I think it, therefore it’s true”: Effects of self-perceived objectivity on hiring discrimination. Organ Behav Hum Dec. 2007;104:207–223. [Google Scholar]
26. Nosek BA, Banaji MR, Greenwald AG. Math = male, me = female, therefore math not = me. J Pers Soc Psychol. 2002;83:44–59. [PubMed] [Google Scholar]
27. Dasgupta N. Ingroup experts and peers as social vaccines who inoculate the self-concept: The stereotype inoculation model. Psychol Inq. 2011;22:231–246. [Google Scholar]
28. The Massachusetts Institute of Technology (1999) A study on the status of women faculty in science at MIT. Available at http://web.mit.edu/fnl/women/women.html#The%20Study (Accessed February 13, 2012)
29. Steele J, James JB, Barnett RC. Learning in a man’s world: Examining the perceptions of undergraduate women in male-dominated academic areas. Psychol Women Q. 2002;26(1):46–50. [Google Scholar]
30. Lent RW, et al. The role of contextual supports and barriers in the choice of math/science educational options: A test of social cognitive hypotheses. J Couns Psychol. 2001;48:474–483. [Google Scholar]
31. Seymour E, Hewitt NM. Talking about Leaving: Why Undergraduates Leave the Sciences. Boulder: Westview; 1996. [Google Scholar]
32. Gasiewski JA, et al. From gatekeeping to engagement: A multicontextual, mixed method study of student academic engagement in introductory STEM courses. Res Higher Educ. 2012;53:229–261. [PMC free article] [PubMed] [Google Scholar]
33. Byars-Winston A, Gutierrez B, Topp S, Carnes M. Integrating theory and practice to increase scientific workforce diversity: A framework for career development in graduate research training. CBE Life Sci Educ. 2011;10:357–367. [PMC free article] [PubMed] [Google Scholar]
34. Campbell DT, Fiske DW. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull. 1959;56:81–105. [PubMed] [Google Scholar]
35. Robins RW, Hendin HM, Trzesniewski KH. Measuring global self esteem: Construct validation of a single-item measure and the Rosenberg Self-Esteem Scale. Pers Soc Psychol Bull. 2001;27(2):151–161. [Google Scholar]
36. Moss-Racusin CA, Rudman LA. Disruptions in women’s self-promotion: The backlash avoidance model. Psychol Women Q. 2010;34:186–202. [Google Scholar]
37. Rudman LA, Moss-Racusin CA, Glick P, Phelan JE. In: Advances in Experimental Social Psychology. Devine P, Plant A, editors. New York: Elsevier; 2012. pp. 167–227. [Google Scholar]
38. Swim JK, Aikin KJ, Hall WS, Hunter BA. Sexism and racism: Old-fashioned and modern prejudices. J Pers Soc Psychol. 1995;68:199–214. [Google Scholar]
39. Martell RF, Lane DM, Emrich C. Male-female differences: A computer simulation. Am Psychol. 1996;51(2):157–158. [Google Scholar]
40. Steinpreis RE, Anders KA, Ritzke D. The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study. Sex Roles. 1999;41:509–528. [Google Scholar]
41. Bandura A. Self-efficacy mechanism in human agency. Am Psychol. 1982;37:122–147. [Google Scholar]
42. Scandura TA, Lankau MJ. Relationships of gender, family responsibility and flexible work hours to organizational commitment and job satisfaction. J Organ Behav. 1997;18:377–391. [Google Scholar]
43. Rudman LA, Ashmore RD, Gary ML. “Unlearning” automatic biases: The malleability of implicit prejudice and stereotypes. J Pers Soc Psychol. 2001;81:856–868. [PubMed] [Google Scholar]
44. O’Neill OA, O’Reilly CA. Reducing the backlash effect: Self-monitoring and women’s promotions. J Occup Organ Psychol. 2011;84:825–832. [Google Scholar]
45. Uhlmann EL, Cohen GL. Constructed criteria: Redefining merit to justify discrimination. Psychol Sci. 2005;16:474–480. [PubMed] [Google Scholar]
46. Phelan JE, Moss-Racusin CA, Rudman LA. Competent yet out in the cold: Shifting criteria for hiring reflects backlash toward agentic women. Psychol Women Q. 2008;32:406–413. [Google Scholar]
47. Holbrook AL, Krosnick JA, Pfent A. In: Advances in Telephone Survey Methodology. Lepkowski JM, et al., editors. Hoboken: John Wiley & Sons; 2007. pp. 499–528. [Google Scholar]
48. Simmons JP, Nelson LD, Simonsohn U. False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol Sci. 2011;22:1359–1366. [PubMed] [Google Scholar]
49. Bargh JA, Chartrand TL. In: Handbook of Research Methods in Social and Personality Psychology. Reis HT, Judd CM, editors. New York: Cambridge Univ Press; 2000. pp. 253–285. [Google Scholar]
50. Brescoll VL, Uhlmann EL. Can angry women get ahead? Status conferral, gender, and workplace emotion expression. Psychol Sci. 2008;19:268–275. [PubMed] [Google Scholar]
51. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd Ed. Hillsdale: Erlbaum; 1998. [Google Scholar]

Articles from Proceedings of the National Academy of Sciences of the United States of America are provided here courtesy of National Academy of Sciences