1. Melissa Armendariz, Scott Frankowski, M.A., Michael Zárate, Ph.D. Social Cognition Lab, Psychology Department, The University of Texas at El Paso Contact: marmendariz9@miners.utep.edu Participants (N = 320), 70.31% female, M age = 21.12, SD = 6.04 Design 2x2x2x2 mixed design (Fig. 1) (participant’s gender x applicant’s gender x Engineering/Education x success/failure) Gender disparities exist in STEM (less women; Sekaquaptewa, 2014 ) and in education, social sciences, and health-services (less men; Correll, 2004 ) Women who are successful in typically male dominated fields are liked less (Heilman & Okimoto, 2007) Men in typically female dominated fields face prejudice from those outside their profession (Williams, 1992) i.e. Negative consequences for those who violate gender stereotype expectations 80% of students in college switch majors (National Center for Education Statistics, 2011) Many of these students switch due to difficulty with coursework Most students who graduate from a major that is different than the one they originally chose are going to have bad grades on transcripts from previous major Questions To what extent does previous failure affect perceptions of students? Do gender expectations shape these perceptions? Do gender expectations play a role in gender disparities in STEM, education, social sciences, and health services? Objective We are examining attitudes towards male and female applicants when success or failure in an academic field is congruent or incongruent with stereotypical gender-roles Hypotheses Individuals who fail in gender atypical fields of study should be seen as more positively when they go onto a gender typical field Women failing in engineering going to education Men failing in education going into engineering Those who fail in a gender typical field will be seen as more negatively when they go onto a gender atypical field Women failing in education going into engineering Men failing in engineering going into education Sexist attitudes may moderate these effects Hypotheses were partially supported: Women’s previous failures hinder them when going into program that is incongruent with gender role stereotypes Not hindered if going into gender role congruent program: i.e. women are allowed to fail when conforming to societal gender roles Men’s failure does not hinder them compared to women. i.e. gender role stereotypes did not affect perceptions of men’s career choices after previous failure Women had an advantage with funding, where people tended to grant women more funding when they were applying to engineering compared to men Diversity statement in prime might be a confound Priming “affirmative action” People are generally more surprised at women’s success compared to men’s success Effects were not meaningfully moderated by sexist attitudes Successful applicants consistently rated much more favorably (manipulation check) Overall Performance in undergraduate major: Target sex by condition by success or failure interaction, F(3, 316) = 31.07, p <.001, η 2 p =.09 (Fig. 3) Women who previously failed in education and applied to engineering seen less favorably (M = 2.83, SD = 0.90) compared to women who previously failed in engineering and applied to education (M = 3.34, SD = 0.87), t(316) = 3.88, p <.001, d = 0.44 Women who failed in education and applied to engineering seen less favorably than men who did the same, t(161) = -13.17, p <.001 d paired 1 = 1.05 1 Corrected effect size for within-subjects tests (Morris & DeShon, 2002) Fig. 3. asterisks denote significant mean differences for comparisons of interest. Applicants failed in major opposite of the condition (e.g. women who applied to the engineering program had failed in education). Error bars represent ± 1 standard error. Funding: There was a significant target sex by condition by success or failure interaction, F(3, 316)= 27.51, p <.001, η 2 p =.08 Surprisingly, women are granted more funding when they previously failed in education and are applying to engineering (M = 2.74, SD = 0.88 ) compared to women who previously failed in engineering and are applying to education (M = 2.13, SD = 1.02), t(316) = 5.25, p <.001, d = 0.59, and compared to men applying to engineering after failing in education (M = 2.17, SD = 0.99), t(161) = 11.44, p <.001, d paired = 0.91. Admission : no condition interaction How Typical?: no condition interaction How Surprising?: no condition interaction Target gender by success/failure interaction, F(1, 316) = 9.13, p =.003, η 2 p =.05. Greater surprise at women’s success (M = 4.74, SD = 1.79) compared than men’s (M = 4.47, SD = 1.66), t(320) = 4.35, p <.001, d paired = 0.23. Effects were not meaningfully moderated by sexist attitudes Methods and Materials Introduction Discussion Results Dependent variables: Overall applicant did well as an undergraduate (1 = Strongly disagree, 6 = Strongly agree ), scholarship funding (1 = $0, 5 = $2500), Admission decision (1 = Definitely should not admit, 7 = Definitely should admit) How surprising was their undergraduate performance? (1 = Not at all, 7 = Very surprising) How typical was their undergraduate career? (1 = Not at all, 7 = Very typical) Ambivalent Sexism Inventory measure (Glick & Fiske, 1996) 12 questions to assess their level of sexist attitudes 7 point scale Strongly Disagree to Strongly Agree Hostile sexism- “Women seek to gain power by getting control over men.” Benevolent sexism- “Many women have a quality of purity that few men possess:” and, “Women should be cherished and protected by men.” Demographics and debriefing Conclusions Adherence to gender roles mostly affected attitudes toward women, not men Expectations for women to maintain the status quo and remain in female-dominated academic fields Consequences for women who challenge gender norms (seen less favorably) Evidence that gender norms play a role in affecting perceptions of students The admission and funding results may indicate that women do not face barriers to entry in counter-stereotypic fields; however, attitudes that favor men over women in male-dominated professions may lead to biased behaviors that affect attrition in these fields (see also Williams & Ceci, 2015). Between Subjects Applying to education (n = 159) or engineering (n = 162) master’s program Participant’s gender (Quasi-independent) Within Subjects Target gender (male/female) Academic performance in undergraduate major (success/failure) 8 target transcripts (Fig. 2) 8 filler transcripts Gender neutral – Pretested Gender of each filler randomly assigned Fillers not included in analyses, only meant to distract Future Directions Replication should occur with people in university who make admissions decisions Use of undergraduate participants raises concerns about ecological validity Follow-up study in progress in which previous failure occurs in gender congruent, incongruent, or neutral program Current study lacks test of whether participants are rating failure in general or failure in gender typical or atypical major Follow-up to examine how participants rate the employability of applicants Fig. 1. Experimental design. Applicants randomly assigned to read about applicant to engineering or education master’s program. Rated male and female who failed or succeeded in undergraduate major. Filler genders were pretested and randomly assigned. Fig. 2. Example application that participants rated. * * Acknowledgements References This research was funded through the Campus Office of Undergraduate Research Initiatives awarded to Melissa Armendariz Thank you Jonathan De Santiago, Karla Chavez, and Irvin Escobedo for assisting with data collection Correll, S. J. (2004). Constraints into preferences: Gender, status, and emerging career aspirations. American sociological review, 69(1), 93- 113. Glicke, P., & Fiske, S., T. (1996). The ambivalent sexism inventory: differentiating hostile and benevolent sexism. Journal of personality and social psychology, 70(3), 491. Heilman, M., E., & Okimoto, T., G. (2007). Why are women are penalized for success at male tasks?: the implied communallity deficit. Journal of applied psychology, 92(1), 81. Morris, S., B., & DeShon, R., P. (2002). Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychological methods, 7(1), 105. National Center of Education Statistics, (2011). Sekaquaptewa, D. (2014). Social psychological research factors shaping institutional climate in STEM. In Abstracts of Papers of the American Chemical Society (Vol. 248). Williams, C., L. (1992). The glass escalator: Hidden advantages for men in the “female” professions. Social Problems, pp. 253-267. Williams, W., M., & Ceci, S., J. (2015). National hiring experiments reveal 2: 1 faculty preference for women on STEM tenure track. Proceedings of the National Academy of Sciences, 112(7), 5360-5365.