Using implicit bias training to improve attitudes toward women in STEM
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Implicit biases can foster negative attitudes and lead to damaging stereotypical behaviors. Stereotypes can negatively affect the education, hiring, promotion, and retention of women in science, technology, engineering, and mathematics (STEM). This study evaluated the impact of diversity training on university faculty (\(N = 234\)) by assessing changes in implicit associations and explicit attitudes toward women in STEM. Personal implicit associations about women in STEM improved for men, but not for women who already tended toward more positive implicit associations at pre-test. Men were more likely than women to explicitly endorse stereotypes about women in STEM at both pre- and post-test, and these attitudes did not change as a result of the diversity training. These findings suggest that participation in a brief diversity training can improve implicit associations about women in STEM.
KeywordsGender Stereotypes Implicit attitudes Diversity training
This research was part of a larger study supported by NSF ADVANCE HRD 0810989. We would like to thank Gary Burns for his contributions, and the ADVANCE LEADER team and STEM department chairs at all four institutions for their assistance and cooperation.
- Bar-Anan, Y., & Nosek, B. A. (2013). A comparative investigation of seven indirect attitude measures. Behavior Research Methods. doi: 10.3758/s13428-013-0410-6.
- Burrelli, J. (2008). Thirty-three years of women in S&E faculty positions. National Science Foundation: Arlington, VA (NSF 08–308). Retrieved from http://www.nsf.gov/statistics/infbrief/nsf08308/.
- Cundiff, J. L., Vescio, T. K., Loken, E., & Lo, L. (2013). Do gender-science stereotypes predict science identification and science career aspirations among undergraduate science majors? Social Psychology of Education. Advance online publication. doi: 10.1007/s11218-013-9232-8.
- De Welde, K., Laursen, S., & Thiry, H. (2007). SWS fact sheet: Women in Science, Technology, Engineering and Math (STEM). Network News: The Newsletter for Sociologists for Women in Society, 23(4), 14–19. Retrieved from http://www.socwomen.org/wp-content/uploads/2010/05/fact_12-2007-stem.pdf.
- Hillard, A. L., Jackson, S. M., & Schneider, T. R. (2012). Best Practices for Discussing Diversity and Implicit Bias in the Classroom. Chicago, IL: Poster Presented at the Annual Convention of the Association for Psychological Science.Google Scholar
- Holdren, J. P. (2012). Equal futures: Opening doors to high-quality education and career opportunities for women and girls in STEM. Office of the Science and Technology Policy. Retrieved from http://wh.gov/KXLF.
- McCauley, C., Wright, M., & Harris, M. E. (2000). Diversity workshops on campus: A survey of current practice at U.S. colleges and universities. College Student Journal, 34(1), 100–114. Retrieved from http://hwwilsonweb.com.
- Mischel, W. (1977). The interaction of person and situation. In D. Magnusson & N. S. Endler (Eds.), Personality at the crossroads: Current issues in interactional psychology (pp. 333–352). Hillsdale, NJ: Lawrence Erlbaum Associates Inc.Google Scholar
- Morris, K. A., Ashburn-Nardo, L., & Padgett, R. J. (2011). Think fast: Using web-based reaction time technology to promote teaching about racial bias and diversity. In D. S. Dunn, J. C. Wilson, J. Freeman, & J. R. Stowell (Eds.), Getting connected: Best practices for technology-enhanced teaching and learning in higher education. New York, NY: Oxford University Press.Google Scholar
- Morton, J., & Rosse, M. (2011). Persuasive presentations in engineering spoken discourse. Australasian Journal of Engineering Education, 17(2), 55–64.Google Scholar
- National Research Council. (2007). Beyond bias and barriers: Fulfilling the potential of women in academic science and engineering. Washington, DC: National Academies Press.Google Scholar
- National Science Foundation, Division of Science Resource Statistics. (2013a). Doctorate recipients from U.S. universities: 2012 (NSF Publication No. 14–305). Arlington, VA. Retrieved from http://www.nsf.gov/statistics/sed/digest/2012/.
- National Science Foundation, National Center for Science and Engineering Statistics. (2013b). Women, minorities, and persons with disabilities in science and engineering: 2013 (NSF Publication No. 13–304). Arlington, VA. Retrieved from http://www.nsf.gov/statistics/wmpd/2013/start.cfm.
- Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002a). Math \(=\) male, me \(=\) female, therefore math \(\ne \) me. Journal of Personality and Social Psychology, 83, 44–59.Google Scholar
- Nosek, B. A., & Lane, K. (1999). Analyzing paper-pencil IAT data. Unpublished manuscript, Yale University.Google Scholar
- Office of the Press Secretary. (2013). Fact sheet: The equal futures partnership—From promise to progress. The White House. Retrieved from http://wh.gov/eLeh.
- Van de Mortel, T. F. (2008). Faking it: Social desirability response bias in self-report research. Australian Journal of Advanced Nursing, 25(4), 40–48.Google Scholar
- Vargas, P., Sekaquaptewa, D., & von Hippel, W. (2007). Armed only with paper and pencil: “Low-tech” measures of implicit attitudes. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitiudes (pp. 103–124). New York: The Guilford Press.Google Scholar
- Wittenbrink, B., & Schwarz, N. (Eds.). (2007). Implicit measures of attitudes. New York: The Guilford Press.Google Scholar