Using implicit bias training to improve attitudes toward women in STEM

Abstract

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.

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Notes

  1. 1.

    For men, some traits were correlated with implicit measures. Social desirability correlated significantly with the pre-test GNAT and PGNAT (\(r\)’s \(= -.22\) and .23, respectively), and self-discrepancy correlated with the pre-test PGNAT (\(r = .22\)). There were no significant correlations between traits and implicit measures at post-test. ANCOVAs controlling for traits did not change the pattern of results.

  2. 2.

    We conducted a simple slopes analysis comparing men only in the experimental and control groups. Group was entered in the first step of a linear regression equation along with the pre-test PGNAT score. The interaction term of group by pre-test PGNAT score was added in step 2. The interaction term of group by pre-test PGNAT score explained a marginal amount of variance (\(\Delta R^{2}= 0.02\)) in the post-test PGNAT score, \(\Delta F(1, 146) = 2.69, p = .10\). The slopes of the experimental and control lines were marginally different, suggesting that the two groups changed differently over time. The lack of significance is likely due to low power. It is probable that a larger sample size would yield significant results.

References

  1. 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.

  2. Blair, I. V. (2002). The malleability of automatic stereotypes and prejudice. Personality and Social Psychology Review, 6(3), 242–261.

    Article  Google Scholar 

  3. Bezrukova, K., Jehn, K. A., & Spell, C. S. (2012). Reviewing diversity training: Where we have been and where we should go. Academy of Management Learning and Education, 11, 207–227. doi:10.5465/amle.2008.0090.

    Article  Google Scholar 

  4. 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/.

  5. Carnes, M., Devine, P. G., Isaac, C., Manwell, L. B., Ford, C. E., Byars-Winston, A., et al. (2012). Promoting institutional change through bias literacy. Journal of Diversity in Higher Education, 5(2), 63–77.

    Article  Google Scholar 

  6. Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97, 1045–1060.

    Article  Google Scholar 

  7. Christopher, A. N., & Wojda, M. R. (2008). Social dominance orientation, right-wing authoritarianism, sexism, and prejudice toward women in the workforce. Psychology of Women Quarterly, 32, 65–73.

    Article  Google Scholar 

  8. Crowne, D. P., & Marlowe, D. (1960). A new scale of social desirability independent of psychopathology. Journal of Consulting Psychology, 24, 349–354.

    Article  Google Scholar 

  9. 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.

  10. Czopp, A. M., & Monteith, M. J. (2003). Confronting prejudice (literally): Reactions to confrontations of racial and gender bias. Personality and Social Psychology Bulletin, 29, 532–544. doi:10.1177/0146167202250923.

    Article  Google Scholar 

  11. Czopp, A. M., Monteith, M. J., & Mark, A. Y. (2006). Standing up for a change: Reducing bias through interpersonal confrontation. Journal of Personality and Social Psychology, 90, 784–803.

    Article  Google Scholar 

  12. Dasgupta, N., & Asgari, S. (2004). Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. Journal of Experimental Social Psychology, 40, 642–658.

    Article  Google Scholar 

  13. Devine, P. G. (1989). Stereotypes and prejudice: Their automatic and controlled components. Journal of Personality and Social Psychology, 56, 5–18.

    Article  Google Scholar 

  14. 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.

  15. Eagly, A. H., & Mladinic, A. (1994). Are people prejudiced against women? Some answers from research on attitudes, gender stereotypes, and judgments of competence. European Review of Social Psychology, 5(1), 1–35.

    Article  Google Scholar 

  16. Fazio, R. H., & Olson, M. A. (2003). Implicit measures in social cognition research: Their meaning and use. Annual Review of Psychology, 54(1), 297–327.

    Article  Google Scholar 

  17. Fein, S., & Spencer, S. J. (1997). Prejudice as self-image maintenance: Affirming the self through derogating others. Journal of Personality and Social Psychology, 73, 31–44.

    Article  Google Scholar 

  18. Fuchs, D., Tamkins, M. M., Heilman, M. E., & Wallen, A. S. (2004). Penalties for success: Reactions to women who succeed at male gender-typed tasks. Journal of Applied Psychology, 89(3), 416–427.

    Article  Google Scholar 

  19. Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: Attitudes, self-esteem, and stereotypes. Psychological Review, 102(1), 4–27.

    Article  Google Scholar 

  20. Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480.

    Article  Google Scholar 

  21. Han, H. A., Czellar, S., Olson, M. A., & Fazio, R. H. (2010). Malleability of attitudes or malleability of the IAT? Journal of Experimental Social Psychology, 46(2), 286–298.

    Article  Google Scholar 

  22. Heilman, E. H., & Eagly, A. H. (2008). Gender stereotypes are alive, well, and busy producing workplace discrimination. Industrial and Organizational Psychology, 1, 393–398.

    Article  Google Scholar 

  23. Hewstone, M., Johnston, L., & Aird, P. (1992). Cognitive models of stereotype change: (2) Perceptions of homogeneous and heterogeneous groups. European Journal of Social Psychology, 22(3), 235–249.

    Article  Google Scholar 

  24. 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 

  25. Hillard, A. L., Ryan, C. S., & Gervais, S. J. (2013). Reactions to the Implicit Association Test as an educational tool: A mixed methods study. Social Psychology of Education, 16, 495–516. doi:10.1007/s11218-013-9219-5.

    Article  Google Scholar 

  26. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., & Schmitt, M. (2005). A meta-analysis on the correlation between the Implicit Association Test and explicit self-report measures. Personality and Social Psychology Bulletin, 31(10), 1369–1385.

    Article  Google Scholar 

  27. 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.

  28. Hong, S., & Faedda, S. (1996). Refinement of the Hong Psychological Reactance Scale. Educational & Psychological Measurement, 56(1), 173–182.

    Article  Google Scholar 

  29. Kalev, A., Dobbin, F., & Kelly, E. (2006). Best practices or best guesses? Assessing the efficacy of corporate affirmative action and diversity policies. American Sociological Review, 71, 589–617.

    Article  Google Scholar 

  30. Karpinski, A., & Hilton, J. L. (2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81, 774–778.

    Article  Google Scholar 

  31. Katz, I., & Hass, G. R. (1988). Racial ambivalence and American value conflict: Correlational and priming studies of dual cognitive structures. Journal of Personality and Social Psychology, 55, 893–905.

    Article  Google Scholar 

  32. King, M., & Bruner, G. (2000). Social desirability bias: A neglected aspect of validity testing. Psychology and Marketing, 17(2), 79–103.

    Article  Google Scholar 

  33. Macrae, C. M., Milne, A. B., & Bodenhausen, G. V. (1994). Stereotypes as energy-saving devices: A peek inside the cognitive toolbox. Journal of Personality and Social Psychology, 66, 37–47.

    Article  Google Scholar 

  34. 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.

  35. 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 

  36. Monteith, M. J., Mark, A. Y., & Ashburn-Nardo, L. (2010). The self-regulation of prejudice: Toward understanding its lived character. Group Processes and Intergroup Relations, 13, 183–200.

    Article  Google Scholar 

  37. Monteith, M. J., & Voils, C. I. (1998). Proneness to prejudiced responses: Toward understanding the authenticity of self-reported discrepancies. Journal of Personality and Social Psychology, 75, 901–916.

    Article  Google Scholar 

  38. 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 

  39. Morton, J., & Rosse, M. (2011). Persuasive presentations in engineering spoken discourse. Australasian Journal of Engineering Education, 17(2), 55–64.

    Google Scholar 

  40. Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences of the United States of America, 109, 16474–16479.

    Article  Google Scholar 

  41. Murphy, M. C., Steele, C. M., & Gross, J. J. (2007). Signaling threat: How situational cues affect women in math, science, and engineering settings. Psychological Science, 18, 879–885.

    Article  Google Scholar 

  42. National Research Council. (2007). Beyond bias and barriers: Fulfilling the potential of women in academic science and engineering. Washington, DC: National Academies Press.

  43. 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/.

  44. 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.

  45. Nosek, B. A., & Banaji, M. R. (2001). The go/no-go association task. Social Cognition, 19(6), 625–668.

    Article  Google Scholar 

  46. 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.

  47. Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002b). Harvesting implicit group attitudes and beliefs from a demonstration website. Group Dynamics, 6, 101–115.

    Article  Google Scholar 

  48. Nosek, B. A., & Hansen, J. J. (2008). Personalizing the Implicit Association Test increases explicit evaluation of target concepts. European Journal of Psychological Assessment, 24(4), 226–236.

    Article  Google Scholar 

  49. Nosek, B. A., & Lane, K. (1999). Analyzing paper-pencil IAT data. Unpublished manuscript, Yale University.

  50. Nosek, B. A., Smyth, F. L., Hansen, J. J., Devos, T., Lindner, N. M., Ranganath, K. A., et al. (2007). Pervasiveness and correlates of implicit attitudes and stereotypes. European Review of Social Psychology, 18, 36–88.

    Article  Google Scholar 

  51. 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.

  52. Olson, M. A., & Fazio, R. H. (2004). The influence of extrapersonal associations on the Implicit Association Test: Personalizing the IAT. Journal of Personality and Social Psychology, 86, 653–667.

    Article  Google Scholar 

  53. Paluck, E. L. (2006). Diversity training and intergroup contact: A call for action research. Journal of Social Issues, 62, 577–595.

    Article  Google Scholar 

  54. Prentice, D. A., & Carranza, E. (2002). What women and men should be, shouldn’t be, are allowed to be, and don’t have to be: The contents of prescriptive gender stereotypes. Psychology of Women Quarterly, 26, 269–281.

    Article  Google Scholar 

  55. Richards, Z., & Hewstone, M. (2001). Subtyping and subgrouping: Processes for the prevention and promotion of stereotype change. Personality and Social Psychology Review, 5, 52–73.

    Article  Google Scholar 

  56. Rudman, L. A., Ashmore, R. D., & Gary, M. L. (2001). “Unlearning” automatic biases: The malleability of implicit prejudice and stereotypes. Journal of Personality and Social Psychology, 81, 856–867.

    Article  Google Scholar 

  57. Schneider, T. R., Rivers, S. E., & Lyons, J. B. (2009). The biobehavioral model of persuasion: Generating challenge appraisals to promote health. Journal of Applied Social Psychology, 38, 1928–1952.

    Article  Google Scholar 

  58. Sekaquaptewa, D., & Thompson, M. (2002). Solo status, stereotype threat, and performance expectancies: Their effects on women’s performance. Journal of Experimental Social Psychology, 39, 68–74.

    Article  Google Scholar 

  59. Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69, 797–811.

    Article  Google Scholar 

  60. Teachman, B. A., & Brownell, K. D. (2001). Implicit anti-fat bias among health professionals: Is anyone immune? International Journal of Obesity & Related Metabolic Disorders, 25(10), 1525–1531.

    Article  Google Scholar 

  61. Todd, A. R., Bodenhausen, G. V., Richeson, J. A., & Galinsky, A. D. (2011). Perspective taking combats automatic expressions of racial bias. Journal of Personality and Social Psychology, 100, 1027.

    Article  Google Scholar 

  62. 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 

  63. 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.

  64. Wittenbrink, B., & Schwarz, N. (Eds.). (2007). Implicit measures of attitudes. New York: The Guilford Press.

  65. Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., & Malone, T. W. (2000). Evidence for a collective intelligence factor in the performance of human groups. Science, 330, 686–688.

    Article  Google Scholar 

  66. Wolsko, C., Park, B., Judd, C. M., & Wittenbrink, B. (2000). Framing interethnic ideology: Effects of multicultural and color-blind perspectives on judgments of groups and individuals. Journal of Personality and Social Psychology, 78, 536–654.

    Article  Google Scholar 

  67. Wright, A. L., Schwindt, L. A., Bassford, T. L., Reyna, V. F., Shisslak, C. M., Germain, P. A. S., et al. (2003). Gender differences in academic advancement: Patterns, causes, and potential solutions in one US College of Medicine. Academic Medicine, 78(5), 500–508.

    Article  Google Scholar 

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Acknowledgments

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.

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Correspondence to Amy L. Hillard.

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Jackson, S.M., Hillard, A.L. & Schneider, T.R. Using implicit bias training to improve attitudes toward women in STEM. Soc Psychol Educ 17, 419–438 (2014). https://doi.org/10.1007/s11218-014-9259-5

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Keywords

  • Gender
  • Stereotypes
  • Implicit attitudes
  • Diversity training