Research Article

Stereotype threat and gender differences in statistics

Gita Taasoobshirazi 1 * , Ordene Edwards 2, Bowen Eldridge 1
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1 School of Data Science and Analytics, Kennesaw State University, Kennesaw, GA, USA2 Department of Psychological Sciences, Kennesaw State University, Kennesaw, GA, USA* Corresponding Author
Contemporary Mathematics and Science Education, 4(1), 2023, ep23014,
Published: 12 March 2023
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Stereotype threat (ST) has been extensively explored as an explanation for gender disparities in achievement and participation in mathematics. However, there is a lack of research evaluating ST in statistics. The present study evaluated the impact of ST on gender differences in student performance, self-efficacy, and anxiety in statistics using a four-group, quasi-experimental design. Specifically, 102 elementary statistics students at a university in the Southeast United States were randomly assigned to one of four ST conditions including an explicit ST condition, an implicit ST condition, a reverse ST condition, and a nullified ST condition. Results indicated that there were no gender differences by ST condition in statistics self-efficacy, test anxiety, and performance. Analyses of student responses to open-ended questions indicated that females were more likely than males to report that they had fewer opportunities to achieve in statistics. Implications of our findings and suggestions for future research are discussed.


Taasoobshirazi, G., Edwards, O., & Eldridge, B. (2023). Stereotype threat and gender differences in statistics. Contemporary Mathematics and Science Education, 4(1), ep23014.


  1. Allen, D., Dancy, M., Stearns, E., Mickelson, R., & Bottia, M. (2022). Racism, sexism and disconnection: contrasting experiences of Black women in STEM before and after transfer from community college. International Journal of STEM Education, 9(1), 1-21.
  2. Baloglu, M. (2001). An application of structural equation modeling techniques in the prediction of statistics anxiety among college students [Unpublished doctoral dissertation]. Texas A&M University-Commerce.
  3. Batanero, C., Merino, B., & Díaz, C. (2003). Assessing secondary school student’s understanding of average. European Research in Mathematics Education III, 3, 1-9.
  4. Boas, L. V. (2020). Diversity in data science: A systemic inequality how FAANG companies are dealing with this structural problem. Towards Data Science.
  5. Brown, P. L., Concannon, J. P., Marx, D., Donaldson, C. W., & Black, A. (2016). An examination of middle school students’ STEM self-efficacy with relation to interest and perceptions of STEM. Journal of STEM Education, 17, 27-38.
  6. Buck, J. L. (1985). A failure to find gender differences in statistics achievement. Teaching of Psychology, 12(2), 100-100.
  7. Campbell, K. L., Moore, J. B., & Bartholomew, J. B. (2020). The importance of publishing null results: Editorial guidelines to contribute to the reduction of publication bias in translational exercise research. Translational Journal of the American College of Sports Medicine, 5(11), 1.
  8. Carr, M., & Jessup, D. L. (1997). Gender differences in first-grade mathematics strategy use: Social and metacognitive influences. Journal of Educational Psychology, 89(2), 318-328.
  9. Charles, K. K., & Luoh, M. C. (2003). Gender differences in completed schooling. Review of Economics and Statistics, 85(3), 559-577.
  10. Criado-Perez, C. (2019). Invisible women: Exposing data bias in a World designed for men. Abrams Press.
  11. Cruise, R. J., Cash, R. W., & Bolton, D. L. (1985). Development and validation of an instrument to measure statistical anxiety [Paper presentation]. The Annual Meeting of the American Statistical Association Statistics Education Section.
  12. Davies, P. G., Spencer, S. J., Quinn, D. M., & Gerhardstein, R. (2002). Consuming images: How television commercials that elicit stereotype threat can restrain women academically and professionally. Personality and Social Psychology Bulletin, 28(12), 1615-1628.
  13. Es, C. V., & Weaver, M. M. (2018). Race, sex, and their influences on introductory statistics education. Journal of Statistics Education, 26, 48-54.
  14. Evans, T., Thomas, M. O., & Klymchuk, S. (2021). Non-routine problem solving through the lens of self-efficacy. Higher Education Research & Development, 40(7), 1403-1420.
  15. Fanelli, D. (2010). “Positive” results increase down the hierarchy of the sciences. PloS ONE, 5(4), e10068.
  16. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.
  17. Fogliati, V. J., & Bussey, K. (2013). Stereotype threat reduces motivation to improve: Effects of stereotype threat and feedback on women’s intentions to improve mathematical ability. Psychology of Women Quarterly, 37(3), 310-324.
  18. Forbes. (2020). Fifteen most valuable college majors. Retrieved January 2, 2023.
  19. Glassdoor (2020). 50 best jobs in America.,20.htm
  20. Hiller, S. E., Kitsantas, A., Cheema, J. E., & Poulou, M. (2021). Mathematics anxiety and self-efficacy as predictors of mathematics literacy. International Journal of Mathematical Education in Science and Technology, 53(8), 2133-2151.
  21. Jacobs, J. E. (2005). Twenty-five years of research on gender and ethnic differences in math and science career choices: What have we learned?, New Directions for Child and Adolescent Development, 110, 85-94.
  22. Johnson, H. J., Barnard-Brak, L., Saxon, T. F., & Johnson, M. K. (2012). An experimental study of the effects of stereotype threat and stereotype lift on men and women’s performance in mathematics. The Journal of Experimental Education, 80(2), 137-149.
  23. Kapitanoff, S., & Pandey, C. (2017). Stereotype threat, anxiety, instructor gender, and underperformance in women. Active Learning in Higher Education, 18(3), 213-229.
  24. Legaki, N. Z., Xi, N., Hamari, J., Karpouzis, K., & Assimakopoulos, V. (2020). The effect of challenge-based gamification on learning: An experiment in the context of statistics education. International journal of human-computer studies, 144, 102496.
  25. Mathnasium. (2022). Math vs. statistics: Important points one should know.
  26. Navidi, W. C., & Monk, B. J. (2019). Elementary statistics. McGraw-Hill.
  27. NSF. (2017). Women, minorities, and persons with disabilities in science and engineering: 2017. National Science Foundation.
  28. O’Brien, L. T., & Crandall, C. S. (2003). Stereotype threat and arousal: Effects on women’s math performance. Personality and Social Psychology Bulletin, 29(6), 782-789.
  29. Onwuegbuzie, A. J. (1995). Statistics test anxiety and female students. Psychology of Women Quarterly, 19, 413-418.
  30. Pilotti, M. A. (2021). What lies beneath sustainable education? Predicting and tackling gender differences in STEM academic success. Sustainability, 13(4), 1671.
  31. Pintrich, P. R., Smith, D. A., García, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801-813.
  32. Quinn, D. M., & Spencer, S. J. (2001). The interference of stereotype threat with women’s generation of mathematical problem-solving strategies. Journal of Social Issues, 57(1), 55-71.
  33. Rossman, A., Chance, B., Medina, E., & Obispo, C. P. S. L. (2006). Some key comparisons between statistics and mathematics, and why teachers should care. In Thinking and Reasoning with Data and Chance: Sixty-Eighth Annual Yearbook of the National Council of Teachers of Mathematics (pp. 323-333).
  34. Saidi, S. S., & Siew, N. M. (2019). Assessing students' understanding of the measures of central tendency and attitude towards statistics in rural secondary schools. International Electronic Journal of Mathematics Education, 14(1), 73-86.
  35. Schram, C. M. (1996). A meta-analysis of gender differences in applied statistics achievement. Journal of Educational and Behavioral Statistics, 21(1), 55-70.
  36. Smith, J. L., & White, P. H. (2001). Development of the domain identification measure: A tool for investigating stereotype threat effects. Educational and Psychological Measurement, 61(6), 1040-1057.
  37. Smith, J. L., & White, P. H. (2002). An examination of implicitly activated, explicitly activated, and nullified stereotypes on mathematical performance: It’s not just a woman’s issue. Sex Roles, 47(3-4), 179-191.
  38. Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women’s math performance. Journal of Experimental Social Psychology, 35(1), 4-28.
  39. Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69(5), 797-811.
  40. Stern, D., Stern, R., Parsons, D., Musyoka, J., Torgbor, F., & Mbasu, Z. (2020). Envisioning change in the statistics-education climate. Statistics Education Research Journal, 19(1), 206-225.
  41. Stroup, D. F., & Jordan, E. W. (1982). Statistics: Monster in the university. In Proceedings of Statistical Education, the American Statistical Association (pp. 135-138).
  42. Susbiyanto, S., Kurniawan, D. A., Perdana, R., & Riantoni, C. (2019). Identifying the mastery of research statistical concept by using problem-based learning. International Journal of Evaluation and Research in Education, 8(3), 461-469.
  43. Taasoobshirazi, G., Wagner, M., Brown, A., & Copeland, C. (2022). An evaluation of college students’ perceptions of statisticians. Journal of Statistics and Data Science Education, 30(2), 138-153.
  44. Witherspoon, E. B., & Schunn, C. D. (2020). Locating and understanding the largest gender differences in pathways to science degrees. Science Education, 104, 144-163.
  45. Woehlke, P. L., & Leitner, D. W. (1980). Gender differences in performance on variables related to achievement in graduate-level educational statistics. Psychological Reports, 47(3), 1119-1125.
  46. Yates, A., Starkey, L., Egerton, B., & Flueggen, F. (2021). High school students’ experience of online learning during COVID-19: The influence of technology and pedagogy. Technology, Pedagogy and Education, 30(1), 59-73.