Abstract
Math self-concept (MSC) is considered an important predictor of the pursuit of science, technology, engineering and math (STEM) fields. Women’s underrepresentation in the STEM fields is often attributed to their consistently lower ratings on MSC relative to men. Research in this area typically considers STEM in the aggregate and does not account for variations in MSC that may exist between STEM fields. Further, existing research has not explored whether MSC is an equally important predictor of STEM pursuit for women and men. This paper uses a national sample of male and female entering college students over the past four decades to address how MSC varies across STEM majors over time, and to assess the changing salience of MSC as a predictor of STEM major selection in five fields: biological sciences, computer science, engineering, math/statistics, and physical sciences. Results reveal a pervasive gender gap in MSC in nearly all fields, but also a great deal of variation in MSC among the STEM fields. In addition, the salience of MSC in predicting STEM major selection has generally become weaker over time for women (but not for men). Ultimately, this suggests that women’s lower math confidence has become a less powerful explanation for their underrepresentation in STEM fields.
Similar content being viewed by others
Notes
Among the students included in the survey, 44 % attended public colleges and universities, 31 % were enrolled at private religious institutions, and the remaining 25 % attended private non-sectarian institutions.
To categorize which majors qualified as “STEM,” we took a twofold approach. First, we examined the National Center for Educational Statistics (NCES) Classification of Instructional Programs (NCES 2002), which helped us to narrow our broad list of majors into categories (noted in Appendix Table 4). Next, we examined these categories in concert with extant literature and prevailing definitions as used by the National Science Foundation (NSF) and the Department of Homeland Security (DHS) (Gonzalez and Kuenzi 2012). In doing so, we determined our list of STEM fields to include the five mentioned in the text, which are the most frequently used categories of STEM across these sources.
We opted to run binomial logistic regression because our interest was in the choice of each STEM major relative to all other STEM majors; future research may wish to use multinomial logistic regression to differentiate the choice to major in one specific STEM major versus another.
References
Aronson, J., & Steele, C. M. (2005). Stereotypes and the fragility of academic competence, motivation, and self-concept. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 436–456). New York, NY: Guilford.
Astin, A. W. (1993). What matters in college?: Four critical years revisited. San Francisco: Jossey-Bass.
Bandura, A. (1997). Self-efficacy: The exercise of control. NewYork: Free man.
Blickenstaff, J. C. (2005). Women and science careers. Gender and Education, 17(2), 369–386.
Bong, M. (1996). Problems in academic motivation research and advantages and disadvantages of their solutions. Contemporary Educational Psychology, 21(2), 149–165.
Bong, M., & Clark, R. E. (1999). Comparison between self-concept and self-efficacy in academic motivation research. Educational Psychologist, 34(3), 139–153.
Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15(1), 1–39.
Carnevale, A. P., Smith, N., & Melton, M. (2011). STEM: Science, technology, engineering, mathematics. Center for Education and the Workforce, Georgetown University. Retrieved from http://www9.georgetown.edu/grad/gppi/hpi/cew/pdfs/stem-complete.pdf.
Casey, M. B., Nuttall, R. L., & Pezaris, E. (1997). Mediators of gender differences in mathematics college entrance test scores: a comparison of spatial skills with internalized beliefs and anxieties. Developmental Psychology, 33(4), 669.
College Board (2011). SAT Percentile Ranks 2011. College-Bound Seniors—Critical Reading, Mathematics and Writing Percentile Ranks. (n.d.). Retrieved April 6, 2015, from http://media.collegeboard.com/digitalServices/pdf/SAT-Percentile_Ranks_2011.pdf.
Correll, S. J. (2001). Gender and the career choice process: The role of biased self-assessments1. American Journal of Sociology, 106(6), 1691–1730.
Eagan, K., Lozano, J. B., Hurtado, S., & Case, M. H. (2013). The American freshman: National norms fall 2013. Los Angeles: Higher Education Research Institute, UCLA.
Eagan, K., Stolzenberg, E. B., Ramirez, J. J., Aragon, M. C., Suchard, M. R., & Hurtado, S. (2014). The American freshman: National norms fall 2014. Los Angeles: Higher Education Research Institute, UCLA.
Eccles, J. S. (1994). Understanding women’s educational and occupational choices. Psychology of Women Quarterly, 18(4), 585–609.
Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D. (1993). Development during adolescence: the impact of stage-environment fit on young adolescents’ experiences in schools and in families. American Psychologist, 48(2), 90.
Eccles, J. S., Wigfield, A., & Schiefele, U. (1998). Social, emotional, and personality development. In W. Damon & N. Eisenberg (Eds.), Handbook of child psychology (5th ed., Vol. 3, pp. 1017–1095). Hoboken, NJ: Wiley.
Ethington, C. A. (1988). Differences among women intending to major in quantitative fields of study. The Journal of Educational Research, 81(6), 354–359.
Fairweather, J. (2008). Linking evidence and promising practices in science, technology, engineering, and mathematics (STEM) undergraduate education. Board of Science Education, National Research Council, The National Academies, Washington, DC.
Fredricks, J. A., & Eccles, J. S. (2002). Children’s competence and value beliefs from childhood through adolescence: Growth trajectories in two “male-typed” domains. Developmental Psychology, 38, 519–534.
Ginzberg, E., Ginsburg, S. W., Axelrad, S., & Herma, J. L. (1951). Occupational choice. New York: Columbia University Press.
Gonzalez, H.B., & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics (STEM) education: A primer. Retrieved from http://fas.org/sgp/crs/misc/R42642.pdf on March 1, 2012.
Gottfredson, L. S. (1981). Circumscription and compromise: A developmental theory of occupational aspirations. Journal of Counseling Psychology, 28, 545–579.
Hill, C., Corbett, C., & St Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. Washington, DC: American Association of University Women.
Kanny, M. A., Sax, L. J., & Riggers-Piehl, T. A. (2014). Investigating forty years of STEM research: How explanations for the gender gap have evolved over time. Journal of Women and Minorities in Science and Engineering, 20(2), 127–148.
Lent, R. W., Brown, S. D., & Gore, P. A, Jr. (1997). Discriminant and predictive validity of academic self-concept, academic self-efficacy, and mathematics-specific self-efficacy. Journal of Counseling Psychology, 44(3), 307–315.
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79–122.
Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. In Duane Brown and Associates (Ed.), Career choice and behavior (pp. 255–311). San Francisco, CA: Jossey Bass.
Lewis, S., Harris, R., & Cox, B. (2000). Engineering a better workplace: A diversity guide for the engineering profession. Melbourne: Swinburne University of Technology.
Marra, R. M., Rodgers, K. A., Shen, D., & Bogue, B. (2009). Women engineering students and self-efficacy: A multi-year, multi-institution study of women engineering student self-efficacy. Journal of Engineering Education, 98(1), 27–38.
Marsh, H. W. (1986). Global self-esteem: Its relation to specific facets of self-concept and their importance. Journal of Personality and Social Psychology, 51(6), 1224–1236.
Marsh, H. W. (1989). Effects of single-sex and coeducational schools: A response to Lee and Bryk. Journal of Educational Psychology, 81(4), 651–653.
Marsh, H. W., & Martin, A. J. (2011). Academic self-concept and academic achievement: Relations and causal ordering. British Journal of Educational Psychology, 81(1), 59–77.
Marsh, H. W., Smith, I. D., & Barnes, J. (1985). Multidimensional self-concepts: Relations with sex and academic achievement. Journal of Educational Psychology, 77(5), 581.
Marsh, H. W., & Yeung, A. S. (1998). Longitudinal structural equation models of academic self-concept and achievement: Gender differences in the development of math and English constructs. American Educational Research Journal, 35(4), 705–738.
McGraw, R., Lubienski, S. T., & Strutchens, M. E. (2006). A closer look at gender in NAEP mathematics achievement and affect data: Intersections with achievement, race/ethnicity, and socioeconomic status. Journal for Research in Mathematics Education, 37, 129–150.
Meece, J. L., Parsons, J. E., Kaczala, C. M., & Goff, S. B. (1982). Sex differences in math achievement: Toward a model of academic choice. Psychological Bulletin, 91(2), 324.
Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math anxiety and its influence on young adolescents’ course enrollment intentions and performance in mathematics. Journal of Educational Psychology, 82(1), 60–70.
National Academy of Sciences. (2010). Rising above the gathering storm, revisited: Rapidly approaching Category 5. Washington, DC: National Academies Press.
National Center for Education Statistics. (2002). Classification of Instructional Programs: 2000 Edition. Retrieved from http://nces.ed.gov/pubs2002/2002165.pdf on March 1, 2012.
National Center for Education Statistics. (2013). Digest of Education Statistics 2013. Washington, DC: U.S. Department of Education.
National Science Board. (2012). Science and engineering indicators 2012. Arlington VA: National Science Foundation (NSB 12-01).
Pajares, F. (2005). Gender differences in mathematics self-efficacy beliefs. New York, NY: Cambridge University Press.
Pajares, F., & Miller, M. D. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of Educational Psychology, 86, 193.
Pascarella, E. T., Smart, J. C., Ethington, C. A., & Nettles, M. T. (1987). The influence of college on self-concept: A consideration of race and gender differences. American Educational Research Journal, 24(1), 49–77.
Pryor, J. H., Eagan, K., Palucki Blake, L., Hurtado, S., Berdan, J., & Case, M. H. (2013). The American freshman: National norms fall 2012. Los Angeles: Higher Education Research Institute, UCLA.
Pryor, J. H., Hurtado, S., DeAngelo, L., Palucki Blake, L., & Tran, S. (2010). The American freshman: National norms fall 2010. Los Angeles: Higher Education Research Institute, UCLA.
Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women’s careers in science. Proceedings of the National Academy of Sciences, 111(12), 4403–4408.
Riegle-Crumb, C., Moore, C., & Ramos-Wada, A. (2011). Who wants to have a career in science or math? Exploring adolescents’ future aspirations by gender and race/ethnicity. Science Education, 95(3), 458–476.
Sadker, M., & Sadker, D. (1994). Failing at fairness: How America’s schools cheat girls. New York: Charles Scribner’s Sons.
Sax, L. J. (1994a). Predicting gender and major-field differences in mathematical self-concept during college. Journal of Women and Minorities in Science and Engineering, 1, 291–307.
Sax, L. J. (1994b). Mathematical self-concept: How college reinforces the gender gap. Research in Higher Education, 35(2), 141–166.
Sax, L. J. (2008). The gender gap in college: Maximizing the developmental potential of women and men. San Francisco: Jossey-Bass.
Sax, L. J., Bryant, A. N., & Harper, C. E. (2005). The differential effects of student-faculty interaction on college outcomes for women and men. Journal of College Student Development, 46(6), 642–659.
Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: Validation of construct interpretations. Review of educational research, 46(3), 407–441.
Shavlik, J., & Shavlik, M. (2004). Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 276-285). ACM.
Sherman, J. (1982). Continuing in mathematics: A longitudinal study of the attitudes of high school girls. Psychology of Women Quarterly, 7(2), 132–140.
Sherman, J. (1983). Factors predicting girls’ and boys’ enrollment in college preparatory mathematics. Psychology of Women Quarterly, 7(3), 272–281.
Smart, J. C., & Pascarella, E. T. (1986). Self-concept development and educational degree attainment. Higher Education, 15(1–2), 3–15.
Super, D. E., Brown, D., & Brooks, L. (1990). Career choice and development: Applying contemporary theories to practice. San Francisco: Jossey-Bass.
Tai, R. T., Liu, C. Q., Maltese, A. V., & Fan, X. T. (2006). Planning early for careers in science. Science, 312(5777), 1143–1144.
Tobias, S. (1992). Revitalizing undergraduate science: Why some things work and most don’t. Tucson, AZ: Research Corporation.
Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081–1121. doi:10.3102/0002831213488622.
Watt, H. M. (2000). Measuring attitudinal change in mathematics and English over the 1st year of junior high school: A multidimensional analysis. The Journal of Experimental Education, 68(4), 331–361.
Watt, H. M. (2006). The role of motivation in gendered educational and occupational trajectories related to maths. Educational Research and Evaluation, 12(4), 305–322.
Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81.
Wigfield, A., Eccles, J. S., Yoon, K. S., Harold, R. D., Arbreton, A. J. A., & Blumenfeld, P. C. (1997). Changes in children’s competence beliefs and subjective task values across the elementary school years: A three-year study. Journal of Educational Psychology, 89, 451–469.
Acknowledgments
This research is supported by the National Science Foundation, HRD #1135727.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sax, L.J., Kanny, M.A., Riggers-Piehl, T.A. et al. “But I’m Not Good at Math”: The Changing Salience of Mathematical Self-Concept in Shaping Women’s and Men’s STEM Aspirations. Res High Educ 56, 813–842 (2015). https://doi.org/10.1007/s11162-015-9375-x
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11162-015-9375-x