“But I’m Not Good at Math”: The Changing Salience of Mathematical Self-Concept in Shaping Women’s and Men’s STEM Aspirations

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    We included some factors that fell just below this threshold due to prior usage in several major studies (e.g., Astin 1993; Sax 2008).

  4. 4.

    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.

  5. 5.

    Table 7 provides logistic regression coefficients for the regression model, while Table 8 provides these data as Delta-P statistics.

References

  1. 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.

    Google Scholar 

  2. Astin, A. W. (1993). What matters in college?: Four critical years revisited. San Francisco: Jossey-Bass.

    Google Scholar 

  3. Bandura, A. (1997). Self-efficacy: The exercise of control. NewYork: Free man.

    Google Scholar 

  4. Blickenstaff, J. C. (2005). Women and science careers. Gender and Education, 17(2), 369–386.

    Article  Google Scholar 

  5. Bong, M. (1996). Problems in academic motivation research and advantages and disadvantages of their solutions. Contemporary Educational Psychology, 21(2), 149–165.

    Article  Google Scholar 

  6. Bong, M., & Clark, R. E. (1999). Comparison between self-concept and self-efficacy in academic motivation research. Educational Psychologist, 34(3), 139–153.

    Article  Google Scholar 

  7. Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15(1), 1–39.

    Article  Google Scholar 

  8. 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.

  9. 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.

    Article  Google Scholar 

  10. 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.

  11. Correll, S. J. (2001). Gender and the career choice process: The role of biased self-assessments1. American Journal of Sociology, 106(6), 1691–1730.

    Article  Google Scholar 

  12. 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.

    Google Scholar 

  13. 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.

    Google Scholar 

  14. Eccles, J. S. (1994). Understanding women’s educational and occupational choices. Psychology of Women Quarterly, 18(4), 585–609.

    Article  Google Scholar 

  15. 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.

    Article  Google Scholar 

  16. 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.

    Google Scholar 

  17. Ethington, C. A. (1988). Differences among women intending to major in quantitative fields of study. The Journal of Educational Research, 81(6), 354–359.

    Article  Google Scholar 

  18. 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.

  19. 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.

    Article  Google Scholar 

  20. Ginzberg, E., Ginsburg, S. W., Axelrad, S., & Herma, J. L. (1951). Occupational choice. New York: Columbia University Press.

    Google Scholar 

  21. 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.

  22. Gottfredson, L. S. (1981). Circumscription and compromise: A developmental theory of occupational aspirations. Journal of Counseling Psychology, 28, 545–579.

    Article  Google Scholar 

  23. 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.

    Google Scholar 

  24. 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.

    Article  Google Scholar 

  25. 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.

    Article  Google Scholar 

  26. 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.

    Article  Google Scholar 

  27. 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.

    Google Scholar 

  28. Lewis, S., Harris, R., & Cox, B. (2000). Engineering a better workplace: A diversity guide for the engineering profession. Melbourne: Swinburne University of Technology.

    Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. 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.

    Article  Google Scholar 

  32. 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.

    Article  Google Scholar 

  33. 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.

    Article  Google Scholar 

  34. 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.

    Article  Google Scholar 

  35. 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.

    Google Scholar 

  36. 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.

    Article  Google Scholar 

  37. 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.

    Article  Google Scholar 

  38. National Academy of Sciences. (2010). Rising above the gathering storm, revisited: Rapidly approaching Category 5. Washington, DC: National Academies Press.

    Google Scholar 

  39. 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.

  40. National Center for Education Statistics. (2013). Digest of Education Statistics 2013. Washington, DC: U.S. Department of Education.

    Google Scholar 

  41. National Science Board. (2012). Science and engineering indicators 2012. Arlington VA: National Science Foundation (NSB 12-01).

  42. Pajares, F. (2005). Gender differences in mathematics self-efficacy beliefs. New York, NY: Cambridge University Press.

    Google Scholar 

  43. 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.

    Article  Google Scholar 

  44. 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.

    Article  Google Scholar 

  45. 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.

    Google Scholar 

  46. 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.

    Google Scholar 

  47. 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.

    Article  Google Scholar 

  48. 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.

    Article  Google Scholar 

  49. Sadker, M., & Sadker, D. (1994). Failing at fairness: How America’s schools cheat girls. New York: Charles Scribner’s Sons.

    Google Scholar 

  50. 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.

    Article  Google Scholar 

  51. Sax, L. J. (1994b). Mathematical self-concept: How college reinforces the gender gap. Research in Higher Education, 35(2), 141–166.

    Article  Google Scholar 

  52. Sax, L. J. (2008). The gender gap in college: Maximizing the developmental potential of women and men. San Francisco: Jossey-Bass.

    Google Scholar 

  53. 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.

    Article  Google Scholar 

  54. Shavelson, R. J., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: Validation of construct interpretations. Review of educational research, 46(3), 407–441.

    Article  Google Scholar 

  55. 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.

  56. Sherman, J. (1982). Continuing in mathematics: A longitudinal study of the attitudes of high school girls. Psychology of Women Quarterly, 7(2), 132–140.

    Article  Google Scholar 

  57. Sherman, J. (1983). Factors predicting girls’ and boys’ enrollment in college preparatory mathematics. Psychology of Women Quarterly, 7(3), 272–281.

    Article  Google Scholar 

  58. Smart, J. C., & Pascarella, E. T. (1986). Self-concept development and educational degree attainment. Higher Education, 15(1–2), 3–15.

    Article  Google Scholar 

  59. Super, D. E., Brown, D., & Brooks, L. (1990). Career choice and development: Applying contemporary theories to practice. San Francisco: Jossey-Bass.

    Google Scholar 

  60. Tai, R. T., Liu, C. Q., Maltese, A. V., & Fan, X. T. (2006). Planning early for careers in science. Science, 312(5777), 1143–1144.

    Article  Google Scholar 

  61. Tobias, S. (1992). Revitalizing undergraduate science: Why some things work and most don’t. Tucson, AZ: Research Corporation.

    Google Scholar 

  62. 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.

    Article  Google Scholar 

  63. 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.

    Article  Google Scholar 

  64. 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.

    Article  Google Scholar 

  65. Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81.

    Article  Google Scholar 

  66. 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.

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the National Science Foundation, HRD #1135727.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Linda J. Sax.

Appendix

Appendix

See Tables 4, 5, 6, 7 and 8.

Table 4 Student’s probable major
Table 5 Factor variables, loadings, and reliabilities
Table 6 Descriptive statistics and coding for independent variables
Table 7 Logistic regression results (B coefficients) for men and women who enter specific STEM sub-fields versus all other majors (N = 415,281 men; 489,044 women)
Table 8 Delta-P statistics for men and women who enter specific STEM sub-fields versus all other majors. (N = 415,281 Men; 489,044 Women)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

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

Download citation

Keywords

  • STEM
  • Mathematical self-concept
  • Gender
  • College
  • Major selection