Choice of Academic Major at a Public Research University: The Role of Gender and Self-Efficacy
Females are underrepresented in certain disciplines, which translates into their having less promising career outlooks and lower earnings. This study examines the effects of socio-economic status, academic performance, high school curriculum and involvement in extra-curricular activities, as well as self-efficacy for academic achievement on choices of academic disciplines by males and females. Disciplines are classified based on Holland’s theory of personality-based career development. Different models for categorical outcome variables are compared including: multinomial logit, nested logit, and mixed logit. Based on the findings presented here, first generation status leads to a greater likelihood of choosing engineering careers for males but not for females. Financial difficulties have a greater effect on selecting scientific fields than engineering fields by females. The opposite is true for males. Passing grades in calculus, quantitative test scores, and years of mathematics in high school as well as self-ratings of abilities to analyze quantitative problems and to use computing are positively associated with choice of engineering fields.
KeywordsHolland’s theory of vocational choices Social cognitive theory Multinomial logit Nested logit Mixed logit Gender
- Adelman, C. (2003). Postsecondary attainment, attendance, curriculum, and performance: Selected results from the NELS: 88/2000 Postsecondary Education Transcript Study (PETS), 2000. Washington, DC: U.S. Department of Education.Google Scholar
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice Hall.Google Scholar
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman and Company.Google Scholar
- Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Greenwich: Information Age Publishing.Google Scholar
- Betz, N. E. (1994). Basic issues and concepts in career counseling for women. In W. N. Walsh & S. H. Osipow (Eds.), Career counseling for women (pp. 1–41). Hillsdale: Erlbaum.Google Scholar
- Bureau of Labor Statistics (2014–2015). Occupational outlook handbook. Retrieved March 15, 2015, from http://www.bls.gov/ooh.
- Cole, J. (2012). Using BCSSE and NSSE data to investigate first-year student financial stress and engagement. NSSE Webinar. Retrieved June 12, 2015, from http://www.nsse.indiana.edu/webinars/.
- Croissant, Y. (2015). Estimation of multinomial logit models in R: The mlogit Packages. Retrieved March 19, 2015, from http://www.cran.r-project.org/web/packages/mlogit/vignettes/mlogit.pdf.
- DiPrete, T. A.,& Buchmann, C. (2013). The rise of women: The growing gender gap in education and what it means for American schools. Russell Sage Foundation. Kindle Edition.Google Scholar
- Fitzgerald, L. F., & Weitzman, L. (1992). Women’s career development: Theory and practice from a feminist perspective. In Z. Leibowitz & D. Lea (Eds.), Adult career development: concepts, issues, and practices (pp. 125–157). Alexandria: National Career Development Association.Google Scholar
- Görlitz, K., & Gravert, K. (2015). The Effects of a High School Curriculum Reform on University Enrollment and the Choice of College Major. The Institute for the Study of Labor (IZA) Discussion Paper No.8983. Retrieved January 5, 2016, from http://www.ftp.iza.org/dp8983.pdf.
- Green, K. C. (1992). After the boom: Management majors in the 1990s. New York: McGraw-Hill.Google Scholar
- Hoetker, G. P. (2004). Confounded coefficients: Extending recent advances in the accurate comparison of logit and probit coefficients across groups. Available at SSRN 609104.Google Scholar
- Hole, A. R. (2007). Fitting mixed logit models by using maximum simulated likelihood. The Stata Journal, 7(3), 388–401.Google Scholar
- Holland, J. (1966). The psychology of vocational choice. Waltham: Blaisdell.Google Scholar
- Holland, J. (1973). Making vocational choices: A theory of careers. Englewood Cliffs: Prentice Hall.Google Scholar
- Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa: Psychological Assessment Resources Inc.Google Scholar
- Holland, J. L., & Lutz, S.W. (1967). Predicting a Student’s Vocational Choice. American College Testing Program. Retrieved March 17, 2015, from http://www.act.org/research/researchers/reports/pdf/ACT_RR18.pdf.
- James, E., Nabeel, A., Conaty, J., & To, D. (1989). College quality and future earnings: where should you send your child to college? American Economic Review, 79, 247–252.Google Scholar
- Jones, W. A. (2011). Variation among academic disciplines: An update on analytical frameworks and research. Journal of the Professoriate, 1(6), 9–27.Google Scholar
- Krumboltz, J. D. (1996). A learning theory of career counseling. In M. L. Savickas & W. B. Walsh (Eds.), Handbook of Career Counseling Theory and Practice (pp. 55–80). Palo Alto, CA: Davies-Black.Google Scholar
- Lackland, A. C. (2001). Students’ choices of college majors that are gender traditional and nontraditional. Journal of College Student Development, 42(1), 39–47.Google Scholar
- Long, J.S. (2009). Group Comparisons in Logit and Probit Using Predicted Probabilities. Retrieved from http://www.indiana.edu/~jslsoc/files_research/groupdif/groupwithprobabilities/groups-with-prob-2009-06-25.pdf.
- Long, J. S., & Freeze, J. (2014). Regression models for categorical dependent variables using stata. College Station: Stata Press.Google Scholar
- National Association of Colleges and Employers (2014). NACE salary survey. Retrieved from http://www.naceweb.org/uploadedFiles/Content/static-assets/downloads/executive-summary/2014-january-salary-survey-executive-summary.pdf.
- National Science Foundation (2014). Science and engineering indicators, SESTAT (1993–2010). Retrieved March 15, 2015, from http://www.sestat.nsf.gov.
- Oakes, J. (1990). Opportunities, achievement, and choice: Women and minority students in science and mathematics. Review of Research in Education, 16, 153–222.Google Scholar
- Pampel, F. C. (2000). Logistic regression: A primer. Sage University Papers Series on quantitative applications in the social sciences, 07–132. Thousand Oaks, CA: Sage.Google Scholar
- Patton, W., & McMahon, M. (1999). Career development and systems theory: A new relationship. Pacific Grove: Brooks/Cole.Google Scholar
- Psathas, G. (1968). Toward a theory of occupational choice for women. Sociology and Social Research, 52, 253–268.Google Scholar
- Smart, J. C., Feldman, K. A., & Ethington, C. A. (2000). Academic disciplines: Holland’s theory and the study of college students and faculty. Nashville: Vanderbilt University Press.Google Scholar
- Snyder, T. D., & Dillow, S. A. (2012). Digest of Education Statistics, 2011. Washington: U.S. Department of Education, National Center for Education Statistics. Retrieved August 30, 2012, from http://www.nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2012001.
- Train, K., & Croissant, Y. (2015). Kenneth train’s exercises using the mlogit package for R. Retrieved March 19, 2015, from http://www.cran.r-project.org/web/packages/mlogit/vignettes/Exercises.pdf.