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Career Outcomes of STEM and Non-STEM College Graduates: Persistence in Majored-Field and Influential Factors in Career Choices

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Abstract

Using data from a nationally representative, longitudinal survey of college graduates, this study examines student transition from college to their chosen career paths and identifies factors influencing college graduates’ choosing an occupation related to ones’ undergraduate major. Within the context of expanded econometric framework a wide range of variables are considered, including monetary and nonmonetary costs and benefits as well as cultural and social capital measures. Using multinomial logit regression analyses, the results suggest positive career outcomes associated with individuals who have an occupation closely related to their college major, such as a better income profile and greater job satisfaction. Major-based differences are also examined between STEM and non-STEM graduates, and patterns of changes are documented for 10 years after graduation. An important perspective offered by this study is to consider career outcome as an extended definition of institutional effectiveness and student success. Based on the empirical findings, policy implications are discussed with the hope of bringing attention and improvement to the relationship between the higher educational system and the labor market.

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Notes

  1. In order to generate the major/job congruency measure for 2003, a 3-step approach was taken: (1) student’s response was copied from 1997 to 2003 if the individual answered “no” to the question on the 2003 survey whether s/he had “more than one career in the last 10 years;” (2) Similarly, student’s response was copied from 1997 to 2003 if an individual’s answer was greater than “5” to the question on the 2003 survey how many “years pursuing career in the industry.” At this point, an unweighted sample size of 5995 out of 8969 (weighted percentage was approximately 67.5 %) had a valid value for the major/job congruency. (3) In the last step, a Bayesian network (BN) was constructed to model the relationship between major/job congruence and a list of related measures (e.g., gender, cumulative GPA in undergraduate major, STEM versus non-STEM undergraduate major, undergraduate major in 12 categories, and occupation in 2003) for the 5995 individuals, and the final BN model had a prediction accuracy of 80 %. The BN model was then applied to the remaining 2974 individuals to predict their major/job congruency.

References

  • Austin, A., & Austin, H. (1993). Undergraduate science education: The impact of different college environment and the educational pipeline in the college. Final report. ED 362 404.

  • Behrman, J., Kletzer, L., McPherson, M., & Schapiro, M. O. (1998). Microeconomics of college choice, careers, and wages. Annals of the American Academy of Political and Social Science, 559, 12–23.

    Article  Google Scholar 

  • Bourdieu, P., & Passeron, J. C. (1977). Reproduction in education, society, and culture. London: Sage.

    Google Scholar 

  • Bourdieu, P., & Passeron, J. C. (1979). The inheritors: French students and their relations to culture. Chicago: University of Chicago Press.

    Google Scholar 

  • Cabrera, A. F. (1994). Logistic regression analysis in higher education: An applied perspective. Higher Education: In J. C. Smart (Ed.), Handbook for the study of higher education (Vol. 10). New York, NY: Agathon Press.

  • Campbell, G., Denes, R., & Morrison, C. (2000). Access denied: Race, ethnicity, and the scientific enterprise. Oxford: Oxford University Press.

    Google Scholar 

  • Chang, M. J., Eagan, M. K., Lin, M. H., & Hurtado, S. (2011). Considering the impact of racial stigmas and science identity: Persistence among biomedical and behavioral science aspirants. The Journal of Higher Education, 82(5), 564–596.

    Article  Google Scholar 

  • Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Supplement), 95–120.

    Article  Google Scholar 

  • Ehrenberg, R. G. (1991). Academic labor supply. In C. T. Clotfelter, R. G. Ehrenberg, M. Getz, & J. J. Siegfried (Eds.), Economic challenges in higher education (pp. 143–260). Chicago: University of Chicago Press.

  • Ethington, C. A., & Smart, J. C. (1986). Persistence to graduate education. Research in Higher Education, 24(3), 287–303.

    Article  Google Scholar 

  • Flyer, F. A. (1997). The influence of higher moments of earnings distributions of career decisions. Journal of Labor Economics, 15(4), 689–713.

    Article  Google Scholar 

  • Fox, M. (1992). Student debt and enrollment in graduate and professional school. Applied Economics, 24, 669–677.

    Article  Google Scholar 

  • Frehill, L. M., Di Fabio, N. M., & Hill, S. T. (2008). Confronting the ‘‘new’’ American dilemma. White Plains National Action Council for Minorities in Engineering. Retrieved February 19, 2012 from http://www.nacme.org/user/docs/NACME%2008%20ResearchReport.pdf

  • Goyette, K. A., & Mullne, A. L. (2006). Who studies the arts and sciences? Social background and the Choice and consequences of undergraduate field of study. The Journal of Higher Education, 77(3), 497–538.

    Article  Google Scholar 

  • Griffith, A. L. (2010). Persistent of women and minorities in the STEM field majors: Is it the school that matters. Economics of Education Review, 29, 911–922.

    Article  Google Scholar 

  • Hosmer, D., & Lemeshow, S. (2000). Applied logistic regression analysis (2nd ed.). New York, NY: Wiley.

    Book  Google Scholar 

  • Hurtado, S., Eagan, M. K., Cabrera, N. L., Lin, M. H., Park, J., & Lopez, M. (2008). Training future scientists: Predicting first-year minority student participation in health science research. Research in Higher Education, 49(2), 126–152.

    Article  Google Scholar 

  • Hurtado, S., Han, J. C., Sae′nz, V. B., Espinosa, L. L., Cabrera, N. L., & Cerna, O. S. (2007). Predicting transition and adjustment to college: Biomedical and behavioral science aspirants’ and minority students’ first year of college. Research in Higher Education, 48(7), 841–887.

    Article  Google Scholar 

  • Ishida, H., Spilerman, S., & Su, K. (1997). Educational credentials and promotion changes in Japanese and American organizations. American Sociological Review, 62(6), 866–882.

    Article  Google Scholar 

  • Jackson, G. A. (1990). Financial aid, college entry, and affirmative action. American Journal of Education, 98, 523–550.

    Google Scholar 

  • Jacobs, J. A. (1986). The sex-segregation of fields of study: Trends during the college years. Journal of Higher Education, 57(2), 134–154.

    Article  Google Scholar 

  • Jacobs, J. A. (1995). Gender and academic specialties: Trends among recipients of college degrees in the 1980 s. Sociology of Education, 68(2), 81–98.

    Article  Google Scholar 

  • Joy, L. (2000, May). Do colleges shortchange women? Gender differences in the transition from college to work. Papers and proceedings of the one hundred twelfth annual meeting of the American economic association (pp. 471–475).

  • Keane, M. P., & Wolpin, K. I. (1997). The career decisions of young men. Journal of Political Economy, 105(3), 473–522.

    Article  Google Scholar 

  • Lamont, M., & Lareau, A. (1988). Cultural capital: Allusions, gaps and glissandos in recent theoretical developments. Sociological Theory, 6, 153–168.

    Article  Google Scholar 

  • Melguizo, T., & Wolniak, G. (2011). The earnings benefits of majoring in STEM fields among high achieving minority students. Research in Higher Education (online preprint).

  • Montmarquette, C., Cannings, K., & Mahseredjian, S. (2002). How do young people choose college major? Economics of Education Review, 21, 543–556.

    Article  Google Scholar 

  • Morrow, V. (1999). Conceptualizing social capital in relation to the well-being of children and young people: A critical review. Sociological Review, 47, 744–765.

    Article  Google Scholar 

  • Mullen, A. L., Goyette, K. A., & Soares, J. A. (2003). Who goes to graduate school? Social and academic correlates of educational continuation after college. Sociology of Education, 76, 143–169.

    Article  Google Scholar 

  • Pampel, F. C. (2000). Logistic regression: A primer. In Sage University papers series on quantitative applications in the social sciences, 07-132. Thousand Oaks, CA: Sage Publications.

  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research. San Francisco: Jossey-Bass.

    Google Scholar 

  • Peng, C. J., So, T. S. H., Stage, F. K., & St. John, E. P. (2002). The use and interpretation of logistic regression in higher education journals. Research in Higher Education, 43, 259–294.

    Article  Google Scholar 

  • Perna, L. W. (2000). Differences in the decision to enroll in college among African Americans, Hispanics, and Whites. Journal of Higher Education, 71, 117–141.

    Article  Google Scholar 

  • Perna, L. W. (2004). Understanding the decision to enroll in graduate school: Sex and racial/ethnic group differences. The Journal of Higher Education, 75(5), 487–527.

    Article  Google Scholar 

  • Roksa, J. (2005). Double disadvantage or blessing in disguise? Understanding the relationship between college major and employment sector. Sociology of Education, 78(3), 207–232.

    Article  Google Scholar 

  • Roksa, J., & Levey, T. (2010). What can you do with that degree? College major and occupational status of college graduates over time. Social Forces, 89, 389–416.

    Article  Google Scholar 

  • Sax, L. J. (2001). Undergraduate science majors: Gender differences in who goes to graduate school. The Review of Higher Education, 24(2), 153–172.

    Article  Google Scholar 

  • Seibert, S. E., Kraimer, M. L., & Liden, R. C. (2001). A social capital theory of career success. The Academy of Management Journal, 44(2), 219–237.

    Article  Google Scholar 

  • Simpson, J. C. (2001). Segregated by subject: Racial differences in the factors influencing academic major between European Americans, Asian Americans, and African, Hispanic, and Native Americans. Journal of Higher Education, 72(1), 63–100.

    Article  Google Scholar 

  • Smart, J. C. (1986). College effects on occupational status attainment. Research in Higher Education, 24(1), 73–95.

    Article  Google Scholar 

  • Smart, J. C. (1988). College influences on graduates’ income levels. Research in Higher Education, 29, 41–59.

    Article  Google Scholar 

  • Staniec, J. F. O. (2004). The effects of race, sex, and expected returns on the choice of college major. Eastern Economic Journal, 30(4), 549–562.

    Google Scholar 

  • Stoecker, J. L., & Pascarella, E. T. (1991). Women’s college and women’s career attainments revisited. Journal of Higher Education, 62(4), 394–406.

    Article  Google Scholar 

  • Stolzenberg, R. M. (1994). Educational continuation by college graduates. American Journal of Sociology, 99, 1042–1077.

    Article  Google Scholar 

  • Thomas, S. L., & Heck, R. H. (2001). Analysis of large-scale secondary data in higher education research: Potential perils associated with complex sampling designs. Research in Higher Education, 42(5), 517–540.

    Article  Google Scholar 

  • Thomas, S. L., & Zhang, L. (2005). Post-baccalaureate wage growth within 4 years of graduation: The effects of college quality and college major. Research in Higher Education, 46(4), 437–459.

    Article  Google Scholar 

  • Weiler, W. C. (1991). The effect of undergraduate student loans on the decision to pursue post-baccalaureate study. Educational Evaluation and Policy Analysis, 13, 212–220.

    Google Scholar 

  • Wells, R. (2008). The effects of social and cultural capital on student persistence: are community colleges more meritocratic? Community College Review, 36(1), 25–46.

    Article  Google Scholar 

  • Whalen, D. F., & Shelley, M. C. (2010). Academic success for STEM and non-STEM majors. Journal of STEM Education, 11(1–2), 45–60.

    Google Scholar 

  • Wolniak, G. C., & Pascarella, E. T. (2005). The effects of college major and job field congruence on job satisfaction. Journal of Vocational Behavior, 67, 233–251.

    Article  Google Scholar 

  • Zhang, L. (2005). Advance to graduate education: The effect of college quality and undergraduate majors. The Review of Higher Education, 28(3), 313–338.

    Article  Google Scholar 

Download references

Acknowledgments

This material is based upon work supported by the Association for Institutional Research, the National Center for Education Statistics, the National Science Foundation, and the National Postsecondary Education Cooperative under Association for Institutional Research Grant Number RG11-12.

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Authors and Affiliations

  1. College of Education, University of Memphis, Ball Hall 100, Memphis, TN, 38152, USA

    Yonghong Jade Xu

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Correspondence to Yonghong Jade Xu.

Appendix

Appendix

Exploratory factor analysis of the fourteen items on career aspiration

 

Initial extraction

Factor loading

CHOICE01 Job choice depends on previous work experience

0.428

0.654

CHOICE02 Job choice depends on good starting income

Excluded

 

CHOICE03 Job choice depends on good income potential

0.240

0.490

CHOICE04 Job choice depends on job security

0.367

0.606

CHOICE05 Job choice depends on prestige and status

0.324

0.569

CHOICE06 Job choice depends on interesting work

0.378

0.614

CHOICE07 Job choice depends on intellectual work

0.313

0.560

CHOICE08 Job choice depends on freedom at work

0.661

0.813

CHOICE09 Job choice depends on interaction with people

0.429

0.655

CHOICE10 Job choice depends on working independently

0.587

0.766

CHOICE11 Job choice depends on travel

Excluded

 

CHOICE12 Job choice depends on ability to be established

0.448

0.670

CHOICE13 Job choice depends on time for other activities

0.575

0.758

CHOICE14 Job choice depends on other considerations

Excluded

 
  1. The extraction method used is principal component analysis

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Xu, Y.J. Career Outcomes of STEM and Non-STEM College Graduates: Persistence in Majored-Field and Influential Factors in Career Choices. Res High Educ 54, 349–382 (2013). https://doi.org/10.1007/s11162-012-9275-2

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