Can Holland’s Person-Environment Fit Theory Produce Troubling Outcomes for Racial/Ethnic Underrepresented Students in STEM? An Analysis of Social Agency

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Juan Carlos Garibay

Abstract

Increasing the success of Underrepresented Students of Color (USC) in science, technology, engineering, and mathematics (STEM) is a central concern to many researchers, policymakers, and educators. To help understand STEM college student success, many studies have utilized Holland’s (1966, 1973, 1985, 1997) person-environment fit framework applying it uncritically to all students. Using Quantitative Criticalism, this study engages the racial realities of USC while investigating several assumptions of Holland’s theory and their implications for USC pursuing STEM fields. Utilizing a national, longitudinal dataset of 5,564 STEM bachelor’s degree recipients drawn from the Cooperative Institutional Research Program’s 2004 Freshman Survey and 2011 Post-Baccalaureate Survey, this study specifically examines students’ interest in making a positive impact on society through socio-political action, or social agency, which Holland’s typology suggests is incongruent with STEM environments. Findings show that USC may be more likely to be described as “incongruent” with Holland’s classification of STEM environments, that the congruence assumption may not be fully applicable for understanding the long-term success of USC in STEM, and that the social agency of USC did not significantly change over the seven years while white students’ significantly decreased. Implications for broadening participation and promoting equity in STEM fields are discussed.

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References

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

Baillie, C., Pawley, A. L., & Riley, D. (2011). Engineering and social justice in the university and beyond. Purdue University Press.

Beckwith, J., & Huang, F. (2005). Should we make a fuss? A case for social responsibility in science. Nature Biotechnology, 23(12), 1479-1480. DOI: https://doi.org/10.1038/nbt1205-1479

Bensimon, E. M., & Bishop, R. (2012). Why “critical”? The need for new ways of knowing. The Review of Higher Education, 36(1), 1-7. DOI: https://doi.org/10.1353/rhe.2012.0046

Calabrese Barton, A. (2001). Science education in urban settings: Seeking new ways of praxis through critical ethnography. Journal of Research in Science teaching, 38(8), 899-917. DOI: https://doi.org/10.1002/tea.1038

Calabrese Barton, A., & Tan, E. (2011). Why democratic science teaching matters. In S. J., Basu, A. Calabrese Barton, & E. Tan (Eds.), Democratic science teaching: Building the expertise to empower low-income minority youth in science (pp. 115-120). Sense Publishers.

Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching, 44(8), 1187-1218. DOI: https://doi.org/10.1002/tea.20237

Chang, M. J., Sharkness, J., Hurtado, S., & Newman, C. (2014). What matters in college for retaining aspiring scientists and engineers from underrepresented racial groups. Journal of Research in Science Teaching, 51(5), 555-580. DOI: https://doi.org/10.1002/tea.21146

Charleston, L. J. (2012). A qualitative investigation of African Americans’ decision to pursue computing science degrees: Implications for cultivating career choice and aspiration. Journal of Diversity in Higher Education, 5(4), 222-243. DOI: https://doi.org/10.1037/a0028918

Chen, P. D., & Simpson, P. A. (2015). Does personality matter? Applying Holland’s typology to analyze students’ self-selection into Science, Technology, Engineering, and Mathematics Majors. Journal of Higher Education, 86(5), 725-750.

Feldman, K. A., Ethington, C. A., & Smart, J. C. (2001). A further investigation of major field and person-environment fit: Sociological versus psychological interpretations of Holland’s theory. Journal of Higher Education, 72, 670-698. DOI: https://doi.org/10.1080/00221546.2001.11777121

Feldman, K. A., Smart, J. C., & Ethington, C. A. (2008). Using Holland’s theory to study patterns of college student success: The impact of major fields on students. In J. C. Smart (Ed.) Higher Education: Handbook of Theory and Research (Vol. 23, pp. 329-380). Springer.

Feldman, K. A., Smart, J. C., & Ethington, C. A. (2004). What do college students have to lose? Exploring the outcomes of differences in Person-Environment Fits. Journal of Higher Education, 75(5), 528-555. DOI: https://doi.org/10.1353/jhe.2004.0029

Feldman, K. A., Smart, J. C., & Ethington, C. A. (1999). Major field and person-environment fit: Using Holland’s theory to study change and stability of college students. Journal of Higher Education, 70, 642-669. DOI: https://doi.org/10.1080/00221546.1999.11780802

Frankenstein, M. (1983). Critical mathematics education: An application of Paulo Freire’s epistemology. Journal of Education, 165(4), 315-340. DOI: https://doi.org/10.1177/002205748316500403

Frankenstein, M. (2012). Beyond math content and process: Proposals for underlying aspects of social justice education. In A. A. Wager, & D. W. Stinson (Eds.), Teaching Mathematics for Social Justice: Conversations with Educators (pp. 49-62). National Council of Teachers of Mathematics.

Garibay, J. C. (2015). STEM students’ social agency and views on working for social change: Are STEM disciplines developing socially and civically responsible students? Journal of Research in Science Teaching, 52(5), 610-632. DOI: https://doi.org/10.1002/tea.21203

Garibay, J. C. (2018). Beyond traditional measures of STEM success: Long-term predictors of social agency and conducting research for social change. Research in Higher Education, 59(3), 349-381. DOI: https://doi.org/10.1007/s11162-017-9470-2

Garibay, J. C., & Vincent, S. (2018). Racially Inclusive Climates Within Degree Programs and Increasing Student of Color Enrollment: An Examination of Environmental/Sustainability Programs. Journal of Diversity in Higher Education. 11(2), 201-220. DOI: https://doi.org/10.1037/dhe0000030

Gottfredson, G. D., & Holland, J. L. (1996). Dictionary of Holland occupational codes (3rd ed.). Psychological Assessment Resources.

Gutstein, E. (2006). Reading and writing the world with mathematics: Toward a pedagogy for social justice. Routledge.

Hammonds E. M., & Herzig, R. M. (2009). The nature of difference: Sciences of race in the United States from Jefferson to Genomics. The MIT Press.

Harding, S. (2006). Science and social inequality: Feminist and postcolonial issues. University of Illinois Press.

Harper, S. R., & Newman, C. B. (Eds.) (2010). Students of color in STEM. New Directions for Institutional Research, 148. Jossey-Bass.

Higher Education Research Institute [HERI], (2010). Degrees of Success: Bachelor’s degree completion rates among initial STEM majors. UCLA Higher Education Research Institute. http://www.heri.ucla.edu/nih/downloads/2010%20-%20Hurtado,%20Eagan,%20Chang%20-%20Degrees%20of%20Success.pdf

Holland, J. L. (1966). The psychology of vocational choice. Blaisdell.

Holland, J. L. (1973). Making vocational choices: A theory of vocational personalities and work environments. Prentice-Hall.

Holland, J. L. (1985). Making vocational choices: A theory of vocational personalities and work environments (2nd ed.). Prentice-Hall.

Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Psychological Assessment Resources.

Huang. Y., & Healy, C. C. (1997). The relations of Holland-typed majors to students’ freshman and senior work values. Research in Higher Education, 38, 455-477. DOI: https://doi.org/10.1023/A:1024914610562

Johnson, A. C. (2007). Unintended consequences: How science professors discourage women of color. Science Education, 91(5), 805-821. DOI: https://doi.org/10.1002/sce.20208

Jordan, T. (2006). Science and civic engagement: Changing perspectives from Dewey to DotNets. In J. J. Mintzes & W. H. Leonard (eds.), Handbook of College Science Teaching (pp. 387-394).: National Science teachers Association Press.

Lima, M. (2000). Service-Learning: A unique perspective on engineering education. In E. Tsang (ed.), Projects that matter: Concepts and models for service-learning in engineering (pp. 109-117). AAHE.

Martin, D. B. (2003). Hidden assumptions and unaddressed questions in mathematics for all rhetoric. The Mathematics Education, 13(2), 7-21.

McGee, E. O. (2020). Interrogating structural racism in STEM higher education. Educational Researcher. Advance online publication. DOI: https://doi.org/10.3102/0013189X20972718

McGee, E., & Bentley, L. (2017). The Equity Ethic: Black and Latinx College Students Reengineering their STEM Careers toward Justice. American Journal of Education, 124(1), 1-36. DOI: https://doi.org/10.1086/693954

McGee, E. O., White, D. T., Jenkins, A. T., Houston, S., Bentley, L. C., Smith, W. J., & Robinson, W. H. (2016). Black engineering students’ motivation for Ph.D. attainment: Passion plus purpose. Journal for Multicultural Education, 10(2), 167-193.

Milem, J. F., & Umbach, P. D. (2003). Examining the perpetuation hypothesis: The influence of pre-college factors on students’ predispositions regarding diversity activities in college. Journal of College Student Development, 45(5), 611-624. DOI: https://doi.org/10.1353/csd.2003.0056

Museus, S. D., Palmer, R. T., Davis, R. J., & Maramba, D. C. (2011). Racial and ethnic minority students’ success in STEM education. ASHE-Higher Education Report Series. Jossey-Bass.

National Academy of Sciences (NAS). (2007). Rising above the gathering storm: Engerginzing and employing America for a brighter economic future. Washington, DC: National Academies Press.

National Science Board (NSB). (2004). An emerging and critical problem of the science and engineering labor force: A companion to science and engineering indicators 2004. Retrieved from www.nsf.gov/statistics/nsb0407/nsb0407.pdf

National Science Foundation, National Center for Science and Engineering Statistics [NSF]. (2019). Women, Minorities, and Persons with Disabilities in Science and Engineering: 2019. Special Report NSF 19-304. https://ncses.nsf.gov/pubs/nsf19304/digest

National Science Foundation [NSF], National Center for Science and Engineering Statistics [NCSES]. (2017). Women, minorities, and persons with disabilities in science and engineering: 2017. Special Report NSF 17-310. https://www.nsf.gov/statistics/2017/nsf17310/static/downloads/nsf17310-digest.pdf

National Science Foundation [NSF], National Center for Science and Engineering Statistics [NCSES]. (2013). Women, minorities, and persons with disabilities in science and engineering: 2013. Special Report NSF 13-304. http://www.nsf.gov/statistics/wmpd/2013/pdf/nsf13304_digest.pdf

National Science Foundation [NSF]. (2011). Report on Women, Minorities and Persons with Disabilities. National Science Foundation. http://www.nsf.gov/statistics/wmpd/2013/start.cfm

Newman, C. B. (2011). Access and success for African American engineers and computer scientists: A case study of two predominantly white public research universities (Unpublished doctoral dissertation). University of California, Los Angeles.

Nicholls, G. M., Wolfe, H., Besterfield-Sacre, M., Shuman, L. J., & Larpkiattaworn, S. (2007). A method for identifying variables for predicting STEM enrollment. Journal of Engineering Education, 96(1), 33-44. DOI: https://doi.org/10.1002/j.2168-9830.2007.tb00913.x

Palmer, R. T., Maramba, D. C., & Gasman, M. (Eds.) (2012). Fostering success of ethnic and racial minorities in STEM: The role of minority serving institutions. Routledge.

Pike, G. R. (2006a). Students’ personality types, intended majors, and college expectations: Further evidence concerning psychological and sociological interpretations of Holland’s theory. Research in Higher Education, 47, 801-822. DOI: https://doi.org/10.1007/s11162-006-9016-5

Pike, G. R. (2006b). Vocational preferences and college expectations: An extension of Holland’s principle of self-selection. Research in Higher Education, 47, 591-612. DOI: https://doi.org/10.1007/s11162-005-9008-x

Porter, S. R., & Umbach, P. D. (2006). College major choice: An analysis of person-environment fit. Research in Higher Education, 47(4), 429-449. DOI: https://doi.org/10.1007/s11162-005-9002-3

President’s Council of Advisors on Science and Technology (PCAST). (2012). Engage to Excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics. www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-engage-to-excel.pdf

Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage Publishing.

Reardon, R., & Bullock, E. (2004). Holland’s theory and implications for academic advising and career counseling. NACADA Journal, 24, 111-122. DOI: https://doi.org/10.12930/0271-9517-24.1-2.111

Rendón, L. I. (2006). Reconceptualizing success for underserved students in higher education. Washington, DC: National Postsecondary Education Cooperative. https://nces.ed.gov/npec/pdf/resp_Rendon.pdf

Rogosa, D., Brandt, D., & Zimowski, M. (1982). A growth curve approach to the measurement of change. Psychological Bulletin, 92(3), 726-748. DOI: https://doi.org/10.1037/0033-2909.92.3.726

Rosen, D., Holmberg, K., & Holland, J. L. (1989). The college majors finder. Psychological Assessment Resources.

Sax, L. J. (2000). Citizenship development and the American college student. In T. Ehrlich (ed.), Civic responsibility and higher education (pp. 3-18). Oryx Press.

Semega, J., Kollar, M., Creamer, J., & Mohanty, A. (2019). Income and poverty in US: 2018, US Census Bureau, Current Population Reports. US Government Printing Office.

Seymour, E., & Hewitt, N. (1997). Talking about leaving: Why undergraduates leave the sciences. Westview.

Sharkness, J., DeAngelo, L., & Pryor, J. (2010). CIRP Construct Technical Report. Los Angeles: UCLA Higher Education Research Institute. http://www.heri.ucla.edu/PDFs/constructs/Appendix2009.pdf

Smart, J. C., Feldman, K. A., & Ethington, C. A. (2000). Academic disciplines: Holland’s theory and the study of college students and faculty. Vanderbilt University Press.

Smart, J. C., Feldman, K. A., & Ethington, C. A. (2006). Holland’s theory and patterns of college student success. National Postsecondary Education Cooperative. http://nces.ed.gov/npec/pdf/smart_team_report.pdf

Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. Sage.

Stage, F. K. (2007). Answering critical questions using quantitative data. New Directions for Institutional Research, 133, 5-16. Jossey-Bass. DOI: https://doi.org/10.1002/ir.200

Tate, W. R. (1995). Returning to the root: A culturally relevant approach to mathematics pedagogy. Theory into Practice, 34(3), 166-173. DOI: https://doi.org/10.1080/00405849509543676

Umbach, P. D., & Milem, J. F. (2004). Applying Holland's typology to the study of differences in student views about diversity. Research in Higher Education, 45(6), 625-649. DOI: https://doi.org/10.1023/B:RIHE.0000040266.98138.dd

U.S. Department of Education National Center for Education Statistics. (n.d.). Classification of Instructional Programs (CIP 2000). http://nces.ed.gov/pubs2002/cip2000/index.asp.

Van der Leeden, R. (1998). Multilevel analysis of repeated measures data. Quality & Quantity, 32, 15-29. DOI: https://doi.org/10.1023/A:1004233225855

Vaz, R. F. (2005). Connecting science and technology education with civic understanding: A model for engagement. Peer Review, 7(2), 13-16.

Zuñiga, X., Williams, E. A., & Berger, J. B. (2005). Action-oriented democratic outcomes: The impact of student involvement with campus diversity. Journal of College Student Development, 46(6), 660-678. DOI: https://doi.org/10.1353/csd.2005.0069