Modeling Entrance into STEM Fields of Study Among Students Beginning at Community Colleges and Four-Year Institutions
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In this study, a theoretical model is tested to examine factors shaping the decision to pursue STEM fields of study among students entering community colleges and four-year institutions, based on a nationally representative sample of high school graduates from 2004. Applying the social cognitive career theory and multi-group structural equation modeling analysis, this research highlights a number of findings that may point to specific points of intervention along students’ educational pathway into STEM. This study also reveals important heterogeneity in the effects of high school and postsecondary variables based on where students start their postsecondary education: community colleges or four-year institutions. For example, while high school exposure to math and science courses appears to be a strong influence on four-year beginners’ STEM interest, its impact on community college beginners’ STEM interest, albeit being positive, is much smaller. In addition, college academic integration and financial aid receipt exhibit differential effects on STEM entrance, accruing more to four-year college students and less to those starting at community colleges.
KeywordsCommunity college students STEM education Choice of major Social cognitive career theory Multi-group structural equation modeling
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 RG11-07. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the Association for Institutional Research, the National Center for Education Statistics, the National Science Foundation or the National Postsecondary Education Cooperative. I thank Kelly Wickersham, Hsun-yu Chan, and two anonymous reviewers for their valuable and insightful feedback.
- ACT. (2006). Developing the STEM education pipeline. Iowa City, IA: ACT.Google Scholar
- Adelman, C. (1998). Women and men of the engineering path: A model for analysis of undergraduate careers. Retrieved from http://www.erc-assoc.org/nsf/engrg_paths/EPMONOG.pdf. Retrieved 20 Mar 2012.
- Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.Google Scholar
- Anderson, E., & Kim, D. (2006). Increasing the success of minority students in science and technology. Washington, DC: American Council on Education.Google Scholar
- Arum, R., & Roksa, J. (2011). Academically adrift: Limited learning on college campuses. Chicago, IL: University of Chicago Press.Google Scholar
- Astin, A. W. (1993). What matters in college? Four critical years revisited. San Francisco: Jossey-Bass.Google Scholar
- Austin, A. E. (1990). Faculty cultures, faculty values. In W. G. Tierney (Ed.), Assessing academic climates and cultures (Vol. 68, pp. 61–74). San Francisco: Jossey-Bass.Google Scholar
- Bailey, T. R., & Alfonso, M. (2005). Paths to persistence: An analysis of research on program effectiveness at community colleges (New Agenda Series Vol. 6, No. 1). Indianapolis, IN: Lumina Foundation for Education. Retrieved from http://www.luminafoundation.org/publications/PathstoPersistence.pdf. Retrieved 20 Mar 2012.
- Bailey, T. R., Calcagno, J. C., Jenkins, D., Kienzl, G., & Leinbach, T. (2005). Community college student success: What institutional characteristics make a difference? Retrieved from http://cfder.org/uploads/3/0/4/9/3049955/community_college_student_success_what_institutional_characteristics_make_a_difference.pdf. Retrieved 20 Mar 2012.
- Bailey, T. R., Jenkins, D., & Leinbach, T. (2005). Graduation rates, student goals, and measuring community college effectiveness (CCRC Brief No. 28). New York: Teachers College, Columbia University.Google Scholar
- Bailey, T. R., Leinbach, D. T., Scott, M., Alfonso, M., Kienzl, G. S., & Kennedy, B. (2003). The characteristics of occupational sub-baccalaureate students entering the new millennium. New York: Teachers College, Columbia University.Google Scholar
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
- Benitez, M. (1998). Hispanic-serving institutions: Challenges and opportunities. In J. P. Merisotis & C. T. O’Brient (Eds.), New directions for higher education (No. 104, pp. 57–68). San Francisco: Jossey-Bass.Google Scholar
- Berglund, P. A. (2010). An introduction to multiple imputation of complex sample data using SAS v9.2. SAS Global Forum 2010, Paper 265-2010. Retrieved from http://support.sas.com/resources/papers/proceedings10/265-2010.pdf. Retrieved 20 Mar 2012.
- Burke, R., & Mattis, M. (2007). Women and minorities in science, technology, engineering and mathematics: Upping the numbers. Northampton, MA: Edward Elgar Publishing, Inc.Google Scholar
- Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). New York: Routledge.Google Scholar
- Chen, X., & Weko, T. (2009). Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education (NCES 2009-161). Washington, DC: National Center for Education Statistics.Google Scholar
- Cohen, A., & Brawer, F. (2008). The American community college (5th ed.). San Francisco: Jossey-Bass.Google Scholar
- Commission on Professionals in Science and Technology—CPST. (2007). Is US science and technology adrift? Washington, DC: CPST. Retrieved from http://www.cpst.org/STEM/STEM8_Report.pdf. Retrieved 20 Mar 2012.
- Crisp, G., Nora, A., & Taggart, A. (2009). Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a Hispanic Serving Institution. American Educational Research Journal, 46(4), 924–942.CrossRefGoogle Scholar
- Dayton, B., Gonzalez-Vasquez, N., Martinez, C. R., & Plum, C. (2004). In A. Ortiz (Ed.), New directions for student services (No. 105, pp. 29–39). San Francisco: Jossey-Bass.Google Scholar
- Dowd, A. C. (2008). The community college as gateway and gatekeeper: Moving beyond the access “saga” to outcome equity. Harvard Educational Review, 77(4), 407–419.Google Scholar
- Dowd, A. C. (2011). Developing supportive STEM community college to four-year college and university transfer ecosystems. In S. Olson & J. B. Labov (Eds.), Community colleges in the evolving STEM education landscape (pp. 107–134). Washington, DC: The National Academies Press.Google Scholar
- Hall, C., Dickerson, J., Batts, D., Kauffmann, P., & Bosse, M. (2011). Are we missing opportunities to encourage interest in STEM fields? Journal of Technology Education, 23(1), 32–46.Google Scholar
- Herrera, F. A., & Hurtado, S. (2011, April). Maintaining initial interests: Developing science, technology, engineering, and mathematics (STEM) career aspirations among underrepresented racial minority students. Paper presented at the Association for Educational Research annual meeting, New Orleans, LA.Google Scholar
- Herrera, F. A., Hurtado, S., & Chang, M. (2011). Maintaining career aspirations in science, technology, engineering, and mathematics (STEM) among college students. Retrieved from http://www.heri.ucla.edu/nih/downloads/ASHE2011HerreraSTEMCareers.pdf. Retrieved 20 Mar 2012.
- Holland, J. L. (1973). Making vocational choices: A theory of careers. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Holland, J. L. (1985). Making vocational choices: A theory of vocational personalities and work environments. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.Google Scholar
- Hurtado, S., Eagan, K., & Hughes, B. (2012, June). Priming the pump or sieve: Institutional contexts and URM STEM degree attainments. Paper presented at the Association for Institutional Research annual forum, New Orleans, LA.Google Scholar
- Kaplan, D. (2009). Structural equation modeling: Foundations and extensions (2nd ed.). Thousand Oaks, CA: Sage Publications.Google Scholar
- Karp, M. M., Hughes, K. L., & O’Gara, L. (2008). An exploration of Tinto’s integration framework for community college students. CCRC Working Paper No. 12. Teachers College, Columbia University, New York.Google Scholar
- Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.Google Scholar
- Lamport, M. A. (1993). Student–faculty informal interaction and the effect on college student outcomes: A review of the literature. Adolescence, 28(112), 971–990.Google Scholar
- Langdon, D., McKittrick, G., Beede, D., Khan, B., & Doms, M. (2011). STEM: Good jobs now and for the future. Washington, DC: U.S. Department of Commerce Economics and Statistics Administration. Retrieved from: http://www.esa.doc.gov/sites/default/files/reports/documents/stemfinalyjuly14_1.pdf. Retrieved 20 Mar 2012.
- Lent, R. W., Brown, S. D., & Hackett, G. (1996). Career development from a social cognitive perspective. In D. Brown, L. Brooks, & Associates (Eds.), Career choice and development (3rd ed., pp. 373–421). San Francisco: Jossey-Bass.Google Scholar
- Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. In D. Brown (Ed.), Career choice and development (pp. 255–311). San Francisco: Jossey-Bass.Google Scholar
- Lent, R. W., Brown, S. D., Sheu, H., Schmidt, J., Brenner, B., Gloster, C. S., … Treistman, D. (2005). Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically Black universities. Journal of Counseling Psychology, 52(1), 84–92.Google Scholar
- Long, B. T. (2005). The remediation debate: Are we serving the needs of underprepared college students? National CrossTalk, 13(4), 11–12.Google Scholar
- Lowell, B. L., & Regets, M. (2006). A half-century snapshot of the STEM workforce, 1950–2000. Washington, DC: Commission on Professionals in Science and Technology. Retrieved from http://www.cpst.org/STEM/STEM_White1.pdf. Retrieved 20 Mar 2012.
- Millar, B. (2010). Community college students’ perceptions of academic readiness. Doctoral dissertation. Retrieved from ProQuest dissertations and theses (Dissertation/thesis No. 3422728).Google Scholar
- Muthén, L. K., & Muthén, B. O. (1998–2010). Mplus user’s guide (6th ed.). Los Angeles: Muthén & Muthén.Google Scholar
- Nakajima, M. (2008). What factors influence student persistence in the community college setting? Dissertation Abstracts International Section A: Humanities and Social Sciences, 69(9-A), 3455.Google Scholar
- National Academies 2005 “Rising Above the Gathering Storm” Committee. (2010). Rising above the gathering storm: Rapidly approaching Category 5. Washington, DC: Author.Google Scholar
- National Research Council and National Academy of Engineering. (2012). Community colleges in the evolving STEM education landscape: Summary of a summit. Steve Olson and Jay B. Labov, Rapporteurs. Planning Committee on Evolving Relationships and Dynamics Between Two- and Four-Year Colleges and Universities, Board on Higher Education and Workforce, Division on Policy and Global Affairs; Board on Life Sciences, Division on Earth and Life Studies; Board on Science Education, Division on Behavioral and Social Sciences and Education; Engineering Education Program Office, National Academy of Engineering and Teacher Advisory Council, Division on Behavioral and Social Sciences and Education, Engineering Education Program Office, National Academy of Engineering. Washington, DC: The National Academies Press.Google Scholar
- National Science Foundation. (2010). Science and Engineering Indicators 2010. Arlington, VA: Author.Google Scholar
- Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: Vol. 2. A third decade of research. San Francisco: Jossey-Bass.Google Scholar
- Rosenbaum, J. (2001). College for all. New York: Russell Sage Foundation.Google Scholar
- Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
- Schunk, D. H., & Miller, S. D. (2002). Self-efficacy and adolescents’ motivation. In F. Pajares & T. Urdan (Eds.), Academic motivation of adolescents (pp. 29–52). Greenwich, CT: Information Age.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, TN: Vanderbilt University Press.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), 549–569.Google Scholar
- Starobin, S. S, & Laanan, F. S. (2008). Broadening female participation in science, technology, engineering, and mathematics: Experiences at community colleges. In J. Leaster (Ed.), New directions for community colleges (No. 142, pp. 37–46). San Francisco: Jossey-Bass.Google Scholar
- Terenzini, P. T., Pascarella, E. T., & Blimling, G. S. (1999). Students’ out-of-class experiences and their influence on learning and cognitive development: A literature review. Journal of College Student Development, 40(5), 610–622.Google Scholar
- Towns, M. H. (2010). Where are the women of color? Data on African American, Hispanic, and native American faculty in STEM. Journal of Science and Teaching, 39(4), 8–9.Google Scholar
- Townsend, B. K. (2001). Blurring the lines: Transforming terminal education to transfer education. In New directions for community colleges (No. 115, pp. 63–71). San Francisco: Jossey-Bass.Google Scholar
- U.S. Department of Labor. (2007). The STEM workforce challenge: The role of the public workforce system in a national solution for a competitive science, technology, engineering, and mathematics (STEM) workforce. Washington, DC: Author.Google Scholar
- Wang, X. (2012). Modeling student choice of STEM fields of postsecondary study: Testing a conceptual framework of motivation, high school learning, and postsecondary context of support. Retrieved from http://www.wiscape.wisc.edu/Publications/Publication.aspx?ID=bd56af78-cf50-473b-92fa-58739151f296. Retrieved 20 Mar 2012.