Abstract
Back-and-forth enrollment at different institutions—student swirl—and concurrent enrollment at two or more institutions—double-dipping—have become common experiences for students in the United States. However, empirical studies explaining student mobility are rather rare. This study examines how student departures from and returns to a single institution are affected by college attendance elsewhere. The model presented here demonstrates that departure rates are higher for students concurrently attending another college. Return rates, on the other hand, are substantially lower for those students who attend other colleges after departure from the study institution. The effect of multi-institutional attendance differs by college type, with the effect of 4-year out-of-state institution attendance being most pronounced. The simultaneous analysis of departures and returns provides the study institution with a more accurate and complete picture of student mobility.
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Adelman, C. (1999). Answers in the toolbox: Academic intensity, attendance patterns, and Bachelor’s degree attainment. Washington, DC: U.S. Department of Education.
Adelman, C. (2005). Moving into town—and moving on: The community college in the lives of traditional-age students. Washington, DC: U.S. Department of Education.
Allen, J., Robbins, S. B., Casillas, A., & Oh, I. S. (2008). Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness. Research in Higher Education, 49(7), 647–664.
Bahr, P. R. (2009). Educational attainment as process: Using hierarchical discrete-time event history analysis to model rate of progress. Research in Higher Education, 50(7), 691–714.
Bahr, P. R. (2011). Student flow between community colleges: Investigating lateral transfer. Research in Higher Education (in print).
Barber, J. S., Murphy, S. A., Axinn, W. G., & Maples, J. (2000). Discrete-time multilevel hazard analysis. Sociological Methodology, 30(1), 201–235.
Becker, G. S. (1964). Human capital. New York: Columbia University Press.
Bontrager, B., Clemetsen, B., & Watts, T. (2005). Enabling student swirl: A community college/university dual enrollment program. College and University Journal, 80(4), 3–6.
Borden, V. M. H. (2004). Accommodating student swirl: When traditional students are no longer the tradition. Change, 36(2), 10–17.
Braxton, J. M. (2000). Reworking the student departure puzzle. Nashville: Vanderbilt University Press.
Caison, A. L. (2007). Analysis of institutionally specific retention research: A comparison between survey and institutional database methods. Research in Higher Education, 48(4), 435–451.
Cole, J. S., & Gonyea, R. M. (2010). Accuracy of self-reported SAT and ACT test scores: Implications for research. Research in Higher Education, 51(4), 305–319.
Cook, B., & Pullaro, N. (2010). College graduation rates: Behind the numbers. Washington, DC: American Council on Education.
De los Santos, A., Jr., & Wright, I. (1990). Maricopa’s swirling students: Earning one-third of Arizona state’s bachelor’s degrees. Community, Technical, and Junior College Journal, 4(6), 32–34.
DesJardins, S. L., Ahlburg, A. A., & McCall, B. P. (2002). Simulating the longitudinal effects of changes in financial aid on student departure from college. Journal of Human Resources, 37(3), 653–679.
DesJardins, S. L., Ahlburg, A. A., & McCall, B. P. (2006a). An Integrated model of application, admission, enrollment, and financial aid. Journal of Higher Education, 77(3), 381–429.
DesJardins, S. L., Ahlburg, A. A., & McCall, B. P. (2006b). The effects of interrupted enrollment on graduation from college: Racial, income, and ability differences. Economics of Education Review, 25, 575–590.
DesJardins, S. L., & McCall, B. P. (2010). Simulating the effects of financial aid packages on college student stopout, reenrollment spells, and graduation chances. The Review of Higher Education, 33(4), 513–541.
DesJardins, S. L., & Toutkoushian, R. K. (2005). Are students really rational? The development of rational thought and its application to student choice. In J.C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 20, pp. 191–240). Dordrecht: Kluwer.
Ehrenberg, R. G. (2005). The perfect storm and the privatization of public higher education. Working papers 59. Retrieved on April 15, 2011, from http://digitalcommons.ilr.cornell.edu/workingpapers/59
Goldrick-Rab, S., Harris, D. N., & Trostel, P. A. (2009). Why financial aid matters (or does not) for college success: Toward a new interdisciplinary perspective. In J. C. Smart (Ed.) Higher education: Handbook of theory and research (Vol. XXIV, pp. 389–426). New York: Agathon Press.
Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied choice analysis: A primer. New York: Cambridge University Press.
Herzog, S. (2005). Measuring determinants of student return vs. dropout vs. transfer: A first-to-second year analysis of new freshmen. Research in Higher Education, 46(8), 883–928.
Hoffman, J. L., & Lowitzki, K. E. (2005). Predicting college success with high school grades and test scores: Limitations for minority students. The Review of Higher Education, 28(4), 455–474.
Horn, L. (1998). Stopouts or stayouts? Undergraduates who leave college in their first year (NCES 1999–087). Washington, DC: U.S. Government Printing Office.
Hossler, D., Ziskin, M., Gross, J. P. K., & Kim, S. (2009). Student aid and its role in encouraging persistence. In J. C. Smart (Ed.) Higher education: Handbook of theory and research (Vol. XXIV, pp. 389–426). New York: Kluwer.
Johnson, I. Y. (2006). Analysis of stopout behavior at a public research university: The multi-spell discrete-time approach. Research in Higher Education, 47(8), 905–934.
Manski, C. F., & Wise, A. D. (1983). College choice in America. Cambridge: Harvard University Press.
McCormick, A. C. (1997). Transfer behavior among beginning postsecondary students: 1989–1994. Washington, DC: U.S. Department of Education.
McCormick, A. C. (2003). Swirling and double-dipping: New patterns of student attendance and their implications for higher education. New Directions for Higher Education, 121, 13–24.
Melguizo, T. (2011). A review of theories developed to describe the process of college persistence. In J. C. Smart, & M. B. Paulsen (Eds.). Higher education: Handbook of theory and research (Vol. 26, pp. 125–160). New York: Springer.
Mullane, L. (2005). Taking transfer credit beyond traditional boundaries. American Council on Education. Retrieved on April 12, 2011, from http://www.acenet.edu/AM/Template.cfm?Section=Home&TEMPLATE=/CM/ContentDisplay.cfm&CONTENTID=12215
Munro, B. H. (1981). Dropouts from higher education: Path analysis of a national sample. American Educational Research Journal, 18(2), 133–141.
Noel, L., Levitz, R., & Saluri, D. (1985). Increasing student retention. San Francisco: Jossey Bass.
O’Toole, D. M., Stratton, L. S., & Wetzel, J. N. (2003). A longitudinal analysis of the frequency of part-time enrollment and the persistence of students who enroll part time. Research in Higher Education, 44(5), 519–537.
O’Connell, A. A., & McCoach, D. B. (2008). Multilevel modeling of educational data. Charlotte, NC: Information Age Publishing, Inc.
Pascarella, E. T., & Terenzini, P. (1980). Predicting freshman persistence and voluntary dropout decisions from a theoretical model. Journal of Higher Education, 51(1), 60–75.
Pascarella, E. T., & Terenzini, P. T. (1983). Predicting voluntary freshman year persistence/withdrawal behavior in a residential university: A path analysis validation of Tinto’s model. Journal of Educational Psychology, 75(2), 215–226.
Pascarella, E., & Terenzini, P. (1991). How college affects students: Findings and insights from twenty years of research. San Francisco: Jossey-Bass.
Pascarella, E., & Terenzini, P. (2005). How college affects students—a third decade of research (Vol. 2). San Francisco: Jossey-Bass.
Paulsen, M. B. (2001). The economics of human capital and investment in higher education. In M. B. Paulsen & J. C. Smart (Eds.), The finance of higher education: Theory, research, policy, and practice (pp. 55–94). New York: Agathon Press.
Peter, K., Cataldi, E. F., & Carroll, C. D. (2005). The road less traveled? Students who enroll in multiple institutions. Washington, DC: U.S. Department of Education.
Pike, G. R., & Askew, J. W. (1990). The impact of fraternity or sorority membership on academic involvement or learning outcomes. NASPA Journal, 28, 13–19.
Pike, G. R., Hansen, M. J., & Lin, C. H. (2011). Using instrumental variables to account for selection effects in research on first-year programs. Research in Higher Education, 52(2), 194–214.
Porter S. (2002). Including transfer-out behavior in retention models: Using the NSLC enrollment search data. IR Professional File, 82.
Porter, S. (2003). Understanding retention outcomes: Using multiple data sources to distinguish between dropouts, stopouts, and transfer-outs. Journal of College Student Retention, 5(1), 53–70.
Prescott, B. T. (2010). Is Colorado’s voucher system worth vouching for? Change, July–August. Retrieved on April 15, 2011, from http://www.changemag.org/Archives/Back%20Issues/July-August%202010/Colorado-voucher-full.html
Rabin, M. (1998). Psychology and economics. Journal of Economic Literature, 36, 11–46.
Ronco, S. L. (1994). Meandering ways: Studying student stopout with survival analysis. Paper Presented at the 34th annual conference of the association for institutional research, New Orleans, LA.
Ronco, S. L. (1996). How enrollment ends: Analyzing the correlates of student graduation, transfer and dropout with a competing risks model. AIR Professional File, 61.
Singell, L., & Stater, M. (2006). Going, going, gone: The effects of aid policies on graduation at three large public institutions. Policy Sciences, 39(4), 379–403.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.
St. John, E. P., Cabrera, A. E., Nora, A., & Asker, E. H. (2000). Economic influences on persistence reconsidered. How can finance research inform the reconceptualization of persistence models? In J. M. Braxton (Ed.), Reworking the student departure puzzle (pp. 29–47). Nashville: Vanderbilt University Press.
Steele, F. (2005). Event history analysis. A national centre for research methods briefing paper. University of Bristol, Bristol, UK. Retrieved on April 29, 2011, from http://eprints.ncrm.ac.uk/88/1/MethodsReviewPaperNCRM-004.pdf
Stewart, C. H. (2010). Multilevel modelling of event history data: Comparing methods appropriate for large datasets. PhD thesis. University of Glasgow. Retrieved on September 29, from http://theses.gla.ac.uk/2007/01/2010stewartphd.pdf
Stratton, L. S., O’Toole, D. M., & Wetzel, J. M. (2008). A multinomial logit model of college stopout and dropout behavior. Economics of Education Review, 27, 319–331.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.
Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.
Toutkoushian, R. K., & Shafiq, M. N. (2010). A conceptual analysis of state support for higher education: Appropriations versus need-based financial aid. Research in Higher Education, 51(1), 40–64.
Wang, Y., & Pilarzyk, T. (2009). Understanding student swirl: The role of environmental factors and retention efforts in the later academic success of suspended students. Journal of College Student Retention, 11(2), 211–226.
Wei, C. C., & Horn, L. (2009). A profile of successful pell grant recipients: Time to bachelor’s degree and early graduate school enrollment (NCES 2009-156). Washington, DC: U.S. Government Printing Office.
Wetzel, J. N., O’Toole, D., & Peterson, S. (1999). Factors affecting student retention probabilities: A case study. Journal of Economics and Finance, 23(1), 45–55.
Willett, J. B., & Singer, J. D. (1995). It’s Déjá Vu all over again: Using multi-spell discrete-time survival analysis. Journal of Educational and Behavioral Statistics, 20(1), 41–67.
Acknowledgments
The authors are grateful to Drew Clark, David Muse and two anonymous reviewers for their numerous valuable comments and suggestions. The authors would also like to thank Sam Lowther and Matthew Campbell for help with data retrieval for the study.
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Johnson, I.Y., Muse, W.B. Student Swirl at a Single Institution: The Role of Timing and Student Characteristics. Res High Educ 53, 152–181 (2012). https://doi.org/10.1007/s11162-011-9253-0
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DOI: https://doi.org/10.1007/s11162-011-9253-0