Research in Higher Education

, Volume 53, Issue 2, pp 152–181 | Cite as

Student Swirl at a Single Institution: The Role of Timing and Student Characteristics

  • Iryna Y. Johnson
  • William B. Muse


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.


Student swirl Retention Stopout Transfer Discrete-time hazard model Multilevel model 



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.


  1. Adelman, C. (1999). Answers in the toolbox: Academic intensity, attendance patterns, and Bachelor’s degree attainment. Washington, DC: U.S. Department of Education.Google Scholar
  2. 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.Google Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. Bahr, P. R. (2011). Student flow between community colleges: Investigating lateral transfer. Research in Higher Education (in print).Google Scholar
  6. Barber, J. S., Murphy, S. A., Axinn, W. G., & Maples, J. (2000). Discrete-time multilevel hazard analysis. Sociological Methodology, 30(1), 201–235.CrossRefGoogle Scholar
  7. Becker, G. S. (1964). Human capital. New York: Columbia University Press.Google Scholar
  8. 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.Google Scholar
  9. Borden, V. M. H. (2004). Accommodating student swirl: When traditional students are no longer the tradition. Change, 36(2), 10–17.CrossRefGoogle Scholar
  10. Braxton, J. M. (2000). Reworking the student departure puzzle. Nashville: Vanderbilt University Press.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. Cook, B., & Pullaro, N. (2010). College graduation rates: Behind the numbers. Washington, DC: American Council on Education.Google Scholar
  14. 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.Google Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.Google Scholar
  20. Ehrenberg, R. G. (2005). The perfect storm and the privatization of public higher education. Working papers 59. Retrieved on April 15, 2011, from
  21. 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.Google Scholar
  22. Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied choice analysis: A primer. New York: Cambridge University Press.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 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.CrossRefGoogle Scholar
  25. Horn, L. (1998). Stopouts or stayouts? Undergraduates who leave college in their first year (NCES 1999–087). Washington, DC: U.S. Government Printing Office.Google Scholar
  26. 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.Google Scholar
  27. 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.CrossRefGoogle Scholar
  28. Manski, C. F., & Wise, A. D. (1983). College choice in America. Cambridge: Harvard University Press.Google Scholar
  29. McCormick, A. C. (1997). Transfer behavior among beginning postsecondary students: 1989–1994. Washington, DC: U.S. Department of Education.Google Scholar
  30. 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.CrossRefGoogle Scholar
  31. 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.Google Scholar
  32. Mullane, L. (2005). Taking transfer credit beyond traditional boundaries. American Council on Education. Retrieved on April 12, 2011, from
  33. Munro, B. H. (1981). Dropouts from higher education: Path analysis of a national sample. American Educational Research Journal, 18(2), 133–141.Google Scholar
  34. Noel, L., Levitz, R., & Saluri, D. (1985). Increasing student retention. San Francisco: Jossey Bass.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. O’Connell, A. A., & McCoach, D. B. (2008). Multilevel modeling of educational data. Charlotte, NC: Information Age Publishing, Inc.Google Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. Pascarella, E., & Terenzini, P. (1991). How college affects students: Findings and insights from twenty years of research. San Francisco: Jossey-Bass.Google Scholar
  40. Pascarella, E., & Terenzini, P. (2005). How college affects students—a third decade of research (Vol. 2). San Francisco: Jossey-Bass.Google Scholar
  41. 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.Google Scholar
  42. 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.Google Scholar
  43. 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.Google Scholar
  44. 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.CrossRefGoogle Scholar
  45. Porter S. (2002). Including transfer-out behavior in retention models: Using the NSLC enrollment search data. IR Professional File, 82.Google Scholar
  46. 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.CrossRefGoogle Scholar
  47. Prescott, B. T. (2010). Is Colorado’s voucher system worth vouching for? Change, July–August. Retrieved on April 15, 2011, from
  48. Rabin, M. (1998). Psychology and economics. Journal of Economic Literature, 36, 11–46.Google Scholar
  49. 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.Google Scholar
  50. 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.Google Scholar
  51. 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.CrossRefGoogle Scholar
  52. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.Google Scholar
  53. 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.Google Scholar
  54. 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
  55. 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
  56. 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.CrossRefGoogle Scholar
  57. Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.Google Scholar
  58. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.Google Scholar
  59. 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.CrossRefGoogle Scholar
  60. 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.CrossRefGoogle Scholar
  61. 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.Google Scholar
  62. 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.CrossRefGoogle Scholar
  63. 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.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Office of Institutional Research and AssessmentAuburn UniversityAuburnUSA
  2. 2.Department of Mathematics and PhilosophyColumbus State UniversityColumbusUSA

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