Abstract
There have recently been significant theoretical developments in multilevel statistical modeling, and improved software is readily available. This study demonstrates the application of multilevel modeling to one of the most common issues that confront institutional researchers: that of student attrition, where the response variable is typically binary rather than continuous. Comparisons are made with a traditional logistic regression approach. The data pertain to one large university. The techniques illustrated may be extended to the analysis of data sets encompassing many institutions, making meaningful interinstitutional comparisons of performance feasible even when there is hierarchical clustering present in the data.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
REFERENCES
Adelman, C. (1999). Answers in the Tool Box: Academic Intensity, Attendance Patterns, and Bachelor's Degree Attainment. Jessup, MD: U.S. Department of Education.
Agresti, A. (1996). An Introduction to Categorical Data Analysis. New York: Wiley.
Braun, H. I. (1989). Empirical Bayes methods: a tool for explanatory analysis. In R. D. Bock (ed.), Multilevel Analysis of Educational Data, pp. 19-55. San Diego: Academic Press.
Braxton, J. M., Shaw Sullivan, A. V., and Johnson, R. M., Jr. (1997). Appraising Tinto's Theory of College Student Departure. In J. C. Smart (ed.), Higher Education: Hand-book of Theory and Research, Vol. XII, pp. 107-164. New York: Agathon Press.
Breslow, N. E., and Clayton, D. G. (1993). Approximate inference in generalised linear mixed models. Journal of the American Statistical Association 88(421): 9-25.
Ethington, C. A. (1997). A hierarchical linear modeling approach to studying college effects. In J. C. Smart (ed.), Higher Education: Handbook of Theory and Research, Vol. XII, pp. 165-194. New York: Agathon Press.
Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (1996). Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
Goldstein, H. (1991). Nonlinear multilevel models, with an application to discrete response data. Biometrika 78(1): 45-51.
Goldstein, H. (1995). Multilevel Statistical Models. London, Sydney, Auckland: Arnold.
Goldstein, H., and Healy, M. J. R. (1995). The graphical presentation of a collection of means. Journal of the Royal Statistical Society, Series A, 158(1): 175-7.
Goldstein, H., and Rasbash, J. (1996). Improved approximations for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A, 159(3): 505-513.
Goldstein, H., Rasbash, J., Plewis, I. et al. (1998). A User's Guide to MLwiN. London: Multilevel Models Project, Institute of Education, University of London.
Goldstein, H., and Spiegelhalter, D. J. (1996). League tables and their limitations: statistical issues in comparisons of institutional performance. Journal of the Royal Statistical Society, Series A, 159(3): 385-443.
Higher Education Funding Council for England (HEFCE) (1999a). Performance indicators in higher education: 1996-97, 1997-98: Report. Circular 99/66. Bristol: Author.
Higher Education Funding Council for England (HEFCE) (1999b). Performance indicators in higher education: 1996-97, 1997-98: Overview. Circular 99/67. Bristol: Author.
Kreft, I., and de Leeuw, J. (1998). Introducing Multilevel Modeling. London, Thousand Oaks, New Delhi: Sage.
Menard, S. (1995). Applied Logistic Regression Analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-106. Thousand Oaks, CA: Sage.
O'Hagan, A. (1994). Kendall's Advanced Theory of Statistics, Vol. 2B: Bayesian Inference. London, Sydney, Auckland: Arnold.
Ozga, J. T., and Sukhnandan, L. (1997). Undergraduate non-completion. In Undergraduate Non-Completion in Higher Education in England, Report No. 2. Bristol: Higher Education Funding Council for England.
Pascarella, E. T., and Terenzini, P. T. (1991). How College Affects Students: Findings and Investigations from Twenty Years of Research. San Francisco: Jossey-Bass.
Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociology Review 15: 351-357.
Rodríguez, G., and Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A, 158(1): 73-89.
Searle, S. R., Casella, G., and McCulloch, C. E. (1992). Variance Components.New York: Wiley.
Seymour, E., and Hewitt, N. M. (1997). Talking About Leaving: Why Undergraduates Leave the Sciences. Boulder, CO and Oxford: Westview Press.
Snijders, T. A. B., and Bosker, R. J. (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London, Thousand Oaks, New Delhi: Sage.
Thomas, M., Adams, S., and Birchenough, A. (1996). Student withdrawal from higher education. Educational Management and Administration 24(2): 207-221.
Tinto, V. (1975). Dropout from higher education: a theoretical synthesis of recent research. Review of Educational Research 45: 89-125.
Tinto, V. (1987). Leaving College: Rethinking the Causes and Cures of Student Attrition. Chicago: University of Chicago Press.
Tinto, V. (1993). Leaving College: Rethinking the Causes and Cures of Student Attrition. (2nd ed.) Chicago: University of Chicago Press.
Yorke, M. (1999). Leaving Early: Undergraduate Non-Completion in Higher Education. London and Philadelphia: Falmer Press.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Patrick, W.J. Estimating First-Year Student Attrition Rates: An Application of Multilevel Modeling Using Categorical Variables. Research in Higher Education 42, 151–170 (2001). https://doi.org/10.1023/A:1026521519201
Issue Date:
DOI: https://doi.org/10.1023/A:1026521519201