Research in Higher Education

, Volume 47, Issue 8, pp 935–956 | Cite as

A Predictive Model of Inquiry to Enrollment

Article

ABSTRACT

The purpose of this paper is to build a predictive model of enrollment that provides data driven analysis to improve undergraduate recruitment efforts. We utilize an inquiry model, which examines the enrollment decisions of students that have made contact with our institution, a medium sized, public, Doctoral I university. A student, who makes an inquiry to our university such as by returning a request for information form, often provides far less information than is available from applicants. Despite this fact we find that characteristics of the student, as well as geographic and demographic data based on the student’s zip code are significant predictors of enrollment. Accounting for uncertainty in our model’s specification, we find that we are able to predict out of sample the enrollment decision of 89% of student inquiries. We also demonstrate how these findings can be used to improve marketing efforts.

KEYWORDS

predictive model recruitment geodemography specification uncertainity 

REFERENCES

  1. Becker G. S. (1993). Human Capital: A Theoretical and Empirical Analysis With Special Reference to Education. University of Chicago Press, ChicagoGoogle Scholar
  2. Bruggink T. H., Gambhir V. (1996). Statistical models for college admission and enrollment: A case study for a selective liberal arts college. Research in Higher Education 37(2):221–240CrossRefGoogle Scholar
  3. Ceballo R., McLoyd V. C., Toyokawa T. (2004). The influences of neighborhood quality on adolescents’ educational values and school effort. Journal of Adolescent Research 19(6): 716–739CrossRefGoogle Scholar
  4. Davis-Van Atta D. L., Carrier S. C. (1986). Using the institutional research office. In: Hossler D. (eds) Managing College Enrollments. New Directions for Higher Education No 53, Jossey-Bass, San FranciscoGoogle Scholar
  5. Datcher L. (1982). Effects of community and family background on achievement. Review of Economics and Statistics 64(1):32–41CrossRefGoogle Scholar
  6. DesJardins S. L. (2002). An analytic strategy to assist institutional recruitment and marketing efforts. Research in Higher Education 43(5): 531–553CrossRefGoogle Scholar
  7. DesJardins S. L., Dundar H., Hendel D. D. (1999). Modeling the college application decision process in a land-grant university. Economics of Education Review 18(1): 117–132CrossRefGoogle Scholar
  8. Duncan G. J. (1994). Families and neighbors as sources of disadvantage in the schooling decisions of white and black-adolescents. American Journal of Education 103(1): 20–53CrossRefGoogle Scholar
  9. Garner C. L., Raudenbush S. W. (1991). Neighborhood effects on educational attainment – A multilevel analysis. Sociology of Education 64(4): 251–262CrossRefGoogle Scholar
  10. Goenner C. F., Snaith S. M. (2004). Accounting for model uncertainty in the prediction of university graduation rates. Research in Higher Education. 45(1): 25–41CrossRefGoogle Scholar
  11. Greene W. H. (1997). Econometric Analysis. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  12. Hoeting J. A., Madigan D., Raftery A. E., Volinsky C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science 14(4):382–401CrossRefGoogle Scholar
  13. Hosmer David W., Lemeshow S. (1989). Applied Logistic Regression. Wiley, New YorkGoogle Scholar
  14. Hossler D., Gallagher K.S. (1987). Studying student college choice: A three phase model and the implications for policymakers. College and University 62(3): 207–221Google Scholar
  15. Ihlanfeldt W. (1980). Achieving Optimal Enrollments and Tuition Revenues: A Guide to Modern Methods of Market Research, Student Recruitment, and Institutional Pricing. Jossey-Bass, San FranciscoGoogle Scholar
  16. Lang R. E., Hughes J. W., Danielson K. A. (1997). Targeting the suburban urbanites: marketing central-city housing. Housing Policy Debate 8(2): 437–470Google Scholar
  17. Leppel K. (1993). Logit estimation of a gravity model of the college enrollment decision. Research in Higher Education 34(4): 387–398CrossRefGoogle Scholar
  18. Leventhal T., Brooks-Gunn J. (2000). The neighborhoods they live in: The effects of neighborhood residence on child and adolescent outcomes. Psychological Bulletin 126(2): 309–337CrossRefPubMedGoogle Scholar
  19. Manski C. F., Wise A. D. (1983). College Choice in America. Harvard University Press, Cambridge, MAGoogle Scholar
  20. Paulsen, M. B. (1990). College Choice: Understanding Student Enrollment Behavior ASHE-ERIC Higher Education Report No. 6. Washington, D.C.: The George Washington University, School of Education and Human DevelopmentGoogle Scholar
  21. Raftery A. E. (1995). Bayesian Model Selection in social research. In: Marsden P. V. (ed.), Sociological Methodology 1995. Blackwells Publishers, Cambridge, MA, pp. 111–163Google Scholar
  22. Raftery A. E. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 92(437): 179–191MathSciNetCrossRefGoogle Scholar
  23. Raftery, A. E., and Volinsky C. T. (1996). Splus function Biclogit, version 2.0. (http://www.research.att.com/∼volinsky/bma.html)
  24. Schwarz G. (1978). Estimating the dimension of a model. The Annals of Statistics 6: 461–464Google Scholar
  25. Thomas E., Dawes W., Reznik G. (2001). Using predictive modeling to target student recruitment: Theory and practice. AIR Professional File 78(Winter): 1–8Google Scholar
  26. Toutkoushian R. K. (2001). Do parental income and education attainment affect the initial choices of New Hampshire’s college-bound students?. Economics of Education Review 20(3): 245–262CrossRefGoogle Scholar
  27. Weiler W. C. (1994). Transition from consideration of a college to the decision to apply. Research in Higher Education 35(6): 631–646Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of North DakotaGrand ForksUSA
  2. 2.Director of Enrollment ServicesUniversity of North DakotaGrand ForksUSA

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