Logistic regression in predictive modeling of admitted student enrollment



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The application of Predictive Modeling within enrollment management provides a tremendous tool for building and shaping future enrollments for institutions of higher education. Though the practice of predictive modeling is a well known application in private business practices, the use of predictive modeling in enrollment management has only recently been employed since 1990s. The independent higher education consulting firm Noel-Levitz popularized the introduction of predictive modeling as a method of providing enrollment management professionals in higher education the opportunity to forecast the possible enrolling classes of students in their institutions. This study followed a recommended strategy for application of predictive modeling for enrollment management. Stephen DesJardins, Ph.D., from the University of Michigan published a methodology for applying predictive modeling to the process of recruiting and admitting students in an attempt to provide institutions who were actively involved in predictive modeling programs, or those who could not afford independent consulting organizations who provided predictive modeling services. The recommended method of predictive modeling as prescribed by DesJardins will be adapted to an entering class of freshmen at a large, public 4-year institution of higher education in the Southwest. The class of 2009 will be analyzed in order to build a model of predictive modeling which will then subsequently be applied to the class of 2010. The effectiveness of both the model and the application were analyzed for effectiveness, since both of these classes have already matriculated.



Enrollment management, Predictive modeling, Logistic regression, Admitted student enrollment