Date of Award

2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Education

Abstract

Post-Great Recession budgets cuts and funding freezes have decreased the level of institutional resources available to recruit and retain undergraduate students. To optimize remaining expenditures in this challenging climate, new analytical approaches must be considered to evaluate and interpret pre-enrollment student data. To date, much of the higher education literature has focused on predicting enrollment using traditional fixed or mixed effects binary logistic models. While robust, these modeling approaches are constrained by standard statistical assumptions, do not account for the timing of students' enrollment decisions, and cannot efficiently incorporate censored data points or competitor information. This study applies a multi-level, competing risks model to the analysis of undergraduate application data to assess time to enrollment as a function of univariable and multivariable sociodemographic, institutional, financial, and academic factors. There are both methodological and practical strengths to the analytic approach. Conceptually, the mixed effects model applied to this sample appropriately accounts for student clustering, thereby incorporating similarities in applicants' academic preparation and backgrounds. Further, the competing risks design allows data on select competitors to enter the model, offering the opportunity to evaluate multiple institutions side-by-side.

In practice, the study uncovered differential effects across the competitive set for every sociodemographic, institutional, financial, and academic factor under review, with the exception of first choice status. The institutional and policy implications associated with these divergent results range from a reduction in undergraduate recruitment expenditures to continued investment in student support services leading to stronger retention, higher graduation rates, and lower cohort default rates (debt delinquency). Reducing recruitment overhead will not only free up important capital to reinvest in vital student support services, including first year programming, but it will also enable administrators to maintain a focus on important post-enrollment metrics. This modeling approach provides unique insights into not only students' final decisions, but also their timelines for making those decisions. Consideration of model results within the undergraduate recruitment process will help to alleviate some of the initial budget constraints by identifying how and when certain known factors increase the probability of student enrollment, while not sacrificing on other important postsecondary measures, such as retention and graduation.

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