A Predictive Model for the Repayment of Student Loans in Community Colleges
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Abstract: The problem of this study was to determine the relationship between selected variables which characterize community college students and student loan defaults and to develop a model using these variables to predict student loan payback. Given the current economic crisis and the increasing reliance on the student loan programs to help students meet educational expenses, a study of the importance of selected student demographic characteristics and their relationships to the student loan default problem is of great importance to the future support of the student loan programs. The liter...
Abstract: The problem of this study was to determine the relationship between selected variables which characterize community college students and student loan defaults and to develop a model using these variables to predict student loan payback. Given the current economic crisis and the increasing reliance on the student loan programs to help students meet educational expenses, a study of the importance of selected student demographic characteristics and their relationships to the student loan default problem is of great importance to the future support of the student loan programs. The literature provided a theoretical basis for this study including appropriate variables for study as predictors of student default. These variables included size of loan total, marital status, sex, grade point average, college standing, and age. The data presented in this study were supplied by the Florida Student Financial Aid Commission, Tallahassee, Florida, and represented a statewide sample of 7 6 community college students who have participated in the Guaranteed Student Loan program. Of the six variables selected, only the size of the loan total and marital status distinguished significantly those who repaid their student loans from those who did not. In addition to these variables, sex, grade point average, college standing, and age were useful in developing a prediction model. Although the model did not provide an infallible formula for predicting those students who are most likely to repay their student loans, the model predicted group membership (defaulter of non-defaulter) for 70% of the sample cases. These findings underscore Pattillo and Wiant's conclusion that items reflecting financial rather than biographical data appear to be better predictors of loan delinquency. Therefore, it appears that the inclusion of additional discriminating variables and a more detailed study design may be necessary in order to improve the identification of students who are likely to repay their student loans. Dissertation Discovery Company and University of Florida are dedicated to making scholarly works more discoverable and accessible throughout the world. This dissertation, "A Predictive Model for the Repayment of Student Loans in Community Colleges" by James A. Schmidt, was obtained from University of Florida and is being sold with permission from the author. A digital copy of this work may also be found in the university's institutional repository, IR@UF. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation.
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