Date of Conferral
2017
Degree
Ph.D.
School
Management
Advisor
Steven Tippins
Abstract
The significant problem addressed in this research was the increasing default rate among federal student loan borrowers who attended non-degree-granting proprietary colleges in Florida (i.e., career and technical colleges). The purpose of this study was to identify, better understand, and predict which borrower characteristics increased the likelihood of student loan default at proprietary non-degree-granting colleges. The research was based on the structural-functional and planned behavior theories and utilized a quantitative, non-experimental, cross-sectional design to explore the relationship between academic success, age, college graduation status, ethnicity, gender, high school class ranking, and federal student loan default. Self-reported data were obtained from students who attended private, for-profit, less than 2-year colleges in Florida. To determine which student borrower characteristics predicted an increase in the likelihood that borrowers would default on their student loan payments, one hypothesis was proposed to evaluate six borrower characteristics. Logistic regression analysis was used to explore the statistical relationships and found that academic success, age, and gender were statistically significant in predicting student loan default among students who attended private, for-profit, less than 2-year colleges in Florida. This study may facilitate positive social change by aiding educational institutions in identifying at-risk borrower characteristics and by providing various default prevention strategies that could be incorporated into specific counseling messages to reduce future student loan defaults and lower institutional cohort default ratings.
Recommended Citation
Kelley, Samuel Hanson, "Factors Affecting Student Loan Default in Proprietary Non-Degree Granting Colleges" (2017). Walden Dissertations and Doctoral Studies. 3898.
https://scholarworks.waldenu.edu/dissertations/3898
Included in
Business Administration, Management, and Operations Commons, Finance and Financial Management Commons, Management Sciences and Quantitative Methods Commons