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ERIC Number: ED559990
Record Type: Non-Journal
Publication Date: 2015-Apr
Pages: 104
Abstractor: As Provided
A Predictive Model of Student Loan Default at a Two-Year Community College
Brown, Chanda Denea
Online Submission, Ph.D. Dissertation, Argosy University
This study explored whether a predictive model of student loan default could be developed with data from an institution's three-year cohort default rate report. The study used borrower data provided by a large two-year community college. Independent variables under investigation included total undergraduate Stafford student loan debt, total number of Stafford loan servicers, year of birth, gender, and last reported enrollment status at that institution. Two logistic regression analyses were conducted on stratified random samples to test and validate the resulting model. Descriptive statistics were calculated for the population overall, as well as generation-specific groups--millennial, generation X, and baby boomer. Results failed to develop a predictive model of student loan default. Additional research to identify other predictors of student loan repayment status would be beneficial for predicting student loan default and the development of default resolution plans. The following are appended: (1) 2011 Three-Year CDR Report Variables Defined; (2) Population Demographics and Relevant Statistics; (3) Millennial Generation Demographics and Relevant Statistics; (4) Generation X Demographics and Relevant Statistics; (5) Baby Boomer Demographics and Relevant Statistics; (6) Sample One Outputs and Data Tables; and (7) Sample Two Outputs and Data Tables.
Publication Type: Dissertations/Theses - Doctoral Dissertations; Numerical/Quantitative Data
Education Level: Two Year Colleges; Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: California; Florida; Nevada
Identifiers - Laws, Policies, & Programs: Pell Grant Program