Predicting Graduation Rates at 4-year Broad Access Institutions Using a Bayesian Modeling Approach

Gloria Crisp (), Erin Doran and Nicole A. Salis Reyes
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Gloria Crisp: Oregon State University
Erin Doran: Iowa State University
Nicole A. Salis Reyes: University of Hawai‘i at Mānoa

Research in Higher Education, 2018, vol. 59, issue 2, No 1, 133-155

Abstract: Abstract This study models graduation rates at 4-year broad access institutions (BAIs). We examine the student body, structural-demographic, and financial characteristics that best predict 6-year graduation rates across two time periods (2008–2009 and 2014–2015). A Bayesian model averaging approach is utilized to account for uncertainty in variable selection in modeling graduation rates. Evidence suggests that graduation rates can be predicted by religious affiliation, proportion of students enrolled full-time, socioeconomic status of the student body, enrollment size and institutional revenue and expenditures. Findings also demonstrate that relatively fewer variables predict institutional graduation rates for Latina/o and African American students at 4-year BAIs. We conclude with implications for policy and key recommendations for research focused on 4-year BAIs.

Keywords: Broad access institutions; Selectivity; Graduation rates; Minority students; Low-income students (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11162-017-9459-x

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