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

Author

Listed:
  • Gloria Crisp

    (Oregon State University)

  • Erin Doran

    (Iowa State University)

  • Nicole A. Salis Reyes

    (University of Hawai‘i at Mānoa)

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.

Suggested Citation

  • Gloria Crisp & Erin Doran & Nicole A. Salis Reyes, 2018. "Predicting Graduation Rates at 4-year Broad Access Institutions Using a Bayesian Modeling Approach," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(2), pages 133-155, March.
    • Handle: RePEc:spr:reihed:v:59:y:2018:i:2:d:10.1007_s11162-017-9459-x
    DOI: 10.1007/s11162-017-9459-x
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    References listed on IDEAS

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    1. Caroline M. Hoxby, 2009. "The Changing Selectivity of American Colleges," Journal of Economic Perspectives, American Economic Association, vol. 23(4), pages 95-118, Fall.
    2. Webber, Douglas A. & Ehrenberg, Ronald G., 2010. "Do expenditures other than instructional expenditures affect graduation and persistence rates in American higher education?," Economics of Education Review, Elsevier, vol. 29(6), pages 947-958, December.
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    4. Marvin A. Titus, 2006. "Understanding the Influence of the Financial Context of Institutions on Student Persistence at Four-Year Colleges and Universities," The Journal of Higher Education, Taylor & Francis Journals, vol. 77(2), pages 353-375, March.
    5. Giuseppe De Luca & Jan R. Magnus, 2011. "Bayesian model averaging and weighted-average least squares: Equivariance, stability, and numerical issues," Stata Journal, StataCorp LP, vol. 11(4), pages 518-544, December.
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    Cited by:

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    4. Sinjini Mitra, 2023. "How Are Students Learning in a Business Statistics Course? Evidence from Both Direct and Indirect Assessment," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 95-103, January.

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