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.
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Percentage of missing values was less than one percent (.39). Between 0 and 4% of data were missing across variables. Data were assumed to be missing at random (MAR). All candidate variables were used in the imputation model which converged in three iterations (−2 Ln(L) = 36328.659).
R Code: bai = bms(bai, burn = 100,000, iter = 50,000, nmodel = 500, mcmc = "bd", g = "UIP", mprior = "random", mprior.size = NA, user.int = TRUE, start.value = NA, g.stats = TRUE, logfile = FALSE, logstep = 10,000, force.full.ols = FALSE, fixed.reg = numeric (0), data = NULL, randomizeTimer = TRUE).
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See Table 5.
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Crisp, G., Doran, E. & Salis Reyes, N.A. Predicting Graduation Rates at 4-year Broad Access Institutions Using a Bayesian Modeling Approach. Res High Educ 59, 133–155 (2018). https://doi.org/10.1007/s11162-017-9459-x
- Broad access institutions
- Graduation rates
- Minority students
- Low-income students