Bending the Curve: Institutional Factors Associated with Graduation Rates

Abstract

For decades, educators and policy makers have decried low graduation rates at U.S. colleges, advocating policies and making investments to improve graduation. We analyze a decade of Integrated Postsecondary Education Data System (IPEDS) data for four-year colleges to investigate how much institutions have improved their graduation rates from 2008 through 2018, once controlling for institutional and student body characteristics. We find substantial improvement to graduation rates at public colleges, modest improvement at private not-forprofits, and a decline in graduation at the for-profit sector. We then investigate whether improvements to graduate rates are associated with variation in student-body composition, selectivity, and institutional expenditures, using pooled cross-sectional, Prais-Winsten, and college fixed effect models. We find that most between-college variation in graduation rates over time reflects variation in the composition of a college's student body and in instructional expenditures. Our Bending the Curve metric utilizes the cross-sectional models to calculate predicted graduation rates for each college and determines how much they exceeded or failed to meet expectations. Unadjusted graduation measures, such as IPEDS' rates that fail to adjust for these compositional factors, are poor indicators of institutional effectiveness and can mislead stakeholders who use them as an indicator of college performance.

Analyzing Student Success Outcome Variables in Higher Education Utilizing the Chi-Square Test of Independence

“…However, there was not a specific or universal method of data analysis in this compilation of articles. Based on the literature, there are certainly effective means of analyzing categorical student success variables such as logistic regression, loglinear analysis, and transforming data into rate ratios for linear analysis (deCastro Galvo et al, 2023;Denning et al, 2022;Wekullo, 2022). However, while these analyses are powerful statistical tools, especially with regards to multiple variable predictors, they also require a fair amount of statistical acumen.…”