Paul Attewell, Christopher Maggio, Frederick Tucker, Jay Brooks, Matt S. Giani, Xiaodan Hu, Tod Massa, Feng Raoking, David Walling, & Nathan Wilson (Journal of Postsecondary Student Success, 2022)

Abstract

This paper reports the results of a four-state collaboration––Texas, New York, Virginia, and Illinois––that uses Student Unit Record Database Systems that track students from high school into college. Using similar statistical models across four state university systems, we identify individual students at greatest risk of non-completion quite accurately at early stages, allowing college staff to prioritize interventions and supports aimed at improving completion for those at greatest risk. Our logistic regression models rely on variables available to university administrators at student entry, including high school GPA, standardized test scores, parental income, remediation requirements, declared major, and college credits attempted in the first semester. Our models do not use gender, race, or ethnicity in determining probability of non-completion, making them useful for public university administrators.