Saturday, October 28, 2017
Heeyun Kim, Meghan Oster, Natsumi Ueda & Stephen DesJardins (Michigan), Predicting Law School Enrollment: The Strategic Use of Financial Aid to Craft a Class:
In this study, we explore what factors predict student decisions to enroll at law schools and how the probability of enrollment varies across students with various profiles and conditions. To find the predictors of enrollment and differences in the probability of enrollment across groups, we employ a logistic regression model using the institutional data obtained from one of the top-ranked law schools in the nation. After estimating the logistic regression model, the probabilities of enrollment are calculated for students with specific profiles and conditions based on the coefficients generated by the logistic regression analysis. The findings reveal many factors that are associated with the probability of enrollment at this law school. Particularly, students with higher academic qualifications, underrepresented minority status, the most selective undergraduate school, STEM background, and previous applicant status have a lower probability of enrollment compared to their respective counterparts.
Simulation analysis findings show that the increase in financial aid does not increase the probability of enrollment for URM students and that out-of-state and international students are more sensitive to financial aid increases than in-state students. Admissions and enrollment management offices at individual institutions could apply this exercise with their own data to understand who is more or less likely to enroll and how their students with various profiles respond differently to various financial aid offers and recruitment efforts. It is our hope that this article is used as an example to other law schools to leverage their institutional data to create enrollment models that will help make more effective admission decision making.