Current definitions of retention and graduation rates distort the picture of student success by limiting it to completion of a degree at the institution of entry. By incorporating data from the National Student Clearinghouse (NSC), a clearer picture emerges. The NSC data captures retention and graduation at both entry and transfer institutions. To accommodate this polychotomous definition of success, more sophisticated methods of modeling limited dependent variables are needed. Though multinomial logit is often considered the most accessible method, the strict assumptions it imposes may be inappropriate. We therefore compare multinomial regression techniques to assess their utility in modeling multi-institutional student success.