TY  - JOUR
AB  - A growing body of research has documented extensive credit loss among transfer students. However, the field lacks theoretically driven and empirically supported frameworks that can guide credit loss research and reforms. We develop and then test a comprehensive framework designed to address this gap using novel administrative credit loss data from Texas. Our results demonstrate how the likelihood of credit loss varies across course characteristics, majors, pretransfer academics, student characteristics, and sending and receiving institutions. Additionally, we are able to disentangle general credit loss from major credit loss and examine how they vary across institutions, majors, and the combination of both. The extensive variation in credit loss among universities in particular underscores the need for future research and reform.
AU  - Giani, Matt S.
AU  - Schudde, Lauren
AU  - Sultana, Tasneem
PY  - 2024
ST  - Toward a Comprehensive Model Predicting Credit Loss in Vertical Transfer
TI  - Toward a Comprehensive Model Predicting Credit Loss in Vertical Transfer
UR  - http://www.edworkingpapers.com/ai24-1050
ER  -