Predicting Student Retention and Academic Success at New Mexico Tech

Department made the necessary preparations for me to access the database. In the beginning stages of this study, Allan Gutjahr helped to form the underlying structure of this thesis. I was very privileged to have been able to work with him. I owe many thanks to my advisor, Brian Borchers, and to my committee members, Bill Stone and Emily Nye for their guidance and support on this thesis. I also need to thank the Mathematics Department for their constant encouragement. iii Abstract Focusing on new, incoming freshmen, this study examines several variables to see which can provide information about retention and academic outcome after three semesters. Two parametric classification models and one non-parametric classification model were used to predict various outcomes based upon persistence and academic standing. These classification models were: Logistic Regression, Discriminant Analysis, and Classification and Regression Trees (CART). In addition, the outcome of the freshmen who participated in the Group Opportunities for Activities and Learning (GOAL) program were examined to determine if these students were retained and performed well academically at higher rates than predicted given their admission criteria.