Bayesian cluster analysis to determine institutional peers

Abstract

Academic peers are a type of institutional peer group often identified for the purpose of assessing an institution’s performance in student success. This project introduces the consideration of a different type of peer group, student population peers, when assessing the performance of the University of Alaska Fairbanks in comparison to its currently identified academic peers. A Bayesian influenced clustering analysis is used to determine student population peer groups; these peer groups are constructed using student metrics data retrieved from the IPEDS data center, thus providing an emphasis on the student body an institution serves. The R package bclust is used to compute our Bayesian cluster analysis. We find that none of the university’s listed academic peers are closely related to the University of Alaska Fairbanks when using student body focused clustering analysis. Using the Bayesian cluster analysis for determining student population peers of the University of Alaska Fairbanks, we allow a more comprehensive discussion of the university’s performance in student success when comparing outcome measures between the university and its current academic peers.