Trajectories of Doctoral Students in the Biological Sciences: A Hidden Markov Model Approach

• In Event: Innovative Approaches to Modeling Educational Data

Abstract

The purpose of this longitudinal study is to use the Early Career Research (ECR) project longitudinal study to investigate academic and social integration, psychosocial characteristics, and identity as a source of STEM progress, retention and completion longitudinally, and how these relations have implications for STEM education and career trajectory. We analyze longitudinal data to examine doctoral trajectories leading to graduation and career, using Hidden Markov modeling. We identify several transitions that are associated with patterns of mentorship and campus climate, and show that a Hidden Markov model can predict graduation or non-graduation and career trajectory based on years and experiences in the program.

Authors

• Jerry Michael Whitmore Jr, Boston University

• Jordan Lawson, Boston College