Empirical Analysis of STEM Faculty Productivity: Using NbClust and Logistic Regression to Explore Interactions Among Faculty Teaching and Research Productivity Metrics, Demographic, and Disciplinary Characteristics

July 2025

This study investigates the nexus between research and teaching productivity among STEM faculty at a public research-intensive university, analyzing data from 553 faculty members across four STEM disciplines: Biological Sciences, Engineering, Information and Computer Sciences, and Physical Sciences. Through the combined application of cluster analysis using the NbClust package and logistic regression, the research explores the correlation between productivity metrics and faculty demographics, including position type, rank, gender, and discipline. The analysis reveals distinct productivity clusters characterized by varying levels of research and teaching productivity outcomes across demographic groups, underscoring significant disparities. The findings emphasize the imperative for institutional policies that holistically support both teaching and research to foster faculty success. By offering a nuanced understanding of faculty productivity profiles, this study informs strategies for equitable resource allocation, faculty development, and evaluation, ultimately contributing to the advancement of STEM education and the fulfillment of institutional missions.

Keywords

teaching-research productivity nexus, NbClust package, cluster analysis, logistic regression, STEM Education

Education level

Post-secondary education

Topics

Teacher and Leader Development