Analyzing STEM Faculty Academic Productivity: Exploring with NbClust Package and Logistic Regression

Music City Center 

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: Information and Computer Sciences, Biological Sciences, Engineering, and Physical Sciences. By applying cluster analysis with the NbClust package and logistic regression, this research explores correlations between academic productivity metrics and faculty demographics, including position type, rank, gender, and discipline etc. The analysis identifies distinct productivity clusters characterized by varying levels of research and teaching outcomes across demographic groups, highlighting significant disparities. These findings highlight the need for institutional policies that comprehensively support both teaching and research, thereby fostering STEM faculty success. This study provides a nuanced understanding of STEM faculty productivity profiles, informing strategies for equitable institutional resource allocation, faculty development, and evaluation, ultimately contributing to the advancement of STEM education and fulfilling institutional missions.

NbClust Package

Cluster Analysis

Logistic Regression

STEM Education

Academic Productivity (Teaching and Research)

STEM Faculty Characteristics 

Caucus for Women in Statistics