Kyle Cox is an assistant professor of educational research, measurement, and evaluation (ERME) at the University of North Carolina at Charlotte and serves as the Program Director for the Graduate Certificate in Quantitative Analyses. Kyle teaches graduate level statistics and research methods courses while his research focuses on improving the feasibility of multilevel studies through design improvements and analytic advancements. This work is applicable across the social sciences as the methods accommodate natural hierarchical structures and complex theories but Kyle is most interested in their application in educational settings. Specifically, Kyle has investigated statistical power for multilevel experiments and estimation of structural equation models when sample sizes are limited with a particular focus on effect heterogeneity. Kyle also collaborates with colleagues at Charlotte and other institutions on educational research with a substantive focus. His recent and ongoing applied research includes investigations of instructional coaching effects on teacher retention, utilization of generative AI to support instructional coaching, principal leadership, and the evaluation of a new training for using AI in educational research. Prior to joining UNC Charlotte in 2019, Kyle earned his doctorate in quantitative research methodology at the University of Cincinnati after nearly a decade of teaching 6th grade math.
EDUCATION
M.A.- University of Cincinnati, 2012, Educational Studies: Quantitative Research Methods
B.S.- Miami University, 2005, Education
TEACHING
Structural Equation Modeling
Advanced Statistics
Introduction to Research Methods
Data Management and Visualization
RESEARCH
Research Interests/Areas of Expertise
Multilevel Experimental Designs
Effect Heterogeneity
AWARDS
University of Cincinnati Research Council Graduate Student Stipend and Research Cost Award for Faculty-Student Collaboration
2016 CADRE STEM Fellowship
Finalist for Top Proposal to the 2016 American Educational Research Association Annual Meeting: Division D In-Progress Research Gala
2015 Project Excellence Award for Teaching
Selected Publications
Cox, K., Kelcey, B., Luce, H. (2024). Power to detect moderated effects in three-level partially nested designs. Journal of Experimental Education. 92, 130-153.
Cox, K., Kelcey, B., Bai, F. (2023). Croon’s bias-corrected estimation for multilevel structural equation models with latent interactions. Structural Equation Modeling: A Multidisciplinary Journal. 30, 467-480.
