Kevin J. Grimm

I am a Professor in the Department of Psychology at Arizona State University.  I received my B.A. in Mathematics and Psychology with a concentration in Education from Gettysburg College (2000), and my M.A. and Ph.D. in Psychology at the University of Virginia (2001-2006). In graduate school, I studied structural equation modeling and longitudinal data analysis (e.g., growth curve analysis, longitudinal mixture modeling, longitudinal measurement, and dynamic models) with Jack McArdle and John Nesselroade.  After completing my Ph.D., I worked with Bob Pianta as a research associate in the Center for the Advanced Study of Teaching and Learning at the University of Virginia. In 2007, I joined the faculty in the Department of Psychology at the University of California, Davis as an Assistant Professor, and was promoted to Associate Professor in 2011. In 2014, I moved to the Department of Psychology at Arizona State University, and was promoted to Full Professor in 2016.

I am an author of Categorical Data Analysis with Structural Equation Models: Applications in Mplus and lavaan (Guilford, 2026), Growth Modeling: Structural Equation and Multilevel Modeling Approaches (Guilford, 2017), Machine Learning for Social and Behavioral Research (Guilford, 2023), and Longitudinal Multivariate Psychology (Routledge, 2019). I teach graduate quantitative courses at Arizona State University, including Longitudinal Growth Modeling, Machine Learning in Psychology, Structural Equation Modeling, and Advanced Categorical Data Analysis, and Intermediate Statistics. I have also taught workshops sponsored by the American Psychological Association, Statistical Horizons, Stats Camp, Instats, and various universities and centers across the country (University of Virginia, University of Illinois, University of Miami, Centers for Disease Control and Prevention, University of North Carolina, Greensboro). These workshops have focused on growth modeling, structural equation modeling, machine learning, structural equation models with categorical outcomes, power analysis, and mixture modeling.