Kevin J. Grimm, Ph.D., is Professor of Psychology at Arizona State University. He received his B.A. in Mathematics and Psychology with a concentration in Education from Gettysburg College, and his M.A. and Ph.D. in Psychology at the University of Virginia. In graduate school, he studied structural equation modeling and longitudinal data analysis (e.g., growth curve analysis, longitudinal mixture modeling, longitudinal measurement, and dynamic models) with Drs. John J. McArdle and John R. Nesselroade. After completing his Ph.D., he worked with Dr. Robert C. Pianta as a research associate in the Center for the Advanced Study of Teaching and Learning at the University of Virginia. In 2007, he 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, he moved to the Department of Psychology at Arizona State University, and was promoted to Full Professor in 2016.
Dr. Grimm’s research interests include multivariate methods for the analysis of change, multiple group and latent class models for understanding divergent developmental processes, nonlinearity in development, machine learning techniques for psychological data, and cognitive/achievement development. He has published more than 200 articles and chapters and four books. He is an author of Growth Modeling: Structural Equation and Multilevel Modeling Approaches with Drs. Nilam Ram and Ryne Estabrook, Machine Learning for Social and Behavioral Research with Drs. Ross Jacobucci and Zhiyong Zhang, and Categorical Data Analysis with Structural Equation Models: Applications in Mplus and lavaan.
Dr. Grimm teaches undergraduate and graduate quantitative courses at Arizona State University, including Longitudinal Growth Modeling, Machine Learning in the Behavioral Sciences, Structural Equation Modeling, Advanced Categorical Data Analysis, and Intermediate Statistics. He has taught workshops sponsored by the American Psychological Association’s Advanced Training Institute, Statistical Horizons, Instats, Stats Camp, the Centers for Disease Control and Prevention, and various departments/schools across the country. These workshops have focused on growth modeling, structural equation modeling, machine learning, categorical data analysis, power analysis, and mixture modeling.
