Join us for a talk by Dr. Kevin Grimm (Director of Research and Operations and Professor, Arizona State University)!
Title: Challenges and Novel Approaches to Examine Individual Change
Abstract: Growth models are a primary analytic tool to examine within-person change processes, between-person differences in change, and determinants of change; however, their application is often limited by challenges related to data collection and analysis. For example, nonlinear growth models that may better capture the developmental process under study may not be feasible unless the curvature of the trajectory is captured by the data and there are enough data points to estimate the parameters of interest. Moreover, statistical programs are limited by the types of variables that can be analyzed and the complexity of the models that can be estimated. These challenges lead researchers to simplify their approach. In this presentation, I discuss three projects where novel statistical modeling aided our understanding of the developmental process. The first project examines the development of Spanish and English expressive language growth of children in bilingual households. The second project examines changes of daytime sleepiness during adolescence and early adulthood as a function of sleep restrictions. Finally, the third project examines reading trajectories and how age-based versus time-in-study-based models of change affect our understanding of the development process.
About Kevin Grimm: Dr. Kevin Grimm is the Director of Research and Operations and a Professor of Psychology at Arizona State University. His research focuses on advanced multivariate methods for studying change, latent class, multiple-group models, nonlinear developmental processes, and machine learning applications in psychology. He is widely recognized for his contributions to understanding cognitive and achievement development with the development and application of advanced quantitative methods.
Dr. Grimm’s scholarship has earned multiple honors, including the Tanaka Award from the Society for Multivariate Experimental Psychology (SMEP) and SMEP’s Early Career Research Award. He is the author of several influential books – Growth Modeling, Machine Learning for Social and Behavioral Research (recipient of the Barbara Byrne Book Award), Longitudinal Multivariate Psychology, and Categorical Data Analysis with Structural Equation Models. He has published more than 190 peer-reviewed articles in leading journals such as Psychological Methods, Structural Equation Modeling, Multivariate Behavioral Research, Clinical Psychological Science, Journal of Experimental Psychology: General, Journal of Personality and Social Psychology, Developmental Science, Child development, and Developmental Psychology. His research has been supported by over $71 million in funding from the National Institutes of Health, Department of Defense, Institute of Education Sciences, and the Centers for Disease Control and Prevention.