Longitudinal and Behavioral Data Science Lab

The Longitudinal & Behavioral Data Science Lab seeks to develop and evaluate methods and statistical models used to capture key characteristics of individual change processes, the determinants of longitudinal change process, and the determinants of between-person differences in the individual change process. We have focused on the development and application of machine learning methods to latent variable and longitudinal models common in the social and behavioral sciences. We have published papers on growth models, linear and nonlinear mixed-effects models, growth mixture models, latent class models, linear dynamic models, machine learning techniques, and exploratory approaches to studying individual change. The majority of our applications are focused on health, cognition, and achievement outcomes.