The use of Latent Class Analysis (LCA) in predicting program outcomes.

This webinar will explore the application of Latent class analysis (LCA), a statistical procedure used to identify qualitatively different subgroups within populations that often share specific outward characteristics. The underlying assumption of LCA is that membership in unobserved groups (or classes) can be explained by the patterns of scores across survey questions, assessment indicators, or scales. For example, within an analysis of program outcomes, a researcher may use LCA to identify subgroups within a population that share similar patterns of risk and protective factors. This approach helps to understand and predict program success/failure, which could lead to more targeted interventions and improved outcomes.

Instructor: Dr. Kevin Wolff is an Associate Professor in the Department of Criminal Justice and a member of the doctoral faculty in the Criminal Justice Doctoral Program at John Jay College/The Graduate Center. Dr. Wolff earned his Ph.D. from the College of Criminology and Criminal Justice at Florida State University in 2014. His research interests include the spatial and temporal patterning of crime, juvenile justice, criminological theory, program evaluation, and quantitative methods.