Latent class analysis (LCA) identifies unobservable subgroups within a population. We work to expand LCA models to allow scientists to better understand the impact of exposure to patterns of multiple risks, as well as the antecedents and consequences of complex behaviors, so that interventions can be tailored to target the subgroups that will benefit most.
Latent transition analysis (LTA) is a related method that allows scientists to estimate movement between subgroups over time.
Introductory Example: Profiles of Teen Sex and Drug Use
In this example, LCA identifies five subgroups of teenagers based on their substance use and sexual behaviors. This analysis could be used to understand complex behavior patterns and variables that predict high-risk behavior, and to identify the subgroups that are most at-risk. With this information, scientists can develop interventions that target the neediest individuals.