Latent Class Modeling

Latent class modeling refers to a group of techniques for identifying unobservable, or latent, subgroups within a population. Researchers have developed and expanded methods like latent class analysis (LCA) and latent transition analysis (LTA) over the last two decades. Our current research focuses on expanding methods to include latent class variables in larger models of complex developmental processes. 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.

Latent Class Analysis

Latent class analysis (LCA) identifies unobservable subgroups within a population. LCA typically uses cross-sectional data to identify subgroups at a single time point; in this sense we think of class membership as being static.

Latent Transition Analysis

Latent transition analysis (LTA) is a longitudinal extension of LCA that allows scientists to estimate movement between subgroups over time; in this sense we think of class membership as being dynamic and class membership represents a developmental stage.

Multilevel Latent Class Analysis

Multilevel latent class analysis (MLCA) was developed more than a decade ago to address nested data, such as individuals nested within a higher-order structure (e.g., students nested in classrooms). By applying MLCA to the analysis of intensive longitudinal data (ILD), we can better understand the heterogeneity of behavior patterns in daily life and identify within-person vs. between-person risk factors.

Advanced Models

Latent class analysis (LCA) and latent transition analysis (LTA) can be extended in a variety of ways. Advanced features, such as covariates, outcomes, moderators, and inverse propensity score weights, can be added to “baseline” static and dynamic latent class models.

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