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Latent Class Analysis (LCA)

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

The latent variable “youth risk behavior” is measured by the observed variables “sex,” “drinking,” “smoking,” and “other drugs.” 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.

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Finite mixture model used to identify underlying (latent) subgroups within a population based on individuals’ responses to multiple observed variables. Factor analysis is based on continuous latent variables, whereas LCA is based on categorical latent variables." , , , , , ,



Center research on LCA is currently supported by the National Institute on Drug Abuse (NIDA) grant P50 DA10075 and National Cancer Institute (NCI) grant R01 CA168676.


Significant supported was provided by NIDA grant R03 DA023032.

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