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.
Algorithm for assigning individuals to groups (clusters) so that the most similar objects are grouped. Cluster analysis can be done using many different algorithms, and typically is based on responses to multiple continuous variables.
An observed variable used in a measurement model such as LCA to measure a latent variable (often referred to as an item). For example, if the latent variable is “teen delinquency,” the indicators might include shoplifting, lying to parents, property damage, and carrying a gun.
An unobserved variable posited to explain a set of observed responses to indicators; in LCA the latent variables are categorical, whereas in factor analysis the latent variables are continuous. For example you might use observed values of shoplifting, lying to parents, property damage, and carrying a gun to estimate delinquency latent classes.
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.
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.