All of the models presented here are considered “static” in the sense that they model a single categorical latent variable where an individual’s class membership does not change. These models include latent class analysis (LCA), latent profile analysis (LPA), and mixed indicator models. Although different names for these models appear throughout the literature, here we use the convention that models that include only categorical indicators are LCAs, include only continuous indicators are LPAs, and include both categorical and continuous indicators are mixed indicator latent class models.
LCA: Baseline LCA with all binary indicators
This code fits a 4-class, baseline, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent class variable. This code also plots the item-response probabilities using a line graph.
LCA: LCA with a grouping variable and measurement invariance
This code fits a 4-class, latent-class model for marijuana use and attitudes using 7 binary indicators of the latent class variable. It includes a grouping variable for year, and observations came from 3 different years.
LPA: Baseline LPA with all continuous indicators
This code fits a 5-class, baseline, latent-profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable.
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