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: LCA with a grouping variable and without measurement variance

Description 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. Measurement invariance across groups is not imposed resulting in an unrestricted latent class model with multiple groups. Software Downloads Latent Gold Mplus SAS Stata Exercise Exercise 4 This exercise asks you to add a grouping variable for year to a 4-class model for marijuana use and attitudes that uses 7 binary indicators of the latent class variable. It asks you to fit a model...

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