The models presented here add grouping variables to “baseline” models. Code for models with and without measurement invariance across groups is available, as is code for different types of measurement invariance available with LPA.

LCA: LCA with a grouping variable and without measurement variance

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.

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