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 covariate and a grouping variable (1-step approach)

Description This code fits a 4-class, latent-class model for marijuana use and attitudes using a model-based approach (1-step approach). It includes a covariate for grades and a grouping variable for year in the model. Software Downloads Latent Gold SAS Exercise Exercise 5 This exercise asks you to use a model-based approach (1-step approach) to add a covariate for grades and 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. You have to carefully consider what latent class to use as the reference class in the...

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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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