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 imposed such that analogous item-response probabilities within classes are restricted to be equal to each other across groups. This ensures the interpretations of the 4 latent classes are identical for each of the 3 years and that the latent class prevalences can be directly compared across years.

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 without measurement invariance across groups, as well as a model with measurement invariance across groups. Then, it asks you to use a likelihood ratio test to determine whether measurement invariance holds across groups and interpret all parameters in the appropriate model. It is recommended that you complete the exercise for the baseline LCA with all binary indicators model before completing this exercise.

Model Category

Static

Model Type

Grouping Variable

Indicator Type

Categorical

Available Software

Latent Gold, Mplus, SAS, Stata

Measurement Invariance

Item-Response Probabilities

Approach to Outcomes

Not Applicable

Contributors

Bethany Bray