Advanced Models

All of the models presented here add advanced features to “baseline” static and dynamic latent class models. This includes information about latent class moderation, adding inverse propensity score weights to models, multilevel latent class models, and associative latent transition analysis, among other topics. Note that these models fall under the broad classification of loglinear modeling with latent variables.

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.

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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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LTA: Baseline LTA with 2 times, all binary indicators, and measurement invariance

This code fits a 2-time, 5-class, latent-transition model for delinquency over time using 6 binary indicators of the latent class variable. Measurement invariance across time is imposed such that analogous item-response probabilities within classes are restricted to be equal to each other across times.

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Advanced Models

LCA: Baseline LCA with all binary indicators

LPA: Baseline LPA with all continuous indicators

LTA: Baseline LTA with 2 times, all binary indicators, and measurement invariance

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