The models presented here are considered “baseline” in the sense that they do not add features such as grouping variables, covariates, or outcomes. These are the simplest versions of LCA, LPA, mixed indicator models, and LTA.
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.
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.
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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