All of the models presented here are considered “dynamic” in the sense that they model change over time in a categorical latent variable; that is, an individual is allowed to transition between latent classes when longitudinal data are collected. These models include latent transition analysis (LTA). A “traditional” perspective on LTA is used here, so that these models include LTAs with the same classes and indicators at all times. More general models are available in our collection of “advanced” models. Note that different names for these models appear throughout the literature, a common one being latent Markov models. In addition, more recent literature refers to the identified classes as “classes” whereas older literature often refers to the identified classes as “statuses” to convey the dynamic nature of membership.

LTA: Baseline Latent Transition Analysis with Categorical Indicators

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.

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