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.

LPA: Baseline LPA with all continuous indicators and a covariate

Description This code fits a baseline, latent-profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable and biological sex as a covariate. Software Downloads Latent Gold Mplus Exercise Exercise 6 This exercise asks you to select and interpret a latent profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable as well as add a covariate for biological sex. Then, it asks you to interpret all parameters in the model. Note that, by default in most software packages, the variances of the indicators are restricted to...

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LPA: Baseline LPA with all continuous indicators and a grouping variable with measurement invariance

Description This code fits a baseline, latent-profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable and biological sex as the grouping variable. It also imposes measurement invariance across the groups. Software Downloads Latent Gold Mplus Exercise Exercise 6 This exercise asks you to select and interpret a latent profile model for the “Big 5” personality traits using 5 continuous indicators of the latent class variable as well as add a grouping variable for biological sex. Then, it asks you to interpret all parameters in the model. Please be sure to impose measurement...

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