The models presented here add covariates/predictors to “baseline” models. Code is available for model-based approaches (i.e., one-step approaches) and for classify-analyze approaches (i.e., 3-step approaches).
LCA: Latent Class Moderation
Description This code demonstrates how to use a latent class moderator to examine heterogeneity in intervention effects among adolescents receiving treatment for cannabis use. First, the code identifies latent classes of contextual and individual risk at baseline using LCA. Then, it uses an adjusted 3-step approach with BCH weights to regress the outcomes on level of care, latent class membership, the interaction between them, and covariates. This code corresponds to the research paper titled “Estimating the Effects of a Complex, Multidimensional Moderator: An Example of Latent Class Moderation to Examine Differential Intervention Effects of Substance Use Services” published in Prevention...
LCA: LCA with a covariate (1-step approach)
Description This code fits a 4-class, latent-class model for marijuana use and attitudes using a model-based approach (1-step approach). It includes a covariate for grades in the model. Software Downloads Latent Gold Mplus SAS Stata Exercise Exercise 5 This exercise asks you to use a model-based approach (1-step approach) to add a covariate for grades to a 4-class model for marijuana use and attitudes that uses 7 binary indicators of the latent class variable. You have to carefully consider what latent class to use as the reference class in the multinomial logistic regression. You may wish to standardize the grades...
LCA: LCA with a covariate and a grouping variable (1-step approach)
Description This code fits a 4-class, latent-class model for marijuana use and attitudes using a model-based approach (1-step approach). It includes a covariate for grades and a grouping variable for year in the model. Software Downloads Latent Gold SAS Exercise Exercise 5 This exercise asks you to use a model-based approach (1-step approach) to add a covariate for grades and 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. You have to carefully consider what latent class to use as the reference class in the...
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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