These models include example LatentGOLD code. LatentGOLD and its corresponding documentation is available at www.statisticalinnovations.com. Note that we recommend the “advance syntax” version.
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.
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...
LCA: LCA with a grouping variable and measurement invariance
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.
LCA: LCA with a grouping variable and without measurement variance
Description 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 not imposed resulting in an unrestricted latent class model with multiple groups. Software Downloads Latent Gold Mplus SAS Stata Exercise Exercise 4 This exercise asks you to add 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. It asks you to fit a model...
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.
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...
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...
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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