This code adds a binary and a continuous outcome to the 4-class baseline LCA model with all binary indicators. This code uses the automated 3-step BCH approach as we explore the association between latent class membership and each outcome.

This code adds a binary and a continuous outcome to the 4-class baseline LCA model with all binary indicators. This code uses the the manual 3-step BCH approach as we explore the association between latent class membership and each outcome.

This code fits a longitudinal latent class model, using categorical indicators with 3+ levels, to identify latent classes indicated by multidimensional experiences of racism and heterosexism during the transition to adulthood among sexual minority men of color.

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.

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 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.

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.

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.

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.

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.

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.

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.

This code fits a mixed indicator latent-profile model (using both continuous and categorical indicators) to identify family subgroups that conform to risk factors associated with adolescent antisocial behavior.

This code fits a baseline, latent-profile model to identify and describe profiles of financial stress responses. It also imposes measurement invariance across the groups with means and variances equal.

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.

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.

This model estimates six day-level latent classes of substance use consequences using a marginal modeling approach to account for the nested data structure. One one day-level covariate (daily positive affect) and person-level covariate (student is under legal drinking age) are included to predict consequence latent class membership.

This model estimates six day-level latent classes of substance use consequences using a marginal modeling approach to account for the nested data structure.

This code fits a 2-level latent-class model with covariates using a “non-parametric approach.” This model simultaneously estimates day-level classes of substance-related consequences and person-level classes that group individuals based on proportion of each type of day. This model includes covariates at each level to estimate associations with both the day-level and person-level classes.

This code fits a 2-level latent-class model without covariates using a “non-parametric approach.” This model simultaneously estimates day-level classes of substance-related consequences and person-level classes that group individuals based on proportion of each type of day.

This code fits a 2-level latent-profile model using a “non-parametric approach” to identify mother-father-adolescent relationship structures and dynamics on a daily basis.

This web app uses R Shiny to make it easier to understand output from complex models, such as multilevel latent class analysis.