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.