Residual Associations in Latent Class and Latent Transition Analysis

This article explores a method for modeling associations among binary and ordered categorical variables. The method has the advantage that maximum-likelihood estimation can be used in multivariate models without numerical integration because the observed data log-likelihood has an explicit form. The association model is especially useful with mixture models to handle violations of the local independence assumption. Applications to latent class and latent transition analysis are presented.