Cluster-weighted latent class modeling

TL;DR

This paper introduces a generalized cluster-weighted latent class model that simultaneously assesses covariate distributions across classes and their direct effects on indicators, enhancing analysis of complex relationships.

Contribution

It proposes a unified modeling framework combining latent class regression and distal outcome models, allowing comprehensive testing of covariate effects and distributions.

Findings

Model effectively distinguishes covariate distribution differences across classes.

Empirical application demonstrates improved understanding of asset ownership patterns.

Model shows advantages over traditional latent class approaches.

Abstract

Usually in Latent Class Analysis (LCA), external predictors are taken to be cluster conditional probability predictors (LC models with covariates), and/or score conditional probability predictors (LC regression models). In such cases, their distribution is not of interest. Class specific distribution is of interest in the distal outcome model, when the distribution of the external variable(s) is assumed to dependent on LC membership. In this paper, we consider a more general formulation, typical in cluster-weighted models, which embeds both the latent class regression and the distal outcome models. This allows us to test simultaneously both whether the distribution of the covariate(s) differs across classes, and whether there are significant direct effects of the covariate(s) on the indicators, by including most of the information about the covariate(s) - latent variable relationship. We show the advantages of the proposed modeling approach through a set of population studies and an empirical application on assets ownership of Italian households.