Latent Class Analysis: A Guide to Best Practice

TLDR

Latent class analysis identifies qualitatively distinct subgroups within populations by modeling patterns of responses across survey items, and its application remains an evolving research area. The article aims to provide detailed guidance on key considerations for conducting LCA as researchers increasingly adopt the method. The authors describe LCA, review essential elements for its implementation, and illustrate the approach with a practical example.

Abstract

Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different subgroups within populations who often share certain outward characteristics. The assumption underlying LCA is that membership in unobserved groups (or classes) can be explained by patterns of scores across survey questions, assessment indicators, or scales. The application of LCA is an active area of research and continues to evolve. As more researchers begin to apply the approach, detailed information on key considerations in conducting LCA is needed. In the present article, we describe LCA, review key elements to consider when conducting LCA, and provide an example of its application.