A Primer to Latent Profile and Latent Class Analysis

Abstract

This chapter gives an applied introduction to latent profile and latent class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals are probing whether and, if so, how many latent classes can be identified in the data and estimating their proportional size and response profiles. Moreover, latent class membership can serve as a predictor or outcome for external variables. Substantively, LPA/LCA adopt a person-centred approach that is useful for analysing individual differences in learning prerequisites, processes, or outcomes. This chapter provides a conceptual overview of LPA/LCA, a nuts-and-bolts discussion of the steps and decisions involved in their application, and illustrative examples using available data and the R statistical environment.