Abstract
In this article, we consider the broad applicability of latent class analysis (LCA) and related approaches to advance research on child development. First, we describe the role of person-centered methods such as LCA in developmental research, and review prior applications of LCA to the study of development and related areas of research. Then we present practical considerations when applying LCA in developmental research, including model selection and statistical power. Finally, we introduce several recent methodological innovations in LCA, including causal inference in LCA, predicting a distal outcome from latent class membership, and latent class moderation (in which LCA quantifies multidimensional moderators of effects in observational and experimental studies), and we discuss their potential to advance developmental science. We conclude with suggestions for ongoing developmental research using LCA.