This presentation will introduce Latent Class Analysis (LCA) and its implementation in Mplus. LCA, a latent variable modeling approach, is used to classify people into groups that are similar on unobserved constructs, based on their response patterns. In LCA, group membership is unseen and is indicated by observed variables. LCA is a model-based approach and thus the results from LCA can be replicated in other samples. LCA provides researchers valuable insights into the various types of respondents and how to better construct future intervention strategies targeting different types of respondents. During the presentation, we will talk about the overview of LCA and learn how to conduct LCA in Mplus step-by-step through an example (data/syntax/output).

Visit https://education.uky.edu/edp/apslab/... to download the Handout and Mplus Files for this talk.