This workshop introduces mixture modeling as a powerful technique for uncovering hidden subgroups within your data. Participants will learn how to identify latent classes, underlying subpopulations that drive patterns in observed data, and how to determine the optimal number of classes using model fit comparisons and significance testing. How to estimate latent class membership probabilities for individual observations and integrate the resulting classification variable into broader regression frameworks, whether as a predictor or an outcome will also be presented. This workshop is ideal for researchers looking to move beyond one-size-fits-all models and capture meaningful heterogeneity in their data.

This is a researcher focused workshop.