IN PERSON – 5-day Statistics Short Course

Seminar Overview:

An introduction to “person-centered” data analysis. Topics include latent class analysis, latent class cluster analysis, modeling predictors and outcomes of latent class membership, and select extensions. Hands-on practice with Mplus is provided.

This five-day camp is an intensive short seminar in the fundamentals of finite mixture modeling.

Seminar Description:

Finite mixture models are a type of latent variable model that express the overall distribution of one or more variables as a mixture of a finite number of component distributions. In direct applications, one assumes that the overall population heterogeneity with respect to a set of manifest variables is due to the existence of two or more distinct homogeneous subgroups, or latent classes, of individuals. These approaches are often termed “person-centered” analyses in contrast to the “variable-centered” analyses of conventional factor and SEM models.

This seminar will introduce participants to the prevailing “best practices” for direct applications of basic finite mixture modeling to cross-sectional data, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA. s), in terms of model assumptions, specification, estimation, evaluation, selection, and interpretation. Models that allow for the inclusion of correlates and predictors of latent class membership as well as distal outcomes of latent class membership will be presented. The seminar will also explore “hybrid” latent variable models that include both latent factors and latent classes (termed factor mixture models) and will touch briefly on some   longitudinal extensions of mixture modeling, as time allows (for a more in-depth treatment, see the Stats Camp Session 2 seminar on longitudinal mixture modeling). The implementation of these models in the most recent version of the Mplus software will be demonstrated throughout the seminar.