Growth Mixture Models: A Structural Equation Modeling Approach (Chapel Hill, NC)


  • Sarah Mustillo, University of Notre Dame

The Growth Mixture Model (GMM) is an extension of the Latent Growth Curve Model (LGCM) that identifies distinct subgroups of growth trajectories and allows individuals to vary around subgroup-specific mean trajectories. Conventional growth modeling estimates a single mean intercept and slope for each individual and variance parameters around the mean intercept and slope. The GMM relaxes the assumption that all individuals are drawn from a single population with common parameters by using latent trajectory classes, resulting in separate intercepts, slopes, and variance parameters for each subgroup.

This three-day workshop will provide training in estimating GMMs to analyze growth trajectories. Key features of this model are that it can identify the number and form of distinct subgroups of growth trajectories, estimate the proportion of the population in each subgroup, and model predictors of the trajectories and predictors of class membership. In addition to the basic model, this workshop will cover several extensions, such as including a distal outcome predicted by the trajectories, multiple group GMMs, and parallel process or joint trajectory models.

Prerequisites: Participants should be familiar with LGCMs. Familiarity with MPlus would be helpful but is not required.

Fee: Members = $1500; Non-members = $2800

Tags: Growth Mixture Models, Structural Equation Models, SEM

Course Sections

Section 1

Location: University of North Carolina -- Chapel Hill, NC

Date(s): June 12 - June 14

Time: 9:00 AM - 5:00 PM


  • Sarah Mustillo, University of Notre Dame