This webinar will present the steps to apply a type of latent class analysis on longitudinal data commonly known as growth mixture model (GMM). This family of models is a natural extension of the latent variable model. GMM combines longitudinal data analysis and Latent Class Analysis to extract the probabilities of each case to belong to latent trajectories with different model parameters. A brief (not exhaustive) list of steps to prepare, analyze and interpret GMM will be presented. A published case will be described to exemplify an application of GMM and its complexity.
Finally, an alternative approach to GMM will be presented where the longitudinal model approach is linear mixed effects (also known as hierarchical linear model or multilevel modeling). The idea is the same as in GMM using growth curve modeling, mainly that the latent class membership specifies specific unobserved trajectories. These models are equivalent to GMM and are sometimes referred to heterogeneous linear mixed effects, underlining the idea that the sample may not belong to one single homogeneous population, but potentially to a mixture of distributions.
About the Instructor
Before working as a psychometrician, he worked extensively as a research and statistical consultant for faculty members, students at York University, and a variety of clients including health researchers, imaging clinics, educational institutions, and the Ontario government.
He has extensive expertise in factor analytical and latent-trait methods of measurement, as well as applications of linear mixed effects to nested, longitudinal, unbalanced data.
Manolo is passionate about the implementation of technology as an educational, learning, and training tool. He is an Excel, SPSS, and Mplus power user, and a supporter of the expanding use of the R language and environment for statistical computing.