Statistical Methods Seminar Description
Overview: An advanced course in “person-centered” longitudinal data analysis. Topics include repeated measures latent class analysis, semi-parametric group-based trajectory modeling, growth mixture modeling, latent transition analysis, associative latent transition analysis, and select extensions. Hands-on practice with Mplus is provided. This five-day summer institute is an advanced intensive short course in the analysis of longitudinal data using latent class analysis and finite mixture modeling. 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 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 course will introduce participants to the prevailing “best practices” for direct applications of mixture modeling to longitudinal data, specifically repeated measures latent class analysis (RMLCA), semi-parametric group-based trajectory analysis (a.k.a., latent class growth analysis), growth mixture modeling (GMM), latent transition analysis (LTA), and associative latent transition analysis (ALTA) in terms of model assumptions, specification, estimation, evaluation, selection, and interpretation. Growth mixture models that allow for the inclusion of correlates and predictors of latent trajectory class membership as well as distal outcomes of latent trajectory class membership will be presented. Latent transition models that allow for the inclusion of predictors of latent class membership over time, moderators of latent transitions, and higher order latent class variables (i.e., hidden Markov chain models) will be presented. The course will also explore model combinations (e.g., latent transition growth mixture models) and extensions (e.g., multilevel latent transition analysis, onset-to-growth mixture models, etc.) as participant interest dictates and time allows. The implementation of these models in the most recent version of the Mplus software will be demonstrated throughout the course.
Instructors: Katherine Masyn & Karen Nylund-Gibson
Katherine is an Associate Professor of Biostatistics at Georgia State University’s School of Public Health. She was previously on the faculty at the Harvard Graduate School of Education and received her doctorate in Social Research Methodology at UCLA under the mentorship of Prof. Bengt Muthén. Dr. Masyn’s research focuses on the development and application of latent variable statistical models related to: survival and event history analysis; multivariate and multifaceted longitudinal processes, e.g., latent transition growth mixture models; and, more broadly, the characterization and parameterization of both observed and unobserved population heterogeneity in cross-sectional and longitudinal settings, e.g., factor mixture models. Dr. Masyn enjoys close collaborations with colleagues from the fields of Human Development, Education, Psychology, Public Health, and Prevention Science and is known for her enthusiastic instruction on applied latent variable techniques for substantive audiences.
Karen is an Associate Professor of Quantitative Research Methodology in the Department of Education at the University of California, Santa Barbara (UCSB). She has been at UCSB since 2009. Prior to joining the department, she was a Postdoctoral Fellow at the Department of Mental Health at Johns Hopkins University. She earned her PhD at UCLA, working with Bengt Muthen. Her research focus is on latent variable models, specifically mixture models and she has published many articles and book chapters on developments, best practices, and applications of latent class analysis, latent transition analysis, and growth mixture models. She is dedicated to teaching and working with applied researchers from all areas of social researches to integrate latent variable models as a means to answering complex research questions.
Software and Computer Support
Participants should bring a laptop computer. Instruction will be provided for the methods using the most current version of Mplus (base program with mixture add-on or base program with combination add-on). Mplus is available for Windows, Mac, and Linux environments (www.statmodel.com).
Participants who do not have access to software will be given temporary access to the server that contains fully functioning versions of the recommended software.
Note: We will also make use of Excel and R to do various post-processing summaries.
Participants will receive an electronic copy of all course materials, including PowerPoint slides, Mplus scripts, output files, relevant supporting documentation, and recommended readings.
If you already have a strong background in the application of finite mixture models to cross-sectional data (e.g., latent class analysis and latent class cluster analysis) and you need to learn how to apply more advanced mixture models to longitudinal data, this course is for you. We strongly recommend that you attend our five-day intensive summer institute on applied latent class analysis and finite mixture modeling as a pre-requisite to taking this five-day advanced course. If you have not taken the foundations course, you should have extensive experience doing latent class analysis and/or latent profile analysis in Mplus or have taken a graduate-level course on latent class analysis using Mplus before enrolling. You do not need to know matrix algebra, likelihood theory, or longitudinal SEM, although knowledge of latent growth modeling in a SEM framework would be greatly beneficial. Participants from a variety of fields—including psychology, education, human development, public health, prevention science, sociology, marketing, business, biology, medicine, political science, and communication—will benefit from the course.
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