Introduction to Finite Mixture Modeling
The Center for Integrated Latent Variable Research (CILVR)
is pleased to announce its COMPLETELY ONLINE offering
INTRODUCTION TO FINITE MIXTURE MODELING
April 28-30, 2021 (Wednesday-Friday)
Model-based clustering methods, commonly referred to as finite mixture modeling, have been applied to a wide variety of cross-sectional and longitudinal data to account for heterogeneity in population characteristics. Finite mixture models represent a type of latent variable model that expresses 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 analytic models. This three-day course is intended as both a theoretical and practical introduction to finite mixture modeling as it pertains to statistical methods regularly used in educational, behavioral, and social science research.
Instructor:
Dr. Jeffrey R. Harring, University of Maryland
(harring@umd.edu)
Participants may join us ONLINE, from anywhere in the world with a good wi-fi connection -- synchronously (real time) or
asynchronously (delayed/recorded).