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Introduction to Finite Mixture Modeling

Center for Integrated Latent Variable Research - April 28-30, 2021

The Center for Integrated Latent Variable Research (CILVR)
is pleased to announce its COMPLETELY ONLINE offering

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.

Dr. Jeffrey R. Harring, University of Maryland

Participants may join us ONLINE, from anywhere in the world with a good wi-fi connection -- synchronously (real time) or
asynchronously (delayed/recorded).

For more information, see the announcement

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