Abstract: After a brief overview of the many uses of finite mixture modeling, applications of new mixture modeling developments are discussed. One major development goes beyond the conventional mixture of normal distributions to allow mixtures with flexible non-normal distributions. This has interesting applications to cluster analysis, factor analysis, SEM, and growth modeling. The talk focuses on applications of Growth Mixture Modeling for continuous outcomes that are skewed. Examples are drawn from national longitudinal surveys of BMI as well as twin studies. Extensions of this modeling to the joint study of survival and non-ignorable dropout are also discussed.
Bengt Muthén obtained his Ph.D. in Statistics at the University of Uppsala, Sweden and is Professor Emeritus at UCLA. He was the 1988-89 President of the Psychometric Society and the 2011 recipient of the Psychometric Society’s Lifetime Achievement Award. He has published extensively on latent variable modeling and is one of the developers of the Mplus computer program, which implements many of his statistical procedures.
Dr. Muthén’s research interests focus on the development of applied statistical methodology in areas of education and public health. Education applications concern achievement development while public health applications involve developmental studies in epidemiology and psychology. Methodological areas include latent variable modeling, analysis of individual differences in longitudinal data, preventive intervention studies, analysis of categorical data, multilevel modeling, and the development of statistical software (namely Mplus!).
For more information about Bengt Muthén, check out his website: www.statmodel.com/bmuthen/