Finite Mixture Modeling May 2019

Event Phone: 1-610-715-0115

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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 2-Day Seminar 
Taught by Jeffrey Harring, Ph.D.

Finite mixture modeling (FMM), commonly referred to as model-based clustering methods, has been applied to a wide variety of cross-sectional and longitudinal data to account for heterogeneity in population characteristics. This two-day seminar will give you the theoretical background and practical skills to address interesting research questions using FMM.

The seminar starts with an introduction to mixture modeling in the context of univariate and multivariate distributions including general linear models, illustrating the use of MplusA brief introduction of how FMMs are estimated quickly gives way to identifying latent classes and a discussion of the challenges inherent in fitting such models. Next, structural equation models with measured and latent variables are briefly reviewed, providing a launching point to discuss latent variable mixture models for cross-sectional data and growth mixture models for longitudinal data. The integration of covariates and distal outcomes into an analysis will be discussed.

The seminar will combine lectures, software demonstrations, computer exercises, and discussion. There will be ample opportunities for participants to discuss how FMMs can be applied to their own research.

At the end of the seminar, participants will:

  • understand how finite mixture models relate to the statistical methods and models (e.g., latent growth model) underlying the mixture models more broadly.
  • conceptually understand the notation of finite mixture models.
  • develop a principled modeling framework to step through any mixture analysis.
  • understand the data analytic decision points in this modeling framework as they relate to the current methodological literature and conventional wisdom of fitting mixture models.
  • understand the components necessary to specify mixture models using statistical software.
  • be able to interpret the input/output from Mplus related to running a mixture analysis covered in the course.

Venue:  

Address:
1515 Market Street, Philadelphia, Pennsylvania, 19103, United States