//Applied Longitudinal Mixture Modeling Seminar
Applied Longitudinal Mixture Modeling Seminar 2018-05-14T20:15:06+00:00

Applied Longitudinal Mixture Modeling Seminar

Session 2: June 11 – 15, 2018
Albuquerque, NM – Embassy Suites


FAQVenue Info
$1,795 Faculty/Professional or $1,095 Student/Post-Doc

Payment Options
Per Seminar Qty
Professionalshow details + $1,795 (USD)  
Studentshow details + $1,095 (USD)  

Statistics Seminar Objectives

The five-day training institute on Applied Longitudinal Mixture Modeling will enable participants to:

  • Acquire understanding of longitudinal mixture modeling techniques as applied in the social and behavioral sciences.
  • Develop an appreciation for the research questions and data best suited for different longitudinal mixture models and the common pitfalls leading to the misuse of such models.
  • Gain detailed knowledge of current “best practices” for longitudinal mixture model specification, estimation, selection, evaluation, comparison, interpretation, and presentation.
  • Understand how growth mixture models and latent transition models may be integrated into a larger (latent) variable system.
  • Become acquainted with a variety of longitudinal mixture modeling extensions.

Seminar Syllabus

Summer Stats Camp 2018: Longitudinal Mixture Modeling
Monday June 11, 2018
9:00-9:30 Welcome and introductions
9:30-10:45 Overview of longitudinal mixture modeling in a general latent variable framework; Review of latent class analysis (LCA) and latent profile analysis (LPA)
10:45-11:00 Snack and refreshment break
11:00-12:30 Repeated measures latent class analysis (RMLCA)
12:30-1:30 Lunch break
1:30-3:15 Introduction to latent transition analysis (LTA)
3:15-3:30 Snack and refreshment break
3:30-5:00 LTA (continued)
Tuesday June 12, 2018
9:00-9:30 Q & A
9:30-10:45 LTA with predictors and distal outcomes
10:45-11:00 Snack and refreshment break
11:00-12:30 Higher-order LTA
12:30-1:30 Lunch break
1:30-3:15 Associative latent transition analysis (ALTA)
3:15-3:30 Snack and refreshment break
3:30-5:00 Multilevel LTA
Wednesday June 13, 2018
9:00-10:45 Review of latent growth curve modeling
10:45-11:00 Snack and refreshment break
11:00-12:30 Introduction to growth mixture modeling
12:30-1:30 Lunch break
1:30-3:15 Latent class growth analysis (LCGA)
3:15-3:30 Snack and refreshment break
3:30-5:00 Growth mixture modeling (GMM)
Thursday June 14, 2018
9:00-9:30 Q & A
9:30-10:45 Growth mixture modeling with predictors and distal outcomes (including 3-Step procedures)
10:45-11:00 Snack and refreshment break
11:00-12:30 Growth mixture modeling with predictors and distal outcomes (continued)
12:30-1:30 Lunch break
1:30-3:15 Combined GMM + LTA models
3:15-3:30 Snack and refreshment break
3:30-5:00 Overview of advanced topics in longitudinal mixture modeling
Friday June 15, 2018
9:00-10:45 Discrete-time survival analysis (DTSA) using latent variables
10:45-11:00 Snack and Refreshment Break
11:00-12:30 DTSA (continued)
12:30-1:30 Lunch break
1:30-4:00 Individual consultations

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Seminar Resources
  • All-Day Refreshments
  • Breakfast (Embassy guests), Lunches, & Snacks Daily

Statistical Methods Seminar Description

Overview: An advanced seminar 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 seminar 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 seminar 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 seminar 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 seminar.

Instructors: Katherine Masyn

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.

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 seminar materials, including PowerPoint slides, Mplus scripts, output files, relevant supporting documentation, and recommended readings.

Seminar Audience

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 seminar 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 seminar. If you have not taken the foundations seminar, you should have extensive experience doing latent class analysis and/or latent profile analysis in Mplus or have taken a graduate-level seminar 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 seminar.

Seminar Materials

Click Here to View Seminar Materials (Password Protected)

Applied Longitudinal Mixture Modeling Seminar Katherine and Karen Gibson