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Applied Latent Class Analysis 2017-06-22T18:01:03+00:00

Applied Latent Class Analysis

Session 1: May 1 – 3, 2017
Lisbon, Portugal
DoubleTree by Hilton Lisbon – Fontana Park

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Student/Post-DocFaculty/Professional

Statistics Course Objectives

The 2.5 day mini-camp on Applied Latent Class Analysis will enable participants to:

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

This course is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies for cross-sectional categorical data using Mplus that might be most appropriate for their research questions.  The course is also intended to provide a foundation for future learning about mixture modeling and resources to guide such endeavors.

Course Syllabus

Monday May 1, 2017
9:00-9:30 Welcome and introductions
9:30-10:45 Overview of categorical data analysis in Mplus
10:45-11:00 Snack and refreshment break
11:00-12:30 Introduction to finite mixture modeling and LCA
12:30-1:30 Lunch break
1:30-3:15 Latent class enumeration
3:15-3:30 Snack and refreshment break
3:30-5:00 Latent class enumeration (continued)
Tuesday May 2, 2017
9:00-9:30 Q & A
9:30-10:45 Latent class enumeration (continued)
10:45-11:00 Snack and refreshment break
11:00-12:30 Introduction to latent class regression (LCR)
12:30-1:30 Lunch Break
1:30-3:15 Latent class MIMIC modeling for DIF
3:15-3:30 Snack and refreshment break
3:30-5:00 Stepwise LCA for distal outcomes
Wednesday May 3, 2017
9:00-9:30 Q&A
9:30-10:45 Multiple group LCA
10:45-11:00 Snack and refreshment break
11:00-12:30 LCA extensions and wrap-up

Why Should You Attend?

  • Get 1 on 1 Consultation With Instructor
  • Professional Networking
  • Peer Socializing
  • Collaboration
  • All Course Resources
  • Breakfast (For Students Staying At Venue Hotel)
  • Lunch, and All-Day Tea/Coffee/Snack Service
  • After Class Networking Mixers

Statistical Methods Course Description

Overview: An introduction to cross-sectional “person-centered” data analysis. Topics include traditional latent class analysis (i.e., LCA with categorical indicators), multiple group LCA, measurement invariance testing, and modern stepwise methods for modeling predictors and outcomes of latent class membership. Hands-on practice with Mplus is provided.

This 2.5-day mini-camp is an intensive short course in the fundamentals of cross-sectional latent class analysis in Mplus.

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 manifest 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 course will introduce participants to the prevailing “best practices” for direct applications of basic finite mixture modeling to cross-sectional categorical data, specifically latent class analysis (LCA), in terms of model assumptions, specification, estimation, evaluation, selection, and interpretation.  Conditional latent class models that allow for the inclusion of correlates and predictors of latent class membership as well as distal outcomes of latent class membership will be presented, including the most recent “stepwise” or “3-step” models.  Multiple group LCA and measurement invariance testing will be discussed.  The course will touch briefly on some multilevel extensions of LCA, 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 course.

Instructor: Katherine Masyn Ph.D.

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 Mplus will be given temporary access to a cloud server with a fully functioning version 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 course materials, including PowerPoint slides, Mplus scripts, output files, relevant supporting documentation, and recommended readings.

Course Audience

If you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent classes) characterized by qualitative differences in observed multivariate outcome distributions, this course is for you. You should have a good working knowledge of the principles and practice of multiple linear regression, binary logistic regression, multinomial logistic regression, and elementary statistical inference. Previous experience with factor analysis and SEM is not required but would be beneficial.  You do not need to know matrix algebra or likelihood theory.  No previous knowledge of LCA or Mplus is assumed. 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 course.

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