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$ 945.00

LIVESTREAM / ASYNCHRONOUS – 4-day Statistics Short Course

Seminar Overview:

An introduction to “person-centered” data analysis. Topics include latent profile analysis (aka, latent class cluster analysis), and modeling predictors and outcomes of latent profile membership. Hands-on practice with Mplus is provided.

Seminar Topics:

Latent Profile Analysis (LPA) steps including research questions appropriate for latent profile analysis, profile (class) enumeration and assessing profile model results (classification quality, profile homogeneity and separation), predicting profile membership with other variables and profile membership predicting outcomes. Practice analyses will be completed to build comfort with syntax and reading of output. We will also cover how to interpret and present the results to maximize audience understanding.

Seminar Description:

This four-day camp is an intensive short seminar in the fundamentals of latent profile analysis (LPA).
LPA is a type of latent variable model-based finite mixture models that express the overall distribution of one or more continuous 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 continuous, manifest variables is due to the existence of two or more distinct homogeneous subgroups, or latent profiles, 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 basic latent profile analysis to cross-sectional data, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA), including model assumptions, specification, estimation, evaluation, selection, and interpretation. Models that allow for the inclusion of correlates and predictors of latent profile membership as well as distal outcomes of latent profile membership will be presented. The implementation of these models in the most recent version of the Mplus software will be demonstrated and practiced throughout the seminar.

Instructor: Whitney Moore, Ph.D.

Whitney Moore Ph.D.

Dr. Whitney Moore is an Assistant Professor of Kinesiology at East Carolina University. Whitney received her Ph.D. in the Psychosocial Aspects of Health and Physical Activity from the University of Kansas. She has been a Stats Camp instructor since 2012 (after experience being a “counselor” for SEM, Longitudinal SEM, and MLM). Whitney has taught graduate courses in research design, introduction to statistics, ANOVA, SEM, and measurement development at two different R1 institutions. Her research is at the intersection of advanced quantitative methods and psychosocial aspects applied to sport, exercise, and physical education contexts. This is particularly illustrated in her work on measurement development; helping to develop or modify 12 measures in the last 10 years. Whitney is particularly interested in planned missing data designs, finite mixture modeling, plus mediation and moderation in SEM.

APA Continuing Education Credits:

This course provides 16 credit hours for continuing education. Stats Camp Foundation is approved by the American Psychological Association to sponsor continuing education for psychologists. Stats Camp Foundation maintains responsibility for this program and its content.

Seminar Includes:

Materials, downloads, recorded course video viewable for up to one year.

Learning Objectives:

This comprehensive 4-day statistics training institute on Latent Profile Analysis will enable participants to:

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

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

Seminar Prerequisites:

If you are interested in learning “person-centered” statistical modeling techniques that can identify unobserved subgroups (latent profiles) characterized by qualitative differences in observed multivariate outcome distributions, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression and elementary statistical inference. You will get the most out of the seminar if you already have experience with binary and multinomial logistic regression. You do not need to know matrix algebra, likelihood theory, or SEM, although that knowledge would be beneficial. No previous knowledge of mixture modeling, latent class analysis, latent profile analysis, 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 seminar.

Software and Computer Support:

Participants should have 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 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.

All statistical software used at Stats Camp will be available, free to participants, on our SMORS (statistical modeling on remote servers) system for the duration of camp.

2024 Stats Camp: 4-Day LPA
Day 1 Day 1 (Friday)
12:00-12:30 Welcome and introductions
12:30-1:30 Overview of mixture modeling in a general latent variable framework
1:30-1:45 Break
1:45-3:15 Overview of LPA Analysis Steps
3:15-3:30 Break
3:30-5:00 Mplus syntax introduction with Latent Profile Analysis (LPA) example
Day 2 Day 2 (Saturday)
12:00-1:30 LPA class enumeration across variance-covariance structures introduction
1:30-1:45 Snack and refreshment break
1:45-3:15 Practice running LPA models in Mplus
3:15-3:30 Lunch break
3:30-5:00 Syntax and interpretation of output for LPA enumeration across variance-covariance structures
Day 3 Day 3 (Sunday)
12:00-1:30 Multinomial Logistic Regression Review (On Own)
1:30-1:45 Snack and refreshment break
1:45-3:15 Examination of Profile homogeneity and separation
3:15-3:30 Lunch break
3:30-5:00 Individual consultation & Practice of LPA modeling process
Day 4 Day 4 (Monday)
12:00-1:30 Introduction to latent class regression (LCR) with inclusion of predictive covariates
1:30-1:45 Snack and refreshment break
1:45-3:15 LCR continued with inclusion of distal outcomes
3:15-3:30 Lunch break
3:30-5:00 Individual consultation

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