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$ 1,395.00

IN PERSON – 5-day Statistics Short Course

Seminar Overview:

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

This five-day camp is an intensive short seminar in the fundamentals of finite mixture modeling.

Seminar Description:

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 seminar will introduce participants to the prevailing “best practices” for direct applications of basic finite mixture modeling to cross-sectional data, specifically latent profile analysis (LPA) also known as latent class cluster analysis (LCCA. s), in terms of model assumptions, specification, estimation, evaluation, selection, and interpretation. 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. The seminar will also explore “hybrid” latent variable models that include both latent factors and latent classes (termed factor mixture models) and will touch briefly on some   longitudinal extensions of mixture modeling, as time allows (for a more in-depth treatment, see the Stats Camp Session 2 seminar on longitudinal mixture modeling). The implementation of these models in the most recent version of the Mplus software will be demonstrated throughout the seminar.

Instructor: Whitney Moore, Ph.D.

Latent Class Analysis Statistics Course

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 offers 29 hours of Continuing Education Credits. 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:

After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will be able to:

  • Describe mixture modeling in a general latent variable framework.
  • Perform binary, ordinal, and multinomial logistic regression.
  • Utilize Mplus to evaluate generalized linear models.
  • Identify best practice methods to enumerate latent classes for latent class analysis.
  • Perform latent class regression.
  • Conduct measurement invariance in a latent class analysis framework.
  • Derive distal outcomes in latent class analysis.
  • Perform structural equation mixture modeling.
  • Describe the process of finite mixture modeling
  • Enumerate latent classes in a finite mixture modeling framework.
  • Conduct finite mixture modeling with non-normal indicators.
  • Describe advanced topics and applications of mixture modeling, such as multilevel latent class analysis.

This seminar is intended to give participants the knowledge and understanding necessary to identify and effectively execute “person-centered” analysis strategies 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.

Latent Class Analysis Course Prerequisites:

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, or Mplus is assumed.

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 Studio 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.

Latent Class Analysis 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 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, 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.

Who Can Benefit From a Latent Class Analysis Course:

A latent class analysis course would be useful for researchers, data analysts, and practitioners who work with categorical data and want to identify unobserved subgroups or latent classes within their data. This includes individuals from a wide range of fields, such as psychology, sociology, epidemiology, marketing, education, and public health.

Specifically, individuals who may benefit from a latent class analysis course include:

  1. Researchers who want to identify subgroups of individuals or objects with similar characteristics, attitudes, behaviors, or preferences. For example, a researcher studying consumer behavior may want to identify different types of shoppers based on their buying habits and demographic characteristics.
  2. Data analysts who want to use latent class analysis as a tool for data reduction or variable selection. For example, a data analyst working with survey data may want to reduce the number of survey items to a smaller set of latent factors that capture the most important dimensions of the data.
  3. Practitioners who want to use latent class analysis as a tool for program evaluation or needs assessment. For example, a public health practitioner may want to identify different types of health behaviors or risk factors among a population in order to tailor intervention programs to specific subgroups.

A latent class analysis course would be useful for anyone who wants to gain a deeper understanding of how to use latent class analysis to identify meaningful subgroups within categorical data and make informed decisions based on the results.

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.

Summer Stats Camp 2024: LCA
Monday June 3, 2024
9:00-9:30 Welcome and introductions
9:30-10:45 Overview of mixture modeling in a general latent variable framework
10:45-11:00 Rest Break
11:00-12:30 Review of binary, ordinal, and multinomial logistic regression
12:30-1:30 Rest Break
1:30-3:15 Introduction to Mplus
3:15-3:30 Rest Break
3:30-5:00 Generalized linear modeling in Mplus
Tuesday June 6, 2024
9:00-9:30 Q & A
9:30-10:45 Introduction to Latent Class Analysis (LCA)
10:45-11:00 Rest Break
11:00-12:30 Latent class enumeration for LCA
12:30-1:30 Rest Break
1:30-3:15 Latent class enumeration (continued)
3:15-3:30 Rest Break
3:30-5:00 Introduction to Latent Class Regression (LCR)
Wednesday June 4, 2024
9:00-9:30 Q & A
9:30-10:45 LCR (continued)
10:45-11:00 Rest Break
11:00-12:30 Measurement invariance in LCA
12:30-1:30 Rest Break
1:30-3:15 Distal outcomes in LCA
3:15-3:30 Rest Break
3:30-5:00 Structural equation mixture modeling
Thursday June 5, 2024
9:00-9:30 Q & A
9:30-10:45 Introduction to Finite Mixture Modeling (FMM)
10:45-11:00 Rest Break
11:00-12:30 Latent class enumeration for FMM
12:30-1:30 Rest Break
1:30-3:15 Latent class enumeration for FMM (continued)
3:15-3:30 Rest Break
3:30-5:00 FMM with non-Normal indicators
Friday June 6, 2024
9:00-9:30 Q & A
9:30-10:45 Overview of “hybrid” factor mixture models (including growth mixture models)
10:45-11:00 Rest Break
11:00-12:30 Overview of advanced topics in mixture modeling (e.g., multilevel LCA)
12:30-1:30 Rest Break
1:30~4:30 Individual consultations

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