Mixture Modeling and Latent Class Analysis

Our motivating goal is to provide an enjoyable instructional experience that focuses on a large number of both introductory and advanced topics in mixture modeling and latent class/profile analysis. We strive to strike an equal balance between core concepts of the analytic techniques along with their practical application and interpretation when implemented with real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of mixture modeling and latent class/profile analysis and to be able to thoughtfully apply these procedures to their own data.

Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences. Knowledge of basic statistics is recommended, such as familiarity with the normal distribution and using p-values to make inferences, however core concepts will be reviewed during the class.

Chapter 1. Introduction
1.1 Introduction and Organization of the Workshop
1.2 When and How Clustering can be Useful

Chapter 2. Finite Normal Mixture Models with One Variable
2.1 The Development and Application of Finite Mixture Models
2.2 Explicating the Model
2.3 How to Specify, Fit, and Interpret the Model
2.4 Determining the Number of Latent Classes

Chapter 3. Finite Mixture Models with Multiple Variables (LPA & LCA)
3.1 Extending the Normal Mixture Model to Multiple Variables
3.2 Model Specifications for Multivariate Normal Mixtures
3.3 Latent Profile Analysis
3.4 Latent Class Analysis with Binary or Ordinal Indicators

Chapter 4. Relating Latent Classes to Predictors and Distal Outcomes
4.1 One-Step versus Three-Step Approaches
4.2 Predicting Who's in Which Class
4.3 Determining Long-Term Outcomes of Class Membership
4.4 Latent Class Moderation Analysis: Do Predictors Have Differential Importance Across Classes?

Chapter 5. Longitudinal Mixture Models
5.1 Distinct Trajectories of Change Over Time
5.2 Latent Class Growth Analysis / Semiparametric Groups Based Trajectory Modeling
5.3 "General" Growth Mixture Models with Random Effects
5.4 Survey of Latent Transition Analysis and Survival Mixture Models

Pre-recorded software demonstrations are provided for self-paced learning using Mplus and R. Note that R can be downloaded for free. While it is helpful to have some familiarity with Mplus or R, this is not necessary. The lectures which constitute the majority of the workshop are software-independent.

Mixture Modeling and Latent Class Analysis is a five-day workshop originally co-taught live via Zoom by Dan Bauer and Doug Steinley. Daily lectures were held from 10:00 to 3:30 with morning, lunch, and afternoon breaks. Sessions consisted of comprehensive lectures, detailed presentation of real-data examples, response to participant questions, and general discussion. Pre-recorded videos are available for all demonstrations in R and Mplus.

Self-paced participants receive lifetime access to all course materials, including complete course notes, lecture recordings, software demonstration notes, software demonstration recordings, and data and code for all examples. You can revisit these materials any time you like, without worrying about expiration dates. Pretty awesome, huh?