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

Livestream lectures will begin at 10:00 a.m. and finish at approximately 3:30 p.m. Eastern Time (US) each day. There will be 10-minute morning and afternoon breaks and a one-hour lunch break, the exact times of which are determined during lecture. Local time zones within which the participant is connected must be adjusted to correspond to Eastern Time (US).

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.

Dan Bauer and Doug Steinley co-teach the workshop live via Zoom, with recordings posted at the end of each day for those who might miss or wish to review any of the material. The lectures for the workshop are software-independent but demonstrations are provided in Mplus and R via pre-recorded videos.

Participants receive access to the livestream, one-year of access to recorded lecture and demonstration videos, downloadable complete PDF copies of the course notes and software demonstrations, as well as data and code for all examples. The PDFs are not time-limited and may be retained indefinitely but should not be distributed to others without obtaining prior permission.