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.

Multivariate finite mixture models are expressed using vectors and matrices, so some familiarity with linear algebra can be helpful (see our free Matrix Algebra review) but this is not required to fully benefit from 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. Auxiliary Variables: Predictors and Distal Outcomes
4.1 Hypothesis Testing Within Finite Mixture Models
4.2 Prediction of Class Membership (Who's in Which Class?)
4.3 Predicting Distal Outcomes (What Happens Next?)
4.4 Latent Class Moderation (Do Relations Between Other Variables Differ by Class?)

Chapter 5. Extensions and Next Steps
5.1 Summary of Core Topics Covered
5.2 Where to Go From Here: Extensions and Next Steps
- Longitudinal Mixture Models
- Other Unsupervised Learning Techniques

Recorded software demonstrations are provided for 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.

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?

Content for this class is evergreen, meaning participants will always have access to the most current version of the course materials. Thus, if we replace a lecture recording or software demonstration, or update a syntax file, you will have immediate access to the updated content without needing to re-enroll in the class. Even more awesome.