# Course: Introduction to Latent Class Cluster Analysis

Date:
July 6-8, 2015

Teacher(s):
Jeroen K. Vermunt, Tilburg University
Margot Sijssens-Bennink, Statistical Innovations

Location:
Barcelona , Spain

Course content:
Latent class (LC) analysis is used in a broad range of research fields with the aim to cluster respondents into a small number subgroups called latent classes. Typically the observed variables used in a LC analysis are categorical variables, but it also possible to use continuous variables or counts. In this introductory course you will learn how to perform such an analysis by yourself.  We will not only focus on practical skills, but also discuss the most important parts of the underlying theory. All steps will be illustrated using empirical examples. During this course we will use the software package Latent GOLD.

First, using a simple example application, we present the structure of the basic LC model for categorical variables and discuss its key model assumptions. Subsequently, we switch to a more realistic example to discuss the important issue of model selection or, more specifically, how to determine the number of latent classes. For model selection, we use information criteria (AIC, AIC3, and BIC), goodness-of-fit statistics (possibly with bootstrap p-values), bivariate residuals, bootstrapped likelihood-ratio tests, and Wald tests.

Once a model with a specific number of latent classes is selected, respondents are classified into the latent classes based on their posterior class membership probabilities. We discuss how these classifications are obtained, as well as how the quality of the classifications can be evaluated using so-called classification statistics.

Subsequently, we will discuss various important extensions of the basic LC model, such as LC models which relax the local dependence assumption, and LC models for ordinal, continuous, and count variables.

Finally, we deal with the important issue on how to investigate the relationship between the individuals’ class memberships with covariates and distal outcomes. This can be done in various ways, among others using the recently developed bias adjusted three-step approach. We also discuss multiple-group LC analysis.

You don’t need any background in LC analysis to be able to follow this introductory course. Also researchers with some experience in LC analysis will benefit from reviewing the basics since they will pick up more of the details while beginning users can focus on the global learning goals of the course.