Latent Class Analysis

Overview

In this course, you will be introduced to latent class analysis (LCA), a modern statistical technique that is increasingly used in research to identify hidden subgroups within categorical data. You will learn how LCA can be used to uncover common patterns across multiple variables and explore both quantitative and qualitative differences within a population.

The course focuses on building a strong theoretical understanding of LCA, including how models are constructed, interpreted, and evaluated. While no software is used directly, practical examples are included to support understanding and prepare you for applying LCA in your own work or exploring more advanced extensions such as longitudinal models.

Learning outcomes

By the end of the course, you should be able to:

• Understand why latent class analysis (LCA) is used in research
• Explain the LCA modelling process and key parameters (e.g. class proportions and item response probabilities)
• Identify and interpret latent classes within a dataset
• Select the appropriate number of latent classes using statistical and practical criteria
• Assess model quality using diagnostic measures (e.g. information criteria, entropy, class sizes)
• Interpret and label latent classes in a meaningful and applied context
• Gain the foundation needed to implement LCA using appropriate software and explore more advanced extensions