Latent class analysis (LCA) is commonly used across social and behavioral research and the health sciences to uncover hidden structures (subgroups/latent classes) within a population (heterogeneity). For example, in education research, LCA has helped identify profiles of student engagement from survey items measuring motivation, participation, and academic self-efficacy. Epidemiologists use it to derive socioeconomic disadvantage profiles from a set of binary indicators such as housing instability, food insecurity, and unemployment. Marketing researchers apply it to segment consumers into preference classes based on product choice data. LCA is especially useful when the latent classes are theoretically meaningful but unobservable, inferred from the pattern of responses across multiple observed indicators. LCA can be viewed as a latent-variable model with categorical latent variables.

 

The workshop provides a comprehensive, application-oriented introduction to latent class analysis for both categorical and continuous responses (latent profile analysis), with a focus on applications in the social and behavioral sciences. The workshop begins with a comprehensive introduction to the foundational latent class model. From there, the model is extended in several directions:

 

Organizational structure of the course

The course will be structured around four hours of morning lectures and a two-hour afternoon computer lab. In the lectures, you will learn the methods and tools, be introduced to the underlying methodology, and work through examples based on real data applications. The lecture slides will also include R code for all examples covered. You will be encouraged to participate by asking questions and completing short R exercises.

 

During the afternoon lab session, you will work with real data examples and guided exercises to fit and interpret the models introduced in the lectures. This will give you the opportunity to complete assignments and become familiar with the models and their output.

The lecturer and a teaching assistant will be available during the lab sessions to answer questions. We will also use breakout rooms to support small-group discussion. After each exercise, the lecturer will review the code and interpret the output with the full class. Solutions will be shared at the end of each day.

You will find the course useful if:

By the end of the course, you will:

The course will involve hands-on exercises using R. As a participant, you should have your own laptop with a current version of R (≥4.3.0) and, preferably, RStudio installed. The list of packages to be used will be distributed with the course materials.