LCA

Week 16: April 14, 2025

Introduction to the Topic

This week, we dive into Latent Class Analysis (LCA), a method used to identify subgroups or classes within a population based on observed data. LCA is essential for uncovering hidden heterogeneity in data.

Key Concepts:

  • Latent Classes: Unobserved subgroups identified through patterns in the data.
  • Class Membership Probabilities: The likelihood that an individual belongs to a specific class.
  • Model Fit: Using tools like BIC and AIC to determine the optimal number of classes.
  • Interpreting Profiles: Understanding differences between identified classes.

Relevance:

  • Students will learn how to use LCA to identify meaningful subgroups in their data.
  • Students will explore applications of LCA in areas like health disparities, education, and consumer behavior.

Why This Is Important:

  • LCA provides insights into population heterogeneity, allowing researchers to identify distinct subgroups with unique characteristics.
  • It is a powerful tool for tailoring interventions and understanding diverse outcomes.

How This Ties Into the Overall Course:

  • Builds upon prior topics like EFA by extending latent variable modeling to categorical data.
  • Prepares students for advanced mixture modeling techniques and combining LCA with SEM.

By the end of this week, students will be able to perform LCA, interpret latent class solutions, and use these insights to answer research questions about population heterogeneity.

Class files

  • Download today’s slides here
  • Download the data for today from the National Child Health Survey (NCHS) in SPSS and CSV to import into JASP.
  • Here is my example excel file to create a chart of the conditional item probabilities

Additional Notes

Stay Calm… This is the Last Latent Class.

We’ve reached model convergence. The entropy is high. The fit is good. And yes… this is the final class.

For some of you (and for me), the fun is over. For others, the fun is just beginning. If that’s true, we’ve officially detected a latent subgroup of students.

Let’s hope the class proportions are:

  • Class 1 – “Loved Every Minute”: 99.1%
  • Class 2 – “Thrilled It’s Over”: 0.9%

Model fit? Impeccable. Classification? Accurate. Emotion? Mixed. But this… this was a class worth identifying.