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| Duration: | 1 hour |
|---|---|
| Where: | Join us from anywhere! |
| Cost: | Free—but registrations are limited |
In latent class analysis (LCA), we use a categorical latent variable to represent unobserved groups in the population that we call classes. We are interested in identifying and understanding these classes
This is an introduction to Stata's LCA features. In this webinar, we will provide a brief introduction to LCA and demonstrate analyses using Stata's gsem command. We will use gsem's postestimation features to report class-specific marginal means, estimate class membership probabilities, and calculate goodness-of-fit statistics. We will show how to constrain and relax parameters across classes, and how to compare models with different numbers of classes. We will also show an example of a finite mixture model (FMM), in which we suspect unobserved classes in a regression model.
The webinar is free, but you must register to attend. Registrations are limited so register soon.
We will send you an email with instructions on how to join prior to the start of the webinar.
Meghan Cain is a Senior Statistician at StataCorp. She earned her PhD in quantitative psychology from the University of Notre Dame, where her research focused on structural equation modeling, multilevel modeling, and Bayesian statistics. At Stata, she develops and presents training on these and other topics. She also conducts webinars, works with developers to produce Stata documentation, and contributes to Stata blogs.
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