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Webinar: Latent class analysis using Stata


When: March 27, 2018 at 10:00 AM CT
Duration: 1 hour
Where: Join us from anywhere!
Cost: Free—but registrations are limited

We can use latent class analysis (LCA) to identify and understand unobserved groups in our data. These groups may be consumers with different buying preferences, adolescents with different patterns of behavior, or different health status classifications.

Join Chuck Huber, Senior Statistician, for an introduction to Stata 15's new LCA features. In this webinar, Chuck will demonstrate how to use Stata's gsem with categorical latent variables to identify and understand unobserved groups. Chuck will provide a brief introduction to standard latent class models—models that identify unobserved groups based a set of observed categorical outcomes. He will also discuss latent profile models in which the observed outcomes are continuous. See how you can fit latent class and latent profile models using Stata. And see how to use the results of those models to determine who is likely to be in a group and how that group's characteristics differ from other groups.

How to join

The webinar is free, but you must register to attend. Registrations are limited so register soon.

We will send you an email prior to the start of the course with instructions on how to access the webinar. You will need access to Adobe Connect to attend.

Don't miss this opportunity.


Registration is now closed.

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Presenter: Chuck Huber

Chuck Huber portrait

Chuck Huber is a Senior Statistician at StataCorp and an adjunct associate professor of biostatistics at the Texas A&M School of Public Health. In addition to working with Stata's team of software developers, he produces instructional videos for the Stata YouTube channel, writes blog entries, develops online NetCourses, and gives talks about Stata at conferences and universities. Most of his current work is focused on statistical methods used by psychologists and other behavioral scientists. He has published in the areas of neurology, human and animal genetics, alcohol and drug abuse prevention, nutrition, and birth defects.





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