Latent Class Analysis Using Stata

Latent variables are a useful tooll for modeling hypothetical constructs such as intelligence, ability, depression, and anxiety. Models for categorical latent variables are often called Latent Class Analysis (LCA) or Latent Profile Analysis (LPA). The levels of a categorical latent variable represent groups in the population and are called classes. We are interested in identifying and understanding these classes. LCA is characterized by discrete response variables while LPA is characterized by continuous response variables. This presentation will provide a brief introduction to LCA/LPA models and how to implement them using Stata.

Chuck Huber is Director of Statistical Outreach at StataCorp and Adjunct Associate Professor of Biostatistics at the Texas A&M School of Public Health and at the New York University School of Global 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 behavioral and health scientists. He has published in the areas of neurology, human and andimal genetics, alcohol and drube abuse prevention, nutrition and birth defects. Dr. Huber currently teaches survey sampling at NYU and introductory biostatistics at Texas A&M where he previously taught categorical data analysis, survey data analysis, and statistical genetics.