Title: Item Response Theory (IRT), Introduction to Bayesian Statistics, Bayesian IRT, & Machine Learning in Stata

presented by Chuck Huber, StatCorp

Date: Friday, 10/24/25, 10am - 3pm, ET, HALL 104.

Meeting Link: Link

WebEx Meeting Number: 2633 977 5419. Password: RMMESTAT

Abstract:

Item Response Theory: In this talk, I introduce the concepts and jargon of item response

theory including latent traits such as ability, item characteristic curves, difficulty,

discrimination, guessing, and differential item functioning. I also demonstrate how to use

Stata's -irt- commands to fit 1PL, 2PL, and 3PL models for binary items as well as partial credit,

generalized partial credit, rating scale, and graded response models for ordinal outcomes.

Introduction to Bayesian Statistics Using Stata: Bayesian analysis has become a popular tool

for many statistical applications. Yet many data analysts have little training in the theory of

Bayesian analysis and software used to fit Bayesian models. This talk will provide an intuitive

introduction to the concepts of Bayesian analysis and demonstrate how to fit Bayesian models

using Stata. No prior knowledge of Bayesian analysis is necessary, and specific topics will

include the relationship between likelihood functions, prior, and posterior distributions,

Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, and use of Stata's

Bayes prefix to fit Bayesian models.

Bayesian Item Response Theory: In this talk, I briefly review item response theory (IRT) and

introduce the concepts and jargon of Bayesian statistics. Then, I demonstrate how to use

Stata's -bayesmh- command to fit 3PL, 4PL, and 5PL IRT models which cannot be fit using

maximum likelihood. I finish by showing how to compare the fit of the Bayesian IRT models.

Introduction to Machine Learning and AI Using Stata: This talk will briefly review the history

of machine learning (ML) and artificial intelligence (AI), introduce relevant concepts and

language, and demonstrate how to use these tools in Stata. Specific examples may include

Lasso and elasticnet methods, Bayesian methods and MCMC, support vector machines (SVM)

using Python integration, random forests and gradient boosting machines using H2O, and the

user-written commands "chatgpt", "claude", "gemini", and "grok".

Bio: Dr. 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. He produces instructional videos for the Stata Youtube
channel, writes blog entries, develops online NetCourses and gives talks
about Stata at conferences and universities. He has published in the
areas of neurology, human and animal genetics, alcohol and drug
abuse prevention, nutrition and birth defects.