4 days
3–4 hours daily
This course introduces multilevel/mixed modeling for nested and longitudinal data and its implementation in Stata. Mixed models contain both fixed effects, analogous to regression coefficients, and random effects, effects that vary across clusters. Participants will learn how to use mixed models to answer research questions about the observation- and cluster-level data and how to disaggregate these effects. Introductory theory, estimation, model building, and diagnostics will be discussed and demonstrated through many examples.
Are you more interested in panel-data models with unobserved individual-level heterogeneity, endogenous variables, or lagged variables? Check out our Panel-data analysis using Stata course.
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Meghan Cain
Assistant Director, Educational Services
Meghan Cain is the Assistant Director of Educational Services at StataCorp LLC. 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 oversees training courses, webinars, and videos, and develops and presents many of them herself. She also reviews Stata Press books and provides statistical expertise for marketing content.
The nested data problem and potential solutions
The multilevel/mixed-effects model (MLMM) with mixed
Longitudinal data analysis
More complex models
Report results from a multilevel modeling analysis
“The course was great—exactly what I was looking for. The instructor did an excellent job making difficult concepts easy to understand, showing us tricks in using Stata, and she also skillfully answered many difficult questions.”
Dr. William Mangino
Associate Professor of Sociology
Hofstra University
