This two-day course is an introduction to using Stata to fit multilevel/mixed models. Mixed models contain both fixed effects analogous to the coefficients in standard regression models and random effects not directly estimated but instead summarized through the unique elements of their variance–covariance matrix. Mixed models may contain multiple levels of nested random effects. These models are also referred to as multilevel or hierarchical models.
The course will be interactive, use real data, and offer ample opportunity for specific research questions and for working exercises to reinforce what is learned.
We offer a 15% discount for group enrollments of three or more participants.
Modeling clustered data
The random-intercept model
The within estimator versus the generalized least squares (GLS) estimator; the Hausman test
Maximum likelihood and restricted maximum likelihood
Using the mixed and xtreg commands for the random-intercept model
Small sample inference for fixed effects in mixed models New
The random coefficients model
Adding random coefficients
Covariance structures for random effects
Specifying models hierarchically
More complex models
Complex and grouped constraints on variance components
Alternate error structures
Binary and count responses
Models for binary and count responses
Estimation via adaptive Gaussian quadrature
Model building using the Laplacian approximation
Predictions, model diagnostics, and other postestimation tasks
Empirical Bayes predictions
Cataloging and comparing mixed-model results in Stata
Likelihood-ratio (LR) tests
Knowledge of linear regression and a working knowledge of Stata.
Currently, there are no scheduled sessions of this course.
Enrollment is limited.
Computers with Stata installed are provided at all public training
All training courses run from 8:30 a.m. to 4:30 p.m. each day.
A continental breakfast, lunch, and an afternoon snack will also be
provided; the breakfast is available before the course begins.