|Where:||Join us from anywhere!|
|Cost:||Free—but registrations are limited|
Multilevel/mixed-effects models account for nested or clustered data structures through the incorporation of random effects. The Bayesian framework offers a natural approach to the estimation of random effects, thus offering many benefits and adding flexibility to the analysis of clustered data.
Join us for an introduction to fitting Bayesian multilevel models in Stata. We will start by using the bayes: prefix, which can be added before any of Stata’s multilevel or longitudinal/panel-data regression models, including mixed, the me commands, and the xt commands. It’s easy to use and offers several options for priors, MCMC settings, and diagnostics. For more advanced usage, the bayesmh command offers joint modeling of multivariate outcomes that include both linear and nonlinear components, more fine control of likelihood functions and prior specifications, and much more. In this webinar, both commands will be demonstrated and compared, and their results will be interpreted.
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.
Meghan Cain is a Senior Statistician at StataCorp. 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 develops and presents training on these and other topics. She also conducts webinars, works with developers to produce Stata documentation, and contributes to Stata blogs.