Multilevel and Mixed Models Using Stata


This three-day workshop 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 more than one level of nested random effects and hence these models are also referred to as "multilevel" or "hierarchical models," particularly in the social sciences. Stata's approach to linear mixed models is to assign random effects to independent panels where a hierarchy of nested panels can be defined for handling nested random effects.

We will start by showing how random intercept models are related to classical linear models and will become familiar with the terminology for both approaches. Next, we will make the jump from random intercepts to random coefficients and the various covariance structures that can be imposed with multiple random effects terms. We will then finish out estimation for linear mixed models by seeing that Stata has niceties which allow fitting more complex models including crossed effects models, growth curve models, and models with complex and grouped constraints on covariance structures. After all the model fitting, we will turn to common post-estimation tasks such as predictions, model diagnostics, and model comparisons. To finish up, we will apply what we have learned about linear mixed models to models for other types of responses, in particular binary and count responses.

The workshop will be interactive in nature as we will consider concrete examples using Stata as we learn each of the concepts.

Fee: Members = $1500; Non-members = $2800

Tags: multilevel models, mixed models

Course Sections