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Organizational training

Multilevel/mixed models using Stata


Learn how to fit multilevel/mixed models in Stata. 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.

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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Course topics

  • 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
  • The random coefficients model
    • Adding random coefficients
    • Covariance structures for random effects
    • Specifying models hierarchically
    • Growth curves
  • More complex models
    • Multiple-level models
    • Crossed-effects models
    • Complex and grouped constraints on variance components
    • Heteroskedastic errors
    • 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
    • Residuals
    • Fit diagnostics
    • Diagnostic plots
    • Cataloging and comparing mixed-model results in Stata
    • Likelihood-ratio (LR) tests


Basic knowledge of standard linear regression and a working knowledge of Stata.


This course is available in-person or virtually. In-person training courses generally run for eight hours per day and include morning and afternoon breaks and a lunch break. Virtual training courses are typically divided into three- to four-hour daily sessions. You can arrange a convenient schedule with your instructor.

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