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Web based: Multilevel/Mixed Models Using Stata

This 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.

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 New

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

Prerequisites

  • Knowledge of linear regression and a working knowledge of Stata.

Click here to register for this Stata web based course!

Arrangement oversigt

Start dato 12. Nov 2019, 17:00
Slut dato 15. Nov 2019, 18:00
Sidste frist 12. Nov 2019, 0:00
Pris 1295 US$
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