Dependent variables

• Continuous
• Binary—logistic model
• Count—Poisson model

Types of models

• Multilevel models
• Hierarchical models
• Mixed models
• Two-, three-, and multiway random-effects models
• Crossed random effects

Types of effects

• Random effects (variance components)
• Random intercepts
• Random coefficients
• Fixed effects

Effect covariance structures

• Identity—shared variance parameter for specified effects with no covariances
• Independent—unique variance parameter for each specified effect with no covariances
• Exchangeable—shared variance parameter and single shared covariance parameter for specified effects
• Unstructured—unique variance parameter for each specified effect and unique covariance parameter for each pair of effects
• Compound—any combination of the above

Residual-error structures for linear models

• Independent
• Exchangeable
• Autoregressive
• Moving-average
• Exponential
• Banded
• Toeplitz
• Unstructured

Estimation

• Maximum likelihood (ML)
• Restricted maximum likelihood (REML)

Survey data

• Sampling weights
• Robust variance estimation
• Clustered variance estimation
• Weights at each model level
• Weight rescaling
• Frequency weights

Other features

• Factor notation for specifying effects
• Allow unbalanced designs and unbalanced panels
• EM method starting values

Other postestimation analysis

• Linear and nonlinear combinations of coefficients with SEs and CIs
• Wald tests of linear and nonlinear constraints
• Likelihood-ratio tests
• Linear and nonlinear predictions
• Summarize the composition of nested groups
• Adjusted predictions
• Information criteria—AIC and BIC
• Hausman tests

Predictions

• Predicted outcomes with and without effects
• Predicted effects
• Pearson, deviance, and Anscombe residuals for binary and count outcomes
• Continuous outcomes
• Best linear unbiased predictions (BLUPs) of any or all effects
• BLUPs of fitted values
• Standard errors of BLUPs
• Residuals and standardized residuals

Factor variables

• Automatically create indicators based on categorical variables
• Form interactions among discrete and continuous variables
• Include polynomial terms
• Perform contrasts of categories/levels

Marginal analysis

• Estimated marginal means
• Marginal and partial effects
• Average marginal and partial effects
• Least-squares means
• Predictive margins
• Adjusted predictions, means, and effects
• Contrasts of margins
• Pairwise comparisons of margins
• Profile plots
• Graphs of margins and marginal effects

Contrasts

• Analysis of main effects, simple effects, interaction effects, partial interaction effects, and nested effects
• Comparisons against reference groups, of adjacent levels, or against the grand mean
• Orthogonal polynomials
• Helmert contrasts
• Custom contrasts
• ANOVA-style tests
• Contrasts of nonlinear responses
• Multiple-comparison adjustments
• Balanced and unbalanced data
• Contrasts of means, intercepts, and slopes
• Graphs of contrasts
• Interaction plots

Pairwise comparisons

• Compare estimated means, intercepts, and slopes
• Compare marginal means, intercepts, and slopes
• Balanced and unbalanced data
• Nonlinear responses
• Multiple-comparison adjustments: Bonferroni, Šidák, Scheffé, Tukey HSD, Duncan, and Student-Newman-Keuls adjustments
• Group comparisons that are significant
• Graphs of pairwise comparisons

Explore more about mixed models in Stata.