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Dependent variables
- Continuous
- Binary—logistic model
- Count—Poisson model
Types of models
- Multilevel models
- Hierarchical models
- Mixed models
- Two-, three-, and multi-way 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
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Estimation
- Maximum likelihood (ML)
- Restricted maximum likelihood (REML)
Other features
- Factor notation for specifying effects
- Allow unbalanced designs and unbalanced panels
- EM method starting values
Predictions
- Predicted outcomes with and without effects
- Predicted effects
- Pearson, deviance, and Anscombe residuals for binary and count
outcomes
- Continous outcomes
- Best linear unbiased predictions (BLUPs) of any or
all effects
- BLUPs of fitted values
- Residuals and standardized residuals
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
- Marginal effects and elasticities with SEs and CIs
- Hausman tests
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