Survey regression models
Descriptive statistics
Linear regression models Updated
Structural equation models
Survival-data regression models Updated
Binary-response regression models Updated
Discrete-response regression models Updated
Fractional-response regression models
Poisson regression models
Instrumental-variables regression models
Regression models with selection Updated
Multilevel mixed-effects models Updated
Finite mixture models New
Item response theory
See multilevel models with survey data
Variance and standard-error estimates
Taylor-series linearization (Huber/White/sandwich)
Balanced and repeated replications (BRR)
Survey jackknife
Bootstrap (with bootstrap replicate weights)
Successive difference replication (SDR)
Sampling designs
Sampling (probability) weights
Stratification
Clustering
Multistage designs
Weights at each sampling stage
Finite population correction in all stages
Support for strata with one sampling unit
Watch Basic introduction to the analysis of complex survey data
Watch Specifying the design of your survey data
Watch Specifying the poststratification of survey data
Features
Poststratification
Design effects
Misspecification effects
Effects for linear combinations
Coefficient of variation
Estimate linear/nonlinear combinations of parameters
Hypotheses tests for survey data
Estimation with linear constraints
Goodness of fit for logistic and probit estimators
Multiple imputation
Maximum pseudolikelihood estimation
User-defined likelihoods
Survey characteristics automatically handled
Summary statistics
Population and subpopulation means
Population and subpopulation standard deviations
Population and subpopulation proportions
Population and subpopulation ratios
Population and subpopulation totals
Provide full covariance estimates across subpopulations
Summary tables
Two-way contingency tables with tests of independence
One-way tables
Table describing the sampling design of survey data
Postestimation Selector
View and run all postestimation features for your command
Automatically updated as estimation commands are run
Watch Postestimation Selector.
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
Works with multiple outcomes simultaneously
Contrasts of margins
Pairwise comparisons of margins
Profile plots
Interaction plots
Graphs of margins and marginal effects
Interactions of categorical variables
A single continuous variable
A single categorical variable