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