Survey methods

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Survey methods

Whether your data require simple weighted adjustment because of differential sampling rates or you have data from a complex multistage survey, Stata's survey features can provide you with correct standard errors and confidence intervals for your inferences. All you need to do is specify the relevant characteristics of your sampling design, including sampling weights (including weights at multiple stages), clustering (at one, two, or more stages), stratification, and poststratification. After that, most of Stata's estimation commands can adjust their estimates to correct for your sampling design.

Survey regression models

See multilevel models with survey data

Watch Multilevel models for survey data in Stata.

See generalized SEM models for 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
Weight calibration via the raking-ratio method
Weight calibration via the generalized regression (GREG) method
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 Updated

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

Watch Introduction to Factor Variables in Stata tutorials

The basics
Interactions
More interactions

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

Watch Profile plots and interaction plots in Stata tutorials

Interactions of categorical and continuous variables
Interactions of categorical variables
A single continuous variable
A single categorical variable

Additional resources

Survey Data Reference Manual
Survey Weights: A Step-by-Step Guide to Calculation
Survey data analysis using Stata training course
Survey data flyer

See New in Stata 19 to learn about what was added in Stata 19.

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Survey methods

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