Stata capabilities: Survey commands

Stata has a number of commands designed to handle the special requirements of complex survey data. The commands will handle any or all of the following survey-design features: probability sampling weights, stratification, multiple stages of cluster sampling, and poststratification. There are commands for estimating means, totals, ratios, and proportions; and commands for linear regression, logistic regression, probit models, and survey estimators for sampling designs; see the table below for a complete listing of svy commands.

Variance estimates are produced using one of the five variance estimation techniques: balanced repeated replication, the bootstrap, the jackknife, successive difference replication, and Taylor linearization.

The Stata estimation commands designed to handle the special requirements of complex survey data work with the svy prefix:

svy: biprobit	Bivariate probit regression for survey data	svy: ologit	Ordered logistic regression for survey data
svy: clogit	Conditional (fixed-effects) logistic regression for survey data	svy: oprobit	Ordered probit regression for survey data
svy: cloglog	Complementary log-log regression for survey data	svy: poisson	Poisson regression for survey data
svy: cnsreg	Constrained linear regression for survey data	svy: probit	Probit regression for survey data
svy: glm	Generalized linear models for survey data	svy: proportion	Estimate proportions for survey data
svy: gnbreg	Generalized negative binomial regression for survey data	svy: ratio	Estimate ratios for survey data
svy: heckman	Heckman selection model for survey data	svy: regress	Linear regression for survey data
svy: heckprob	Probit model with sample selection for survey data	svy: scobit	Skewed logistic regression for survey data
svy: hetprob	Heteroskedastic probit regression for survey data	svy: sem	Structural equation modeling for survey data
svy: intreg	Interval regression for survey data	svy: slogit	Stereotype logistic regression for survey data
svy: ivprobit	Probit model with endogenous regressors for survey data	svy: stcox	Cox proportional hazards model for survey data
svy: ivregress	Single-equation instrumental-variables regression for survey data	svy: streg	Parametric survival models for survey data
svy: ivtobit	Tobit model with endogenous regressors for survey data	svy: tnbreg	Truncated negative binomial regression for survey data
svy: logistic	Logistic regression for survey data, reporting odds ratios	svy: tobit	Tobit regression for survey data
svy: logit	Logistic regression for survey data, reporting coefficients	svy: total	Estimate totals for survey data
svy: mean	Estimate means for survey data	svy: tpoisson	Truncated Poisson regression for survey data
svy: mlogit	Multinomial (polytomous) logistic regression for survey data	svy: treatreg	Treatment-effects regression for survey data
svy: mprobit	Multinomial probit regression for survey data	svy: truncreg	Truncated regression for survey data
svy: nbreg	Negative binomial regression for survey data	svy: zinb	Zero-inflated negative binomial regression for survey data
svy: nl	Nonlinear least-squares estimation for survey data	svy: zip	Zero-inflated Poisson regression for survey data

Many other estimation commands in Stata also have features that make them suitable for certain limited survey designs. For example, Stata’s competing-risks regression routine (stcrreg) handles sampling weights properly when sampling weights are specified, and it also handles clustering.

estat effects computes the design effects DEFF and DEFT, as well as misspecification effects MEFF and MEFT. The test command, used after a svy estimation command, computes adjusted Wald tests and Bonferroni tests for linear hypotheses (single or joint).

The svyset command, illustrated above, allows you to set the variables that contain the sampling weights, strata, and any PSU identifiers at the outset. These variables are remembered for subsequent commands and do not have to be reentered.

Estimating the difference between two subpopulation means can be done by running svy: mean with a over() option to produce subpopulation estimates and then running the command lincom:

The svy: mean, svy: prop, svy: ratio, and svy: total commands produce estimates for multiple subpopulations:

Use estat size to report the number of observations belonging to each subpopulation and estimates of the subpopulation size.

You can fit linear regressions, logistic regressions, and probit models using svy estimators. Shown below is an example of svy: logit, which fits logistic regressions for survey data.

svy: logit can display estimates as coefficients or as odds ratios. Below we redisplay the previous model, requesting that the estimates be expressed as odds ratios.

After running a logistic regression, you can use lincom to compute odds ratios for any covariate group relative to another.

You can also fit linear regressions, logistic regressions, and probit models for a subpopulation:

Survey data require some special data management. The svydescribe command can be used to examine the design structure of the dataset. It can also be used to see the number of missing and nonmissing observations per stratum (or optionally per stage) for one or more variables.