There is no svy: ttest command in Stata; however, svy: mean is an estimation
command and allows for the use of both the test and lincom post-estimation
commands. It is also easy to do a t-test using the svy: regress command.
We will show each of these three ways of conducting a t-test with survey data
below.

We will illustrate this using the hsb2 dataset
pretending that the variable socst is the sampling weight (pweight) and that the sample is
stratified on ses. Let’s say that we wish to do a t-test for write by
gender.
In our dataset, the variable female is coded 1 for females and 0 for
males.

use https://stats.idre.ucla.edu/stat/stata/notes/hsb2, clear

svyset [pw=socst], strata(ses)

      pweight: socst
          VCE: linearized
     Strata 1: ses
         SU 1: 
        FPC 1:

Method 1: Using the test command

First, we use the svy: mean command with the over option to get
the means for each gender.  Next, we use the test command to test
the null hypothesis that these two means are equal.

svy: mean write, over(female)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       3          Number of obs    =     200
Number of PSUs   =     200          Population size  =   10481
                                    Design df        =     197

         male: female = male
       female: female = female

--------------------------------------------------------------
             |             Linearized
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
write        |
        male |   51.65351   1.041066      49.60045    53.70658
      female |   55.81467    .721354       54.3921    57.23723
--------------------------------------------------------------

To use the test command, we need to know the labels that Stata has assigned to the values in the output. We can see these labels by using the coeflegend option on the svy: mean command.

svy: mean write, over(female) coeflegend
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       3        Number of obs   =        200
Number of PSUs   =     200        Population size =     10,481
                                  Design df       =        197

--------------------------------------------------------------------------------
               |       Mean  Legend
---------------+----------------------------------------------------------------
c.write@female |
         male  |   51.65351  _b[[email protected]]
       female  |   55.81467  _b[[email protected]]
--------------------------------------------------------------------------------

Now that we know what the labels are, we can use them in the test command.

test _b[[email protected]] = _b[[email protected]]

Adjusted Wald test

 ( 1)  [email protected] - [email protected] = 0

       F(  1,   197) =   10.45
            Prob > F =    0.0014

Method 2: Using the lincom command

We could also use the lincom command to test the two means. This command should be run after the svy: means command shown above. The lincom command gives us the difference between the means (51.65351 – 55.81467 = -4.161156), the standard error of the difference, as well as the t-value and the p-value. Notice that the p-value is the same as above, and that squaring the t-value yields the F-value shown above ( (-3.23)^2 = 10.45).

svy: mean write, over(female)
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       3          Number of obs    =     200
Number of PSUs   =     200          Population size  =   10481
                                    Design df        =     197

         male: female = male
       female: female = female

--------------------------------------------------------------
             |             Linearized
        Over |       Mean   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
write        |
        male |   51.65351   1.041066      49.60045    53.70658
      female |   55.81467    .721354       54.3921    57.23723
--------------------------------------------------------------

To use the licom command, we need to know the labels that Stata has assigned to the values in the output. We can see these labels by using the coeflegend option on the svy: mean command.

svy: mean write, over(female) coeflegend
(running mean on estimation sample)

Survey: Mean estimation

Number of strata =       3        Number of obs   =        200
Number of PSUs   =     200        Population size =     10,481
                                  Design df       =        197

--------------------------------------------------------------------------------
               |       Mean  Legend
---------------+----------------------------------------------------------------
c.write@female |
         male  |   51.65351  _b[[email protected]]
       female  |   55.81467  _b[[email protected]]
--------------------------------------------------------------------------------

lincom _b[[email protected]] - _b[[email protected]]

 ( 1)  [email protected] - [email protected] = 0

------------------------------------------------------------------------------
        Mean |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         (1) |  -4.161156     1.2871    -3.23   0.001    -6.699419   -1.622892
------------------------------------------------------------------------------

* The precise value of the t statistic can be obtained from the list of values 
* stored by Stata after running the estimation command svy: mean.

return list

scalars:
                 r(df) =  197
                 r(ub) =  -1.622892488144128
                 r(lb) =  -6.699418642311276
                  r(p) =  .0014363375306614
                  r(t) =  -3.232969710887891
              r(level) =  95
                 r(se) =  1.287100077434656
           r(estimate) =  -4.161155565227702

display (-3.232969710887892)^2
10.452093

We can see from the output above that the means are not statistically
equivalent.

Method 3: Using the regress command

The svy: regress command can also be used to compute the t-test.
To do this, simply include the single dichotomous predictor variable.  The
coefficient for female is the t-test.  As you can see, you get the
same coefficient and p-value that we did when we used the lincom command.
The sign of the coefficient is different because above, the mean of the females
was subtracted from the mean of males.  Below, the mean of males was
subtracted from the mean of the females.

svy: regress write female

(running regress on estimation sample)

Survey: Linear regression

Number of strata   =         3                  Number of obs      =       200
Number of PSUs     =       200                  Population size    =     10481
                                                Design df          =       197
                                                F(   1,    197)    =     10.45
                                                Prob > F           =    0.0014
                                                R-squared          =    0.0519

------------------------------------------------------------------------------
             |             Linearized
       write |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      female |   4.161156     1.2871     3.23   0.001     1.622892    6.699419
       _cons |   51.65351   1.041066    49.62   0.000     49.60045    53.70658
------------------------------------------------------------------------------

We can use the test command after the svy: regress if we would like to get the F-ratio.

test female

Adjusted Wald test

 ( 1)  female = 0

       F(  1,   197) =   10.45
            Prob > F =    0.0014

Regardless of the method that we use, we obtain an F-ratio of 10.45 or a t-value
of 3.23 with a p-value of 0.0014.

Note: This FAQ was inspired by several responses to a question on the Statalist.