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How do I analyze survey data with a systematic sample design? | Stata FAQ

NOTE:  If you
want to see the design effect or the misspecification effect, use estat
effects
after the command.  To get the coefficient of variation, use estat cv.

This example is taken from Lehtonen and Pahkinen’s
Practical Methods for Design and Analysis of Complex Surveys, page 46 Table 2.6  Estimates from a systematic sample drawn from the
Province’91 population using implicit stratification.

input id str clu wt ue91 lab91
1 1 1 4 4123 33786
2 1 5 4 721 4930
3 2 9 4 194 2069
4 2 13 4 129 927
5 2 17 4 239 2144
6 2 21 4 61 573
7 2 25 4 262 1737
8 2 29 4 166 1615
end
gen fpc1 = 8/32
svyset clu [pweight=wt], strata(str) fpc(fpc1)
pweight: wt
VCE: linearized
Single unit: missing
Strata 1: str
SU 1: clu
FPC 1: fpc1

svy: total ue91
(running total on estimation sample)

Survey: Total estimation

Number of strata =       2        Number of obs   =          8
Number of PSUs   =       8        Population size =         32
                                  Design df       =          6

--------------------------------------------------------------
             |             Linearized
             |      Total   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
        ue91 |      23580   11801.49     -5297.209    52457.21
--------------------------------------------------------------

estat cv

------------------------------------------------
             |             Linearized
             |      Total   Std. Err.     CV (%)
-------------+----------------------------------
        ue91 |      23580   11801.49     50.0487
------------------------------------------------

estat effects

----------------------------------------------------------
             |             Linearized
             |      Total   Std. Err.       DEFF      DEFT
-------------+--------------------------------------------
        ue91 |      23580   11801.49     .758641   .754308
----------------------------------------------------------
Note: Weights must represent population totals for deff to
      be correct when using an FPC; however, deft is
      invariant to the scale of weights.


svy: ratio ue91 lab91
(running ratio on estimation sample)

Survey: Ratio estimation

Number of strata =       2        Number of obs   =          8
Number of PSUs   =       8        Population size =         32
                                  Design df       =          6

     _ratio_1: ue91/lab91

--------------------------------------------------------------
             |             Linearized
             |      Ratio   Std. Err.     [95% Conf. Interval]
-------------+------------------------------------------------
    _ratio_1 |   .1233754   .0033325      .1152211    .1315297
--------------------------------------------------------------

estat cv

     _ratio_1: ue91/lab91

------------------------------------------------
             |             Linearized
             |      Ratio   Std. Err.     CV (%)
-------------+----------------------------------
    _ratio_1 |   .1233754   .0033325     2.70109
------------------------------------------------

estat effects

     _ratio_1: ue91/lab91

----------------------------------------------------------
             |             Linearized
             |      Ratio   Std. Err.       DEFF      DEFT
-------------+--------------------------------------------
    _ratio_1 |   .1233754   .0033325     1.29364   .985004
----------------------------------------------------------
Note: Weights must represent population totals for deff to
      be correct when using an FPC; however, deft is
      invariant to the scale of weights.