Survey commands in STATA
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1 Survey commands in STATA Carlo Azzarri DECRG
2 Sample survey: Albania 2005 LSMS 4 strata (Central, Coastal, Mountain, Tirana) 455 Primary Sampling Units (PSU) 8 HHs by PSU * 455 = 3,640 HHs
3 svy command: general syntax. svyset PSU [pw=popw], str(stratum) pweight: popw VCE: linearized Single unit: missing Strata 1: stratum SU 1: PSU FPC 1: <zero>.svy supports many estimation commands: mean, proportion, ratio, total cnreg, cnsreg, glm, intreg, nl, ols, tobit, treatreg, truncreg stcox, streg probit, logit, biprobit, cloglog, clogit, mlogit, mprobit, oloig oprobit, slogit nbreg, poisson, zip, zinp. Ivols, ivprobit, ivtobit. Heckman, heckprob
4 Examples average proportion model estimation
5 average. mean TOTPCCONS [pw=popw], over(urban) Mean estimation Number of obs = 3638 Urban: urban = Urban Rural: urban = Rural Over Mean Std. Err. [95% Conf. Interval] TOTPCCONS Urban Rural
6 svy command: average. svy: mean TOTPCCONS, over(urban) (running mean on estimation sample) Survey: Mean estimation Number of strata = 4 Number of obs = 3638 Number of PSUs = 455 Population size = Design df = 451 Urban: urban = Urban Rural: urban = Rural Linearized Over Mean Std. Err. [95% Conf. Interval] TOTPCCONS Urban Rural
7 svy command: average (DEFF). estat effects, deff srssubpop (in Stata 10 ) Urban: urban = Urban Rural: urban = Rural Linearized Over Mean Std. Err. DEFF TOTPCCONS Urban Rural This value means that the sample variance is 2.8 times bigger than it would be if the survey were based on the same sample size but selected randomly
8 differences? mean is the same std. error is higher C.I. widens urban C.I. 11,696-12,493 (w/out sampling design) 11,511-12,678 (w/ sampling design) statistical difference between groups less likely because of overlap (not in this case) design effect
9 average (test). ttest TOTPCCONS [aw=popw], by(urban). reg TOTPCCONS urban [aw=popw] (sum of wgt is e+06) Source SS df MS Number of obs = F( 1, 3636) = Model e e+10 Prob > F = Residual e R-squared = Adj R-squared = Total e Root MSE = TOTPCCONS Coef. Std. Err. t P> t [95% Conf. Interval] urban _cons
10 svy command: average (test). svy: mean TOTPCCONS, over(urban). lincom [TOTPCCONS]Urban-[TOTPCCONS]Rural ( 1) [TOTPCCONS]Urban - [TOTPCCONS]Rural = 0 Coef. Std. Err. t P> t [95% Conf. Interval] (1) ,934 = 12,094 (urban) - 8,160 (rural)
11 model estimation (w/ dummy). svy: reg TOTPCCONS urban Survey: Linear regression Number of strata = 4 Number of obs = 3638 Number of PSUs = 455 Population size = Design df = 451 F( 1, 451) = Prob > F = R-squared = Linearized TOTPCCONS Coef. Std. Err. t P> t [95% Conf. Interval] urban _cons
12 proportion. proportion poor [pw=popw], over(urban) Proportion estimation Number of obs = 3638 no: poor = no yes: poor = yes Urban: urban = Urban Rural: urban = Rural Over Proportion Std. Err. [95% Conf. Interval] no Urban Rural yes Urban Rural
13 svy command: proportion. svy: mean poor, over(urban) (running mean on estimation sample) Survey: Mean estimation Number of strata = 4 Number of obs = 3638 Number of PSUs = 455 Population size = Design df = 451 Urban: urban = Urban Rural: urban = Rural Linearized Over Mean Std. Err. [95% Conf. Interval] poor Urban Rural
14 svy command: proportion (DEFF). estat effects, deff srssubpop (in Stata 10 ) Urban: urban = Urban Rural: urban = Rural Linearized Over Mean Std. Err. DEFF poor Urban Rural Only 1/2.35 as many observations would be needed to measure the urban PHC if a simple random sample were used (instead of the cluster sample with the design effect of 2.35)
15 proportion (test). ttest poor [aw=popw], by(urban). reg poor urban [aw=popw] (sum of wgt is e+06) Source SS df MS Number of obs = F( 1, 3636) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = poor Coef. Std. Err. t P> t [95% Conf. Interval] urban _cons
16 svy command: proportion (test). svy: mean poor, over(urban). lincom [poor]urban-[poor]rural ( 1) [poor]urban - [poor]rural = 0 Coef. Std. Err. t P> t [95% Conf. Interval] (1)
17 model estimation (actual sample). reg TOTPCCONS TOTPCINCOME [pw=popw] Robust TOTPCCONS Coef. Std. Err. t P> t [95% Conf. Interval] TOTPCINCOME _cons svy: reg TOTPCCONS TOTPCINCOME (running regress on estimation sample) Linearized TOTPCCONS Coef. Std. Err. t P> t [95% Conf. Interval] TOTPCINCOME _cons
18 model estimation (actual sample) Standard Errors w/ actual sample S.E. of the prediction TOTPCINCOME
19 model estimation (4 times actual sample) Standard Errors w/ 4 times the actual sample S.E. of the prediction TOTPCINCOME Two-stage stratified SRS
20 model estimation (4 times actual sample). reg TOTPCCONS TOTPCINCOME [pw=popw] Robust TOTPCCONS Coef. Std. Err. t P> t [95% Conf.Interval] TOTPCINCOME _cons svy: reg TOTPCCONS TOTPCINCOME (running regress on estimation sample) Linearized TOTPCCONS Coef. Std. Err. t P> t [95% Conf. Interval] TOTPCINCOME _cons
21 Main message Respondents in the same cluster are likely to be somewhat similar to one another. As a result, in a clustered sample selecting an additional member from the same cluster adds less new information than would a completely independent selection (Health Survey for England: The Health of Young People '95 97) Statistics and parameters do not differ (as long as weights are used), but standard errors do, so always take sampling design into account, otherwise inaccurate/wrong inference
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