Survey data analysis using SAS

Notes on analyzing survey data using SAS

Work a lot on the survey data this week. It turns out that when using statistical package for survey data, it is critical to include all the three variables: clusters (PSU), strata, and weights. But the difference is smaller if without the cluster and strata information.

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For example,
These two procedures in SAS (both w/o weights) will produce the same contingency table (frequency count) and thus same odds ratio (since it is calculated from frequency). The confidence intervals around OR are slightly different.

proc freq data = new;
table PCAbove5 * qn26_Sad  /relrisk chisq nopercent ; 
run;

proc surveyfreq data = new;
table PCAbove5 * qn26_Sad  /relrisk chisq nopercent ; 
strata stratum;
cluster PSU;
run;

If weights are added to both procedures, we can get the same weighted frequency and OR. The OR is different from the unweighted OR. The confidence interval around OR gets wider.

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The difference between using strata and cluster and not using is the in design correction, F-value, and confidence interval.
For example:

proc surveyfreq data = new;
table PCAbove5 * qn26_Sad  /relrisk chisq nopercent ; 
weight weight;
run;

proc surveyfreq data = new;
where QN68_New eq 0;
table PCAbove5 * qn26_Sad  /relrisk chisq nopercent ; 
strata stratum;
cluster PSU;
weight weight;

run;