New SAS Procedures for Analysis of Sample Survey Data
20. April 2007 08:28The SURVEYSELECT procedure provides a variety of methods for selecting probability-based random samples.
PROC SURVEYMEANS and PROC SURVEYREG analyze survey data collected according to a complex survey design.
SURVEYSELECT
The SURVEYSELECT procedure provides the following equal probability sampling methods:
This procedure also provides the following probability proportional to size (PPS) methods:
Example:
proc surveyselect data=frame out=sample
method=srs n=(3, 5, 3, 6, 2);
strata state region;
run;
The METHOD=SRS option specifies that simple random sampling is to be used for sample selection.In simple random sampling, units are selected with equal probability and without replacement. The N = (3, 5, 3, 6, 2) option specifies the sample sizes for the strata — a sample of 3 households from the stratum, 5 households from the second stratum and so on. The OUT=SAMPLE option names the output data set that contains the selected sample. The STRATA statement identifies STATE and REGION as the stratification variables. The input data set FRAME is sorted by these stratification variables.
SURVEYMEANS
The SURVEYMEANS procedure can compute the following statistics:Example:
proc surveymeans data=HHSample N=StrataTotals sum df clm fraction; var income expense; strata state region / list; weight weight; run;
SURVEYREG
The SURVEYREG procedure performs regression analysis for sample survey data. The procedure can handle complex survey sample designs, including designs with stratification, clustering, and unequal weighting. The procedure fits linear models for survey data and computes regression coefficients and their variance-covariance matrix. The procedure also provides significance tests for the model effects and for any specified estimable linear functions of the model parameters. Using the regression model, the procedure can compute predicted values for the sample survey data.
Example:
proc surveyreg data=HHSample N=StrataTotals;
strata state region / list;
model expense = income;
weight weight;
run;
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