The SURVEYSELECT Procedure

Overview

The SURVEYSELECT procedure provides a variety of methods for selecting probability-based random samples. The procedure can select a simple random sample or a sample according to a complex multistage sample design that includes stratification, clustering, and unequal probabilities of selection. With probability sampling, each unit in the survey population has a known, positive probability of selection. This property of probability sampling avoids selection bias and enables you to use statistical theory to make valid inferences from the sample to the survey population.

To select a sample with PROC SURVEYSELECT, you input a SAS data set that contains the sampling frame, or list of units from which the sample is to be selected. You also specify the selection method, the desired sample size or sampling rate, and other selection parameters. The SURVEYSELECT procedure selects the sample, producing an output data set that contains the selected units, their selection probabilities, and sampling weights. When you are selecting a sample in multiple stages, you invoke the procedure separately for each stage of selection, inputting the frame and selection parameters for each current stage.

The SURVEYSELECT procedure provides methods for both equal probability sampling and probability proportional to size (PPS) sampling. In equal probability sampling, each unit in the sampling frame, or in a stratum, has the same probability of being selected for the sample. In PPS sampling, a unit's selection probability is proportional to its size measure. For details on probability sampling methods, refer to Kish (1987, 1965), Kalton (1983), and Cochran (1977).

The SURVEYSELECT procedure provides the following equal probability sampling methods:

simple random sampling
unrestricted random sampling (with replacement)
systematic random sampling
sequential random sampling

This procedure also provides the following probability proportional to size (PPS) methods:

PPS sampling without replacement
PPS sampling with replacement
PPS systematic sampling
PPS algorithms for selecting two units per stratum
sequential PPS sampling with minimum replacement

The procedure uses fast, efficient algorithms for these sample selection methods. Thus, it performs well even for very large input data sets or sampling frames, which may occur in practice for large-scale sample surveys.

The SURVEYSELECT procedure can perform stratified sampling, selecting samples independently within the specified strata, or nonoverlapping subgroups of the survey population. Stratification controls the distribution of the sample size in the strata. It is widely used in practice towards meeting a variety of survey objectives. For example, with stratification you can ensure adequate sample sizes for subgroups of interest, including small subgroups, or you can use stratification towards improving the precision of the overall estimates. When you are using a systematic or sequential selection method, the SURVEYSELECT procedure also can sort by control variables within strata for the additional control of implicit stratification.

The SURVEYSELECT procedure provides replicated sampling, where the total sample is composed of a set of replicates, each selected in the same way. You can use replicated sampling to study variable nonsampling errors, such as variability in the results obtained by different interviewers. You can also use replication to compute standard errors for the combined sample estimates.

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