The SURVEYSELECT procedure provides a variety of methods for selecting probability-based random samples. The procedure can select a simple random sample or can 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, which is the list of units from which the sample is to be selected. The sampling units can be individual observations or groups of observations (clusters). You also specify the selection method, the desired sample size or sampling rate, and other selection parameters. PROC SURVEYSELECT selects the sample and produces an output data set that contains the selected units, their selection probabilities, and their sampling weights. When you select 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.
PROC SURVEYSELECT 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 unitâ€™s selection probability is proportional to its size measure. For information about probability sampling methods, see Lohr (2010), Kish (1965), Kish (1987), Kalton (1983), and Cochran (1977).
PROC SURVEYSELECT provides the following equal probability sampling methods:
simple random sampling (without replacement)
unrestricted random sampling (with replacement)
systematic random sampling
sequential random sampling
Bernoulli sampling
This procedure also provides Poisson sampling and the following probability proportional to size (PPS) sampling 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 large input data sets or sampling frames.
PROC SURVEYSELECT can perform stratified sampling by selecting samples independently within strata, which are nonoverlapping subgroups of the survey population. Stratification controls the distribution of the sample size in the strata. It is widely used in practice toward 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 toward improving the precision of the overall estimates. When you use a systematic or sequential selection method, PROC SURVEYSELECT can also sort by control variables within strata for the additional control of implicit stratification.
For stratified sampling, PROC SURVEYSELECT provides survey design methods to allocate the total sample size among the strata. Available allocation methods include proportional, Neyman, and optimal allocation. Optimal allocation maximizes the estimation precision within the available resources, taking into account stratum sizes, costs, and variances.
PROC SURVEYSELECT provides replicated sampling, where the total sample is composed of a set of replicates, and each replicate is 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 estimate standard errors for combined sample estimates and to perform a variety of other resampling and simulation tasks.