The SURVEYSELECT Procedure

Getting Started

In this example, an Internet service provider wants to conduct a customer satisfaction survey. The survey population consists of the company's current subscribers. The company plans to select a sample of customers from this population, interview the selected customers, and then make inferences about the entire survey population from the sample data.

The SAS data set Customers contains the sampling frame, which is the list of units in the survey population. The sample of customers will be selected from this sampling frame. The data set Customers is constructed from the company's customer database. It contains one observation for each customer, with a total of 13,471 observations. Figure 63.1 displays the first ten observations of the data set Customers.

Internet Service Provider Customers

(First 10 Observations)

Obs	CustomerID	State	Type	Usage
1	416-87-4322	AL	New	839
2	288-13-9763	GA	Old	224
3	339-00-8654	GA	Old	2451
4	118-98-0542	GA	New	349
5	421-67-0342	FL	New	562
6	623-18-9201	SC	New	68
7	324-55-0324	FL	Old	137
8	832-90-2397	AL	Old	1563
9	586-45-0178	GA	New	615
10	801-24-5317	SC	New	728

Figure 63.1: Customers Data Set (First 10 Observations)

In the SAS data set Customers, the variable CustomerID uniquely identifies each customer. The variable State contains the state of the customer's address. The company has customers in the following four states: Georgia (GA), Alabama (AL), Florida (FL), and South Carolina (SC). The variable Type equals `Old' if the customer has subscribed to the service for more than one year; otherwise, the variable Type equals `New'. The variable Usage contains the customer's average monthly service usage, in minutes.

The following sections illustrate the use of PROC SURVEYSELECT for probability sampling with three different designs for the customer satisfaction survey. All three designs are one stage, with customers as the sampling units. The first design is simple random sampling without stratification. In the second design, customers are stratified by state and type, and the sample is selected by simple random sampling within strata. In the third design, customers are sorted within strata by usage, and the sample is selected by systematic random sampling within strata.

Getting Started

Simple Random Sampling

Stratified Sampling

Stratified Sampling with Control Sorting