In this example, an Internet service provider conducts 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.
The following PROC PRINT statements display the first 10 observations of the data set Customers and produce Figure 91.1:
title1 'Customer Satisfaction Survey'; title2 'First 10 Observations'; proc print data=Customers(obs=10); run;
|Customer Satisfaction Survey|
|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 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.