# The SURVEYSELECT Procedure

## Getting Started: SURVEYSELECT Procedure

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 95.1:

title1 'Customer Satisfaction Survey';
title2 'First 10 Observations';
proc print data=Customers(obs=10);
run;

Figure 95.1: Customers Data Set (First 10 Observations)

 Customer Satisfaction Survey 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

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.