Chapter 10: Introduction to Survey Procedures


Overview

This chapter introduces the SAS/STAT procedures for survey sampling and describes how you can use these procedures to analyze survey data.

Researchers often use sample survey methodology to obtain information about a large population by selecting and measuring a sample from that population. Due to variability among items, researchers apply scientific probability-based designs to select the sample. This reduces the risk of a distorted view of the population and allows statistically valid inferences to be made from the sample. Refer to Lohr (1999), Kalton (1983), Cochran (1977), and Kish (1965) for more information on statistical sampling and analysis of complex survey data. To select probability-based random samples from a study population, you can use the SURVEYSELECT procedure, which provides a variety of methods for probability sampling. To analyze sample survey data, you can use the SURVEYMEANS, SURVEYFREQ, SURVEYREG, and SURVEYLOGISTIC procedures, which incorporate the sample design into the analyses.

Many SAS/STAT procedures, such as the MEANS, FREQ, GLM and LOGISTIC procedures, can compute sample means, produce crosstabulation tables, and estimate regression relationships. However, in most of these procedures, statistical inference is based on the assumption that the sample is drawn from an infinite population by simple random sampling. If the sample is in fact selected from a finite population using a complex survey design, these procedures generally do not calculate the estimates and their variances according to the design actually used. Using analyses that are not appropriate for your sample design can lead to incorrect statistical inferences.

The SURVEYMEANS, SURVEYFREQ, SURVEYREG, and SURVEYLOGISTIC procedures do properly analyze complex survey data, taking into account the sample design. These procedures can be used for multistage designs or for single-stage designs, with or without stratification, and with or without unequal weighting . The procedures use the Taylor expansion method to estimate sampling errors of estimators based on complex sample designs. This method is appropriate for all designs where the first-stage sample is selected with replacement, or where the first-stage sampling fraction is small, as it often is in practice.

The following table briefly describes the sampling and analysis procedures in SAS/STAT software.

Table 10.1: Sampling and Analysis Procedures in SAS/STAT Software

SURVEYSELECT

Sampling Methods

simple random sampling unrestricted random sampling (with replacement)

systematic

sequential

selection probability proportional to size (PPS) with and without replacement

PPS systematic

PPS for two units per stratum

sequential PPS with minimum replacement

SURVEYMEANS

Statistics

estimates of population means and totals

estimates of population proportions

standard errors

confidence limits

hypothesis tests

domain analyses

ratio estimates

SURVEYFREQ

Analyses

one-way frequency tables

two-way and multiway crosstabulation tables

estimates of population totals and proportions

standard errors

confidence limits

tests of goodness-of-fit

tests of independence

SURVEYREG

Analyses

linear regression model fitting

regression coefficients

covariance matrices

hypothesis tests

confidence limits

estimable functions

contrasts

SURVEYLOGISTIC

Analyses

cumulative logit regression model fitting

logit, complementary log-log, and probit link functions

generalized logit regression model fitting

regression coefficients

covariance matrices

hypothesis tests

model diagnostics

odds ratios

confidence limits

estimable functions

contrasts




SAS.STAT 9.1 Users Guide (Vol. 1)
SAS/STAT 9.1 Users Guide, Volumes 1-7
ISBN: 1590472438
EAN: 2147483647
Year: 2004
Pages: 156

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