SAS® Software Companion for Sampling : Design and Analysis, Third Edition book cover
1st Edition

SAS® Software Companion for Sampling
Design and Analysis, Third Edition

ISBN 9780367748517
Published November 30, 2021 by Chapman and Hall/CRC
247 Pages 4 B/W Illustrations

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Book Description

The SAS® Software Companion for Sampling: Design and Analysis, designed to be read alongside Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (SDA; 2022, CRC Press), shows how to use the survey selection and analysis procedures of SAS® software to perform calculations for the examples in SDA.

No prior experience with SAS software is needed. Chapter 1 tells you how to access the software, introduces basic features, and helps you get started with analyzing data.

Each subsequent chapter provides step-by-step guidance for working through the data examples in the corresponding chapter of SDA, with code, output, and interpretation. Tips and warnings help you develop good programming practices and avoid common survey data analysis errors.

Features of the SAS software procedures are introduced as they are needed so you can see how each type of sample is selected and analyzed. Each chapter builds on the knowledge developed earlier for simpler designs; after finishing the book, you will know how to use SAS software to select and analyze almost any type of probability sample.

All code is available on the book website and is easily adapted for your own survey data analyses. The website also contains all data sets from the examples and exercises in SDA to help you develop your skills through analyzing survey data from social and public opinion research, public health, crime, education, business, agriculture, and ecology

Table of Contents

1. Getting Started

Windows in SAS Software

Reading Data

Saving Output

Saving Data Sets

Missing Data

Summary, Tips, and Warnings

2. Simple Random Sampling

Selecting a Simple Random Sample

Computing Statistics from an SRS

Estimating Proportions from an SRS

Additional Code for Exercises

Summary, Tips, and Warnings

3. Stratified Sampling

Selecting a Stratified Random Sample

Allocation Methods

Additional Helpful Options for Selecting Stratified Samples

Drawing a Stratified Sample Without a Population Listing

Computing Statistics from a Stratified Random Sample

Estimating Proportions from a Stratified Random Sample

Additional Code for Exercises

Summary, Tips, and Warnings

4. Ratio and Regression Estimation

Ratio Estimation

Regression Estimation

Domain Estimation


Ratio Estimation with Stratified Sampling

Model-Based Ratio and Regression Estimation

Summary, Tips, and Warnings

5. Cluster Sampling with Equal Probabilities

Estimating Means and Totals from a Cluster Sample

One-Stage Cluster Sampling

Multi-Stage Cluster Sampling

Estimating Proportions from a Cluster Sample

Model-Based Design and Analysis for Cluster Samples

Additional Code for Exercises

Summary, Tips, and Warnings

6. Sampling with Unequal Probabilities

Selecting a Sample with Unequal Probabilities

Sampling with Replacement

Sampling without Replacement

Selecting a Two-stage Cluster Sample

Computing Estimates from an Unequal-Probability Sample

Estimates from with-Replacement Samples

Estimates from without-Replacement Samples

Summary, Tips, and Warnings

7. Complex Surveys

Selecting a Stratified Multistage Sample

Estimating Quantiles

Computing Estimates from Stratified Multistage Samples

Univariate Plots from Complex Surveys

Scatterplots from Complex Surveys

Additional Code for Exercises

Summary, Tips, and Warnings

8. Nonresponse

How the Survey Analysis Procedures Treat Missing Data

Poststratification and Weighting Class Adjustments


Summary, Tips, and Warnings

9. Variance Estimation in Complex Surveys

Linearization (Taylor Series) Methods

Replicate Samples and Random Groups

Constructing Replicate Weights

Balanced Repeated Replication



Replicate Weights and Nonresponse Adjustments

Computing Estimates with Replicate Weights

Domain Estimates with Replicate Weights

Variance Estimation for Quantiles

Summary, Tips, and Warnings

10. Categorical Data Analysis in Complex Surveys

Contingency Tables and Odds Ratios

Chi-Square Tests

Loglinear Models

Summary, Tips, and Warnings

11. Regression with Complex Survey Data

Straight Line Regression in an SRS

Linear Regression for Complex Survey Data

Straight Line Regression

Using Regression to Compare Domain Means

Logistic Regression

Logistic Regression in a Simple Random Sample

Logistic Regression in a Complex Survey

Additional Resources and Code for Exercises

Summary, Tips, and Warnings

12. Additional Topics for Survey Data Analysis

Two-Phase Sampling

Estimating the Size of a Population

Ratio Estimation of Population Size

Loglinear Models with Multiple Lists

Small Area Estimation

Evolving Capabilities of SAS Software

A Data Set Descriptions

B Jackknife Macros

B Using Replicate Weights with Non-Survey Procedures

B Jackknife for Two-Phase Sampling

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Sharon L. Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute, and has received the Gertrude M. Cox, Morris Hansen, and Deming Awards. Formerly Dean’s Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a statistical consultant and writer.