1st Edition
SAS® Software Companion for Sampling Design and Analysis, Third Edition
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
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
Poststratification
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
Imputation
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
Jackknife
Bootstrap
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
Biography
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.
