1st Edition

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

By Sharon L. Lohr Copyright 2022
    248 Pages 4 B/W Illustrations
    by Chapman & Hall

    248 Pages 4 B/W Illustrations
    by Chapman & Hall

    247 Pages 4 B/W Illustrations
    by Chapman & Hall

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    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


    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


    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.