Sampling (2^nd Ed.)
Design and Analysis
Chapman & Hall/CRC Texts in Statistical Science Series

What is the unemployment rate? How many adults have high blood pressure? What is the total area of land planted with soybeans? Sampling: Design and Analysis tells you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches sampling using real data sets from social sciences, public opinion research, medicine, public health, economics, agriculture, ecology, and other fields.

The book is accessible to students from a wide range of statistical backgrounds. By appropriate choice of sections, it can be used for a graduate class for statistics students or for a class with students from business, sociology, psychology, or biology. Readers should be familiar with concepts from an introductory statistics class including linear regression; optional sections contain the statistical theory, for readers who have studied mathematical statistics.

Distinctive features include:

• More than 450 exercises. In each chapter, Introductory Exercises develop skills, Working with Data Exercises give practice with data from surveys, Working with Theory Exercises allow students to investigate statistical properties of estimators, and Projects and Activities Exercises integrate concepts. A solutions manual is available.
• An emphasis on survey design.
• Coverage of simple random, stratified, and cluster sampling; ratio estimation; constructing survey weights; jackknife and bootstrap; nonresponse; chi-squared tests and regression analysis.
• Graphing data from surveys.
• Computer code using SAS® software.
• Online supplements containing data sets, computer programs, and additional material.

Sharon 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 has been recognized as Fellow of the American Statistical Association, elected member of the International Statistical Institute, and recipient of the Gertrude M. Cox Statistics Award and the Deming Lecturer Award. Formerly Dean?s Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a freelance statistical consultant and writer. Visit her website at www.sharonlohr.com.

This edition is a reprint of the second edition published by Cengage Learning, Inc. Reprinted with permission.

1. Introduction

A Sample Controversy

Requirements of a Good Sample

Selection Bias

Measurement Error

Questionnaire Design

Sampling and Nonsampling Errors

Exercises

2. Simple Probability Samples

Types of Probability Samples

Framework for Probability Sampling

Simple Random Sampling

Sampling Weights

Confidence Intervals

Sample Size Estimation

Systematic Sampling

Randomization Theory Results for Simple Random Sampling

A Prediction Approach for Simple Random Sampling

When Should a Simple Random Sample Be Used?

Chapter Summary

Exercises

3. Stratified Sampling

What Is Stratified Sampling?

Theory of Stratified Sampling

Sampling Weights in Stratified Random Sampling

Allocating Observations to Strata

Defining Strata

Model-Based Inference for Stratified Sampling

Quota Sampling

Chapter Summary

Exercises

4. Ratio and Regression Estimation

Ratio Estimation in a Simple Random Sample

Estimation in Domains

Regression Estimation in Simple Random Sampling

Poststratification

Ratio Estimation with Stratified Samples

Model-Based Theory for Ratio and Regression Estimation

Chapter Summary

Exercises

5. Cluster Sampling with Equal Probabilities

Notation for Cluster Sampling

One-Stage Cluster Sampling

Two-Stage Cluster Sampling

Designing a Cluster Sample

Systematic Sampling

Model-Based Inference in Cluster Sampling

Chapter Summary

Exercises

6. Sampling with Unequal Probabilities

Sampling One Primary Sampling Unit

One-Stage Sampling with Replacement

Two-Stage Sampling with Replacement

Unequal-Probability Sampling Without Replacement

Examples of Unequal-Probability Samples

Randomization Theory Results and Proofs

Models and Unequal-Probability Sampling

Chapter Summary

Exercises

7. Complex Surveys

Assembling Design Components

Sampling Weights

Estimating a Distribution Function

Plotting Data from a Complex Survey

Design Effects

The National Crime Victimization Survey

Sampling and Design of Experiments

Chapter Summary

Exercises

8. Nonresponse

Effects of Ignoring Nonresponse

Designing Surveys to Reduce Nonsampling Errors

Callbacks and Two-Phase Sampling

Mechanisms for Nonresponse

Weighting Methods for Nonresponse

Imputation

Parametric Models for Nonresponse

What Is an Acceptable Response Rate?

Chapter Summary

Exercises

9. Variance Estimation in Complex Surveys

Linearization (Taylor Series) Methods

Random Group Methods

Resampling and Replication Methods

Generalized Variance Functions

Confidence Intervals

Chapter Summary

Exercises

10. Categorical Data Analysis in Complex Surveys

Chi-Square Tests with Multinomial Sampling

Effects of Survey Design on Chi-Square Tests

Corrections to χ2 Tests

Loglinear Models

Chapter Summary

Exercises

11. Regression with Complex Survey Data

Model-Based Regression in Simple Random Samples

Regression in Complex Surveys

Using Regression to Compare Domain Means

Should Weights Be Used in Regression?

Mixed Models for Cluster Samples

Logistic Regression

Generalized Regression Estimation for Population Totals

Chapter Summary

Exercises

12. Two-Phase Sampling

Theory for Two-Phase Sampling

Two-Phase Sampling with Stratification

Ratio and Regression Estimation in Two-Phase Samples

Jackknife Variance Estimation for Two-Phase Sampling

Designing a Two-Phase Sample

Chapter Summary

Exercises

13. Estimating Population Size

Capture–Recapture Estimation

Multiple Recapture Estimation

Chapter Summary

Exercises

14. Rare Populations and Small Area Estimation

Sampling Rare Populations

Small Area Estimation

Chapter Summary

Exercises

15. Survey Quality

          Coverage Error

          Nonresponse Error

          Measurement Error

          Sensitive Questions

          Processing Error

          Total Survey Quality

          Chapter Summary

          Exercises

          Probability

          Random Variables and Expected Value

          Conditional Probability

          Conditional Expectation

Sharon 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 has been recognized as Fellow of the American Statistical Association, elected member of the International Statistical Institute, and recipient of the Gertrude M. Cox Statistics Award and the Deming Lecturer Award. Formerly Dean’s Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a freelance statistical consultant and writer. Visit her website at www.sharonlohr.com.