This edition is a reprint of the second edition published by Cengage Learning, Inc. Reprinted with permission. 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.
Author(s): Sharon L. Lohr
Series: Texts In Statistical Science
Edition: 2
Publisher: CRC press/Chapman & Hall/Taylor & Francis Group
Year: 2019
Language: English
Pages: xi, 596
Tags: Sampling: Evaluation, Sampling (Statistics), Sampling (Statistics): Computer Programs
Cover
Series Page
Title Page
Copyright Page
Contents
Preface
CHAPTER 1 Introduction
1.1 A Sample Controversy
1.2 Requirements of a Good Sample
1.3 Selection Bias
1.4 Measurement Error
1.5 Questionnaire Design
1.6 Sampling and Nonsampling Errors
1.7 Exercises
CHAPTER 2 Simple Probability Samples
2.1 Types of Probability Samples
2.2 Framework for Probability Sampling
2.3 Simple Random Sampling
2.4 Sampling Weights
2.5 Confidence Intervals
2.6 Sample Size Estimation
2.7 Systematic Sampling
2.8 Randomization Theory Results for Simple Random Sampling
2.9 A Prediction Approach for Simple Random Sampling
2.10 When Should a Simple Random Sample Be Used?
2.11 Chapter Summary
2.12 Exercises
CHAPTER 3 Stratified Sampling
3.1 What Is Stratified Sampling?
3.2 Theory of Stratified Sampling
3.3 Sampling Weights in Stratified Random Sampling
3.4 Allocating Observations to Strata
3.5 Defining Strata
3.6 Model-Based Inference for Stratified Sampling
3.7 Quota Sampling
3.8 Chapter Summary
3.9 Exercises
CHAPTER 4 Ratio and Regression Estimation
4.1 Ratio Estimation in a Simple Random Sample
4.2 Estimation in Domains
4.3 Regression Estimation in Simple Random Sampling
4.4 Poststratification
4.5 Ratio Estimation with Stratified Samples
4.6 Model-Based Theory for Ratio and Regression Estimation
4.7 Chapter Summary
4.8 Exercises
CHAPTER 5 Cluster Sampling with Equal Probabilities
5.1 Notation for Cluster Sampling
5.2 One-Stage Cluster Sampling
5.3 Two-Stage Cluster Sampling
5.4 Designing a Cluster Sample
5.5 Systematic Sampling
5.6 Model-Based Inference in Cluster Sampling
5.7 Chapter Summary
5.8 Exercises
CHAPTER 6 Sampling with Unequal Probabilities
6.1 Sampling One Primary Sampling Unit
6.2 One-Stage Sampling with Replacement
6.3 Two-Stage Sampling with Replacement
6.4 Unequal-Probability Sampling Without Replacement
6.5 Examples of Unequal-Probability Samples
6.6 Randomization Theory Results and Proofs
6.7 Models and Unequal-Probability Sampling
6.8 Chapter Summary
6.9 Exercises
CHAPTER 7 Complex Surveys
7.1 Assembling Design Components
7.2 Sampling Weights
7.3 Estimating a Distribution Function
7.4 Plotting Data from a Complex Survey
7.5 Design Effects
7.6 The National Crime Victimization Survey
7.7 Sampling and Design of Experiments
7.8 Chapter Summary
7.9 Exercises
CHAPTER 8 Nonresponse
8.1 Effects of Ignoring Nonresponse
8.2 Designing Surveys to Reduce Nonsampling Errors
8.3 Callbacks and Two-Phase Sampling
8.4 Mechanisms for Nonresponse
8.5 Weighting Methods for Nonresponse
8.6 Imputation
8.7 Parametric Models for Nonresponse
8.8 What Is an Acceptable Response Rate?
8.9 Chapter Summary
8.10 Exercises
CHAPTER 9 Variance Estimation in Complex Surveys
9.1 Linearization (Taylor Series) Methods
9.2 Random Group Methods
9.3 Resampling and Replication Methods
9.4 Generalized Variance Functions
9.5 Confidence Intervals
9.6 Chapter Summary
9.7 Exercises
CHAPTER 10 Categorical Data Analysis in Complex Surveys
10.1 Chi-Square Tests with Multinomial Sampling
10.2 Effects of Survey Design on Chi-Square Tests
10.3 Corrections to χ[sup(2)] Tests
10.4 Loglinear Models
10.5 Chapter Summary
10.6 Exercises
CHAPTER 11 Regression with Complex Survey Data
11.1 Model-Based Regression in Simple Random Samples
11.2 Regression in Complex Surveys
11.3 Using Regression to Compare Domain Means
11.4 Should Weights Be Used in Regression?
11.5 Mixed Models for Cluster Samples
11.6 Logistic Regression
11.7 Generalized Regression Estimation for Population Totals
11.8 Chapter Summary
11.9 Exercises
CHAPTER 12 Two-Phase Sampling
12.1 Theory for Two-Phase Sampling
12.2 Two-Phase Sampling with Stratification
12.3 Ratio and Regression Estimation in Two-Phase Samples
12.4 Jackknife Variance Estimation for Two-Phase Sampling
12.5 Designing a Two-Phase Sample
12.6 Chapter Summary
12.7 Exercises
CHAPTER 13 Estimating Population Size
13.1 Capture–Recapture Estimation
13.2 Multiple Recapture Estimation
13.3 Chapter Summary
13.4 Exercises
CHAPTER 14 Rare Populations and Small Area Estimation
14.1 Sampling Rare Populations
14.2 Small Area Estimation
14.3 Chapter Summary
14.4 Exercises
CHAPTER 15 Survey Quality
15.1 Coverage Error
15.2 Nonresponse Error
15.3 Measurement Error
15.4 Sensitive Questions
15.5 Processing Error
15.6 Total Survey Quality
15.7 Chapter Summary
15.8 Exercises
APPENDIX A: Probability Concepts Used in Sampling
A.1 Probability
A.2 Random Variables and Expected Value
A.3 Conditional Probability
A.4 Conditional Expectation
References
Author Index
Subject Index
