Sampling: Design And Analysis

Description

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. 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