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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 Deans 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.

Arvustused

"Lohr provides a vast amount of verbal explanation to guide the nonmathematical reader, yet she provides derivations of many results and a 24-page list of references. Citations are well integrated into the readingThe new edition has added a number of topics that are timely relative to current work in survey methodology. These include such topics as Internet surveys and resampling techniquesOne of the distinguishing characteristics of this book is the integration of model-based sampling as well as the traditional methods dating from the 1940s. The exposition is very well written and should be accessible to a wide audience. (this is an excellent text. The exposition is extraordinarily fine and the coverage broad and up-to-date." ~Journal of Biopharmaceutical Statistics



"This is a textbook for a graduate course in survey sampling as well as for those in applied fieldsLohr, in each chapter, introduces the basic concept to be covered, gives il and presents many exercises for students to work out. Her examples are not toy ones, most come from actual (often complex) surveys, and the exercises both advance the learning of new concepts and enable the methodology just learnedThis new and expanded second edition provides a wonderful introduction to sample survey methodology. In a clear fashion Lohr introduces basic concepts, while simultaneously covering a wide variety of topics." ~Journal of the American Statistical Association

Preface ix
Chapter 1 Introduction
1(24)
1.1 A Sample Controversy
1(2)
1.2 Requirements of a Good Sample
3(2)
1.3 Selection Bias
5(4)
1.4 Measurement Error
9(2)
1.5 Questionnaire Design
11(5)
1.6 Sampling and Nonsampling Errors
16(3)
1.7 Exercises
19(6)
Chapter 2 Simple Probability Samples
25(48)
2.1 Types of Probability Samples
25(3)
2.2 Framework for Probability Sampling
28(5)
2.3 Simple Random Sampling
33(6)
2.4 Sampling Weights
39(1)
2.5 Confidence Intervals
40(6)
2.6 Sample Size Estimation
46(4)
2.7 Systematic Sampling
50(1)
2.8 Randomization Theory Results for Simple Random Sampling
51(3)
2.9 A Prediction Approach for Simple Random Sampling
54(4)
2.10 When Should a Simple Random Sample Be Used?
58(1)
2.11
Chapter Summary
59(2)
2.12 Exercises
61(12)
Chapter 3 Stratified Sampling
73(44)
3.1 What Is Stratified Sampling?
73(4)
3.2 Theory of Stratified Sampling
77(5)
3.3 Sampling Weights in Stratified Random Sampling
82(3)
3.4 Allocating Observations to Strata
85(6)
3.5 Defining Strata
91(4)
3.6 Model-Based Inference for Stratified Sampling
95(1)
3.7 Quota Sampling
96(3)
3.8
Chapter Summary
99(2)
3.9 Exercises
101(16)
Chapter 4 Ratio and Regression Estimation
117(48)
4.1 Ratio Estimation in a Simple Random Sample
118(15)
4.2 Estimation in Domains
133(5)
4.3 Regression Estimation in Simple Random Sampling
138(4)
4.4 Poststratification
142(2)
4.5 Ratio Estimation with Stratified Samples
144(2)
4.6 Model-Based Theory for Ratio and Regression Estimation
146(8)
4.7
Chapter Summary
154(1)
4.8 Exercises
155(10)
Chapter 5 Cluster Sampling with Equal Probabilities
165(54)
5.1 Notation for Cluster Sampling
168(2)
5.2 One-Stage Cluster Sampling
170(12)
5.3 Two-Stage Cluster Sampling
182(9)
5.4 Designing a Cluster Sample
191(5)
5.5 Systematic Sampling
196(4)
5.6 Model-Based Inference in Cluster Sampling
200(5)
5.7
Chapter Summary
205(2)
5.8 Exercises
207(12)
Chapter 6 Sampling with Unequal Probabilities
219(62)
6.1 Sampling One Primary Sampling Unit
221(4)
6.2 One-Stage Sampling with Replacement
225(10)
6.3 Two-Stage Sampling with Replacement
235(3)
6.4 Unequal-Probability Sampling Without Replacement
238(11)
6.5 Examples of Unequal-Probability Samples
249(5)
6.6 Randomization Theory Results and Proofs
254(8)
6.7 Models and Unequal-Probability Sampling
262(3)
6.8
Chapter Summary
265(2)
6.9 Exercises
267(14)
Chapter 7 Complex Surveys
281(48)
7.1 Assembling Design Components
281(4)
7.2 Sampling Weights
285(3)
7.3 Estimating a Distribution Function
288(6)
7.4 Plotting Data from a Complex Survey
294(15)
7.5 Design Effects
309(3)
7.6 The National Crime Victimization Survey
312(5)
7.7 Sampling and Design of Experiments
317(2)
7.8
Chapter Summary
319(1)
7.9 Exercises
320(9)
Chapter 8 Nonresponse
329(36)
8.1 Effects of Ignoring Nonresponse
330(2)
8.2 Designing Surveys to Reduce Nonsampling Errors
332(4)
8.3 Callbacks and Two-Phase Sampling
336(2)
8.4 Mechanisms for Nonresponse
338(2)
8.5 Weighting Methods for Nonresponse
340(6)
8.6 Imputation
346(5)
8.7 Parametric Models for Nonresponse
351(3)
8.8 What Is an Acceptable Response Rate?
354(2)
8.9
Chapter Summary
356(1)
8.10 Exercises
357(8)
Chapter 9 Variance Estimation in Complex Surveys
365(36)
9.1 Linearization (Taylor Series) Methods
366(4)
9.2 Random Group Methods
370(3)
9.3 Resampling and Replication Methods
373(13)
9.4 Generalized Variance Functions
386(2)
9.5 Confidence Intervals
388(4)
9.6
Chapter Summary
392(2)
9.7 Exercises
394(7)
Chapter 10 Categorical Data Analysis in Complex Surveys
401(28)
10.1 Chi-Square Tests with Multinomial Sampling
401(6)
10.2 Effects of Survey Design on Chi-Square Tests
407(4)
10.3 Corrections to x2 Tests
411(6)
10.4 Loglinear Models
417(4)
10.5
Chapter Summary
421(1)
10.6 Exercises
422(7)
Chapter 11 Regression with Complex Survey Data
429(40)
11.1 Model-Based Regression in Simple Random Samples
430(4)
11.2 Regression in Complex Surveys
434(11)
11.3 Using Regression to Compare Domain Means
445(2)
11.4 Should Weights Be Used in Regression?
447(6)
11.5 Mixed Models for Cluster Samples
453(2)
11.6 Logistic Regression
455(2)
11.7 Generalized Regression Estimation for Population Totals
457(4)
11.8
Chapter Summary
461(1)
11.9 Exercises
462(7)
Chapter 12 Two-Phase Sampling
469(26)
12.1 Theory for Two-Phase Sampling
472(1)
12.2 Two-Phase Sampling with Stratification
473(4)
12.3 Ratio and Regression Estimation in Two-Phase Samples
477(4)
12.4 Jackknife Variance Estimation for Two-Phase Sampling
481(1)
12.5 Designing a Two-Phase Sample
482(3)
12.6
Chapter Summary
485(1)
12.7 Exercises
486(9)
Chapter 13 Estimating Population Size
495(16)
13.1 Capture-Recapture Estimation
495(6)
13.2 Multiple Recapture Estimation
501(4)
13.3
Chapter Summary
505(1)
13.4 Exercises
505(6)
Chapter 14 Rare Populations and Small Area Estimation
511(16)
14.1 Sampling Rare Populations
512(6)
14.2 Small Area Estimation
518(4)
14.3
Chapter Summary
522(1)
14.4 Exercises
523(4)
Chapter 15 Survey Quality
527(22)
15.1 Coverage Error
529(4)
15.2 Nonresponse Error
533(2)
15.3 Measurement Error
535(5)
15.4 Sensitive Questions
540(2)
15.5 Processing Error
542(1)
15.6 Total Survey Quality
543(2)
15.7
Chapter Summary
545(1)
15.8 Exercises
546(3)
APPENDIX A Probability Concepts Used in Sampling
549(14)
A.1 Probability
549(3)
A.2 Random Variables and Expected Value
552(4)
A.3 Conditional Probability
556(2)
A.4 Conditional Expectation
558(5)
References 563(24)
Author Index 587(5)
Subject Index 592
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 Deans 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.