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E-grāmata: Sampling: Design and Analysis

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"The level is appropriate for an upper-level undergraduate or graduate-level statistics major. Sampling: Design and Analysis (SDA) will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor of survey statistics will gain even more from the content that SDA offers. The updates to SDA have potential to enrich traditional survey sampling classes at both the undergraduate and graduate levels. The new discussions of low response rates, non-probability surveys, and internet as a data collection mode hold particular value, as these statistical issues have become increasingly important in survey practice in recent years I would eagerly adopt the new edition of SDA as the required textbook." (Emily Berg, Iowa State University)

What is the unemployment rate? What is the total area of land planted with soybeans? How many persons have antibodies to the virus causing COVID-19? Sampling: Design and Analysis, Third Edition shows 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 the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology. Readers should be familiar with concepts from an introductory statistics class including probability and linear regression; optional sections contain statistical theory for readers familiar with mathematical statistics.

Key Features:





Has been thoroughly revised to incorporate recent research and applications. Includes a new chapter on nonprobability samples, and more than 200 new examples and exercises have been added. Teaches the principles of sampling with examples from social sciences, public opinion research, public health, business, agriculture, and ecology.

SDAs companion website contains data sets, computer code, and links to two free downloadable supplementary books (also available in paperback) that provide step-by-step guideswith code, annotated output, and helpful tipsfor working through the SDA examples. Instructors can use either R or SAS® software.





SAS® Software Companion for Sampling: Design and Analysis, Third Edition by Sharon L. Lohr (2022, CRC Press) R Companion for Sampling: Design and Analysis, Third Edition by Yan Lu and Sharon L. Lohr (2022, CRC Press)

Recenzijas

"In summary, the revisions of SDA hold value for practitioners, educators, and students. The level is appropriate for an upper-level undergraduate or graduate-level statistics major. SDA will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor of survey statistics will gain even more from the content that SDA offers. The updates to SDA have potential to enrich traditional survey sampling classes at both the undergraduate and graduate levels. The new discussions of low response rates, non-probability surveys, and internet as a data collection mode hold particular value, as these statistical issues have become increasingly important in survey practice in recent years. I have personally used past editions of SDA as a resource in my research and work. I am therefore comfortable recommending that students purchase the SDA. I expect that many of them will find SDA to be a useful reference, even beyond their courseworkThe revision of SDA is not tied to a specific software language. The updated online supplements allow one to easily use SDA in conjunction with either SAS or R. I would eagerly adopt the new edition of SDA as the required textbook." (Emily Berg, Iowa State University)

"I believe that this book now shines above the competing texts. The examples and problems are updated and more relatable to todays student. I believe that the practice analyzing real data will give students a competitive edge on todays job market. Once this is published, I will absolutely adopt this textbook in my course. (Truly, I have been dying for an updated book for Sampling Theory!)" (Samantha Seals, University of West Florida)

"I love Lohrs text on this subject. This book should be adopted as many of the new additions I have reviewed here, including Chapter 15 on nonprobability samples, really elevate it and allow it to retain its relevance amid many changes and advances in our field." (Trent D. Buskirk, Bowling Green State University)

"I think this is by far the best book on survey sampling at the undergraduate level. It is the perfect balance between theoretical and practical. It has an excellent set of exercises, and great suggested additional readings. I use it in my courses and recommend it to everyoneExcellent idea to expand discussion of nonprobability samples. That is very practically relevant." (Elaine Zanutto, University of Pennsylvania) "In summary, the revisions of SDA hold value for practitioners, educators, and students. The level is appropriate for an upper-level undergraduate or graduate-level statistics major. SDA will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor of survey statistics will gain even more from the content that SDA offers. The updates to SDA have potential to enrich traditional survey sampling classes at both the undergraduate and graduate levels. The new discussions of low response rates, non-probability surveys, and internet as a data collection mode hold particular value, as these statistical issues have become increasingly important in survey practice in recent years. I have personally used past editions of SDA as a resource in my research and work. I am therefore comfortable recommending that students purchase the SDA. I expect that many of them will find SDA to be a useful reference, even beyond their courseworkThe revision of SDA is not tied to a specific software language. The updated online supplements allow one to easily use SDA in conjunction with either SAS or R. I would eagerly adopt the new edition of SDA as the required textbook." -Emily Berg, Iowa State University

"I believe that this book now shines above the competing texts. The examples and problems are updated and more relatable to todays student. I believe that the practice analyzing real data will give students a competitive edge on todays job market. Once this is published, I will absolutely adopt this textbook in my course. (Truly, I have been dying for an updated book for Sampling Theory!)" -Samantha Seals, University of West Florida

"I love Lohrs text on this subject. This book should be adopted as many of the new additions I have reviewed here, including Chapter 15 on nonprobability samples, really elevate it and allow it to retain its relevance amid many changes and advances in our field." -Trent D. Buskirk, Bowling Green State University

"I think this is by far the best book on survey sampling at the undergraduate level. It is the perfect balance between theoretical and practical. It has an excellent set of exercises, and great suggested additional readings. I use it in my courses and recommend it to everyoneExcellent idea to expand discussion of nonprobability samples. That is very practically relevant." -Elaine Zanutto, University of Pennsylvania

"This is an excellent book, built on the extensive knowledge of Sampling Design and Analysis gained by the author over many decades. I will limit my review on this third edition as I had no access to the previous editions. At the end of each chapter, there are many mastery exercises and projects to consolidate learning. ... The author must be commended for preparing a comprehensive bibliography on survey sampling. In conclusion, the book would be a good text for graduate study, Survey research statisticians, or as a reference for those with a sound understanding of basic mathematical statistics." -June Elijah Simakani in Journal of the Royal Statistical Society, 2022

"A generation of students have been trained on the two previous editions of this sampling text, and the third edition will ensure that this continues. This book is remarkable for its utility for both learners and practitioners, much as Cochrans classic text (1977) was for decades. For teaching, I have been using the text only for a one-semester upper-level graduate course for statistics students. It provides sufficient technical detail, challenging exercises, and up-to-date references to prepare students to conduct research in the field. ... Sampling practice has undergone profound changes since publication of the second edition of this text in 2009. This edition reflects those changes beautifully. A new chapter on nonprobability sampling provides an overview of the technical details for the newest methods of adjustment. ... In short, this book is a classic. The new edition makes modernizing your sampling course easy and painless. It is also an excellent reference for practitioners and a guide to the latest literature in sampling methodology." -S. Lynne Stokes in Journal of the American Statistical Association, October 2022

"Concluding, the Lohrs book Sampling: Design and Analysis, Third Edition will be again a reference book in the field of survey sampling as the first two editions. The two companion books for carrying out surveys and estimates with R and SAS software fill a gap in the specialist literature. Students, instructors and anyone wishing to train in survey techniques will find in this book the necessary methodology, as well as many examples of sample surveys on real data and how to implement them using R and SAS software in its two companion books." -Camelia Goga, in The Survey Statistician, July 2023

Preface xiii
Symbols and Acronyms xxi
1 Introduction
1(30)
1.1 Guidance from Samples
1(2)
1.2 Populations and Representative Samples
3(3)
1.3 Selection Bias
6(4)
1.3.1 Convenience Samples
6(1)
1.3.2 Purposive or Judgment Samples
6(1)
1.3.3 Self-Selected Samples
6(2)
1.3.4 Undercoverage
8(1)
1.3.5 Overcoverage
8(1)
1.3.6 Nonresponse
9(1)
1.3.7 What Good Are Samples with Selection Bias?
9(1)
1.4 Measurement Error
10(3)
1.5 Questionnaire Design
13(4)
1.6 Sampling and Nonsampling Errors
17(1)
1.7 Why Use Sampling?
18(2)
1.7.1 Advantages of Taking a Census
19(1)
1.7.2 Advantages of Taking a Sample Instead of a Census
19(1)
1.8
Chapter Summary
20(2)
1.9 Exercises
22(9)
2 Simple Probability Samples
31(48)
2.1 Types of Probability Samples
32(2)
2.2 Framework for Probability Sampling
34(5)
2.3 Simple Random Sampling
39(5)
2.4 Sampling Weights
44(2)
2.5 Confidence Intervals
46(4)
2.6 Using Statistical Software to Analyze Survey Data
50(1)
2.7 Determining the Sample Size
50(5)
2.8 Systematic Sampling
55(1)
2.9 Randomization Theory for Simple Random Sampling*
56(2)
2.10 Model-Based Theory for Simple Random Sampling*
58(4)
2.11 When Should a Simple Random Sample Be Used?
62(1)
2.12
Chapter Summary
63(3)
2.13 Exercises
66(13)
3 Stratified Sampling
79(42)
3.1 What Is Stratified Sampling?
79(4)
3.2 Theory of Stratified Sampling
83(4)
3.3 Sampling Weights in Stratified Random Sampling
87(2)
3.4 Allocating Observations to Strata
89(7)
3.4.1 Proportional Allocation
89(2)
3.4.2 Optimal Allocation
91(2)
3.4.3 Allocation for Specified Precision within Strata
93(1)
3.4.4 Which Allocation to Use?
94(2)
3.4.5 Determining the Total Sample Size
96(1)
3.5 Defining Strata
96(3)
3.6 Model-Based Theory for Stratified Sampling*
99(1)
3.7
Chapter Summary
100(1)
3.8 Exercises
101(20)
4 Ratio and Regression Estimation
121(46)
4.1 Ratio Estimation in Simple Random Sampling
121(14)
4.1.1 Why Use Ratio Estimation?
122(3)
4.1.2 Bias and Mean Squared Error of Ratio Estimators
125(7)
4.1.3 Ratio Estimation with Proportions
132(2)
4.1.4 Ratio Estimation Using Weight Adjustments
134(1)
4.1.5 Advantages of Ratio Estimation
135(1)
4.2 Regression Estimation in Simple Random Sampling
135(4)
4.3 Estimation in Domains
139(3)
4.4 Poststratification
142(3)
4.5 Ratio Estimation with Stratified Sampling
145(2)
4.6 Model-Based Theory for Ratio and Regression Estimation*
147(7)
4.6.1 A Model for Ratio Estimation
148(3)
4.6.2 A Model for Regression Estimation
151(1)
4.6.3 Differences between Model-Based and Design-Based Estimators
152(2)
4.7
Chapter Summary
154(1)
4.8 Exercises
155(12)
5 Cluster Sampling with Equal Probabilities
167(52)
5.1 Notation for Cluster Sampling
171(1)
5.2 One-Stage Cluster Sampling
172(10)
5.2.1 Clusters of Equal Sizes: Estimation
172(2)
5.2.2 Clusters of Equal Sizes: Theory
174(5)
5.2.3 Clusters of Unequal Sizes
179(3)
5.3 Two-Stage Cluster Sampling
182(10)
5.4 Designing a Cluster Sample
192(5)
5.4.1 Choosing the psu Size
193(1)
5.4.2 Choosing Subsampling Sizes
194(2)
5.4.3 Choosing the Sample Size (Number of psus)
196(1)
5.5 Systematic Sampling
197(3)
5.6 Model-Based Theory for Cluster Sampling*
200(5)
5.6.1 Estimation Using Models
202(3)
5.6.2 Design Using Models
205(1)
5.7
Chapter Summary
205(2)
5.8 Exercises
207(12)
6 Sampling with Unequal Probabilities
219(54)
6.1 Sampling One Primary Sampling Unit
221(3)
6.2 One-Stage Sampling with Replacement
224(6)
6.2.1 Selecting Primary Sampling Units
224(2)
6.2.2 Theory of Estimation
226(3)
6.2.3 Designing the Selection Probabilities
229(1)
6.2.4 Weights in Unequal-Probability Sampling with Replacement
230(1)
6.3 Two-Stage Sampling with Replacement
230(3)
6.4 Unequal-Probability Sampling without Replacement
233(10)
6.4.1 The Horvitz-Thompson Estimator for One-Stage Sampling
235(4)
6.4.2 Selecting the psus
239(1)
6.4.3 The Horvitz-Thompson Estimator for Two-Stage Sampling
239(1)
6.4.4 Weights in Unequal-Probability Samples
240(3)
6.5 Examples of Unequal-Probability Samples
243(4)
6.6 Randomization Theory Results and Proofs*
247(7)
6.7 Model-Based Inference with Unequal-Probability Samples*
254(2)
6.8
Chapter Summary
256(2)
6.9 Exercises
258(15)
7 Complex Surveys
273(38)
7.1 Assembling Design Components
273(3)
7.1.1 Building Blocks for Surveys
273(2)
7.1.2 Ratio Estimation in Complex Surveys
275(1)
7.1.3 Simplicity in Survey Design
276(1)
7.2 Sampling Weights
276(4)
7.2.1 Constructing Sampling Weights
276(3)
7.2.2 Self-Weighting and Non-Self-Weighting Samples
279(1)
7.3 Estimating Distribution Functions and Quantiles
280(6)
7.4 Design Effects
286(2)
7.5 The National Health and Nutrition Examination Survey
288(3)
7.6 Graphing Data from a Complex Survey
291(10)
7.6.1 Univariate Plots
292(3)
7.6.2 Bivariate Plots
295(6)
7.7
Chapter Summary
301(2)
7.8 Exercises
303(8)
8 Nonresponse
311(48)
8.1 Effects of Ignoring Nonresponse
312(2)
8.2 Designing Surveys to Reduce Nonresponse
314(5)
8.3 Two-Phase Sampling
319(1)
8.4 Response Propensities and Mechanisms for Nonresponse
320(3)
8.4.1 Auxiliary Information for Treating Nonresponse
320(1)
8.4.2 Methods to Adjust for Nonresponse
320(1)
8.4.3 Response Propensities
321(1)
8.4.4 Types of Missing Data
321(2)
8.5 Adjusting Weights for Nonresponse
323(6)
8.5.1 Weighting Class Adjustments
324(4)
8.5.2 Regression Models for Response Propensities
328(1)
8.6 Poststratification
329(6)
8.6.1 Poststratification Using Weights
330(1)
8.6.2 Raking Adjustments
331(2)
8.6.3 Steps for Constructing Final Survey Weights
333(1)
8.6.4 Advantages and Disadvantages of Weighting Adjustments
334(1)
8.7 Imputation
335(5)
8.7.1 Deductive Imputation
335(1)
8.7.2 Cell Mean Imputation
336(1)
8.7.3 Hot-Deck Imputation
337(1)
8.7.4 Regression Imputation and Chained Equations
338(1)
8.7.5 Imputation from Another Data Source
338(1)
8.7.6 Multiple Imputation
339(1)
8.7.7 Advantages and Disadvantages of Imputation
339(1)
8.8 Response Rates and Nonresponse Bias Assessments
340(6)
8.8.1 Calculating and Reporting Response Rates
340(2)
8.8.2 What Is an Acceptable Response Rate?
342(1)
8.8.3 Nonresponse Bias Assessments
343(3)
8.9
Chapter Summary
346(2)
8.10 Exercises
348(11)
9 Variance Estimation in Complex Surveys
359(36)
9.1 Linearization (Taylor Series) Methods
359(4)
9.2 Random Group Methods
363(4)
9.2.1 Replicating the Survey Design
363(2)
9.2.2 Dividing the Sample into Random Groups
365(2)
9.3 Resampling and Replication Methods
367(12)
9.3.1 Balanced Repeated Replication (BRR)
367(6)
9.3.2 Jackknife
373(2)
9.3.3 Bootstrap
375(2)
9.3.4 Creating and Using Replicate Weights
377(2)
9.4 Generalized Variance Functions
379(2)
9.5 Confidence Intervals
381(3)
9.5.1 Confidence Intervals for Smooth Functions of Population Totals
381(1)
9.5.2 Confidence Intervals for Population Quantiles
382(2)
9.6
Chapter Summary
384(2)
9.7 Exercises
386(9)
10 Categorical Data Analysis in Complex Surveys
395(24)
10.1 Chi-Square Tests with Multinomial Sampling
395(4)
10.1.1 Testing Independence of Factors
397(1)
10.1.2 Testing Homogeneity of Proportions
398(1)
10.1.3 Testing Goodness of Fit
398(1)
10.2 Effects of Survey Design on Chi-Square Tests
399(4)
10.2.1 Contingency Tables for Data from Complex Surveys
400(1)
10.2.2 Effects on Hypothesis Tests and Confidence Intervals
401(2)
10.3 Corrections to Chi-Square Tests
403(5)
10.3.1 Wald Tests
403(2)
10.3.2 Rao-Scott Tests
405(2)
10.3.3 Model-Based Methods for Chi-Square Tests
407(1)
10.4 Loglinear Models
408(3)
10.4.1 Loglinear Models with Multinomial Sampling
409(1)
10.4.2 Loglinear Models in a Complex Survey
410(1)
10.5
Chapter Summary
411(1)
10.6 Exercises
412(7)
11 Regression with Complex Survey Data
419(38)
11.1 Model-Based Regression in Simple Random Samples
420(3)
11.2 Regression with Complex Survey Data
423(10)
11.2.1 Point Estimation
424(3)
11.2.2 Standard Errors
427(3)
11.2.3 Multiple Regression
430(2)
11.2.4 Regression Using Weights versus Weighted Least Squares
432(1)
11.3 Using Regression to Compare Domain Means
433(2)
11.4 Interpreting Regression Coefficients from Survey Data
435(5)
11.4.1 Purposes of Regression Analyses
435(1)
11.4.2 Model-Based and Design-Based Inference
436(1)
11.4.3 Survey Weights and Regression
437(1)
11.4.4 Survey Design and Standard Errors
438(1)
11.4.5 Mixed Models for Cluster Samples
439(1)
11.5 Logistic Regression
440(2)
11.6 Calibration to Population Totals
442(4)
11.7
Chapter Summary
446(2)
11.8 Exercises
448(9)
12 Two-Phase Sampling
457(26)
12.1 Theory for Two-Phase Sampling
459(2)
12.2 Two-Phase Sampling with Stratification
461(3)
12.3 Ratio and Regression Estimation in Two-Phase Samples
464(3)
12.3.1 Two-Phase Sampling with Ratio Estimation
464(2)
12.3.2 Generalized Regression Estimation in Two-Phase Sampling
466(1)
12.4 Jackknife Variance Estimation for Two-Phase Sampling
467(2)
12.5 Designing a Two-Phase Sample
469(2)
12.5.1 Two-Phase Sampling with Stratification
469(2)
12.5.2 Optimal Allocation for Ratio Estimation
471(1)
12.6
Chapter Summary
471(1)
12.7 Exercises
472(11)
13 Estimating the Size of a Population
483(16)
13.1 Capture-Recapture Estimation
483(5)
13.1.1 Contingency Tables for Capture-Recapture Experiments
484(1)
13.1.2 Confidence Intervals for AT
485(1)
13.1.3 Using Capture-Recapture on Lists
486(2)
13.2 Multiple Recapture Estimation
488(3)
13.3
Chapter Summary
491(1)
13.4 Exercises
492(7)
14 Rare Populations and Small Area Estimation
499(18)
14.1 Sampling Rare Populations
500(6)
14.1.1 Stratified Sampling with Disproportional Allocation
500(1)
14.1.2 Two-Phase Sampling
501(1)
14.1.3 Unequal-Probability Sampling
501(1)
14.1.4 Multiple Frame Surveys
502(2)
14.1.5 Network or Multiplicity Sampling
504(1)
14.1.6 Snowball Sampling
505(1)
14.1.7 Sequential Sampling
506(1)
14.2 Small Area Estimation
506(4)
14.2.1 Direct Estimators
507(1)
14.2.2 Synthetic and Composite Estimators
508(1)
14.2.3 Model-Based Estimators
509(1)
14.3
Chapter Summary
510(2)
14.4 Exercises
512(5)
15 Nonprobability Samples
517(40)
15.1 Types of Nonprobability Samples
518(6)
15.1.1 Administrative Records
518(1)
15.1.2 Quota Samples
519(3)
15.1.3 Judgment Samples
522(1)
15.1.4 Convenience Samples
523(1)
15.2 Selection Bias and Mean Squared Error
524(7)
15.2.1 Random Variables Describing Participation in a Sample
525(3)
15.2.2 Bias and Mean Squared Error of a Sample Mean
528(3)
15.3 Reducing Bias of Estimates from Nonprobability Samples
531(8)
15.3.1 Weighting
531(5)
15.3.2 Estimate the Values of the Missing Units
536(1)
15.3.3 Measures of Uncertainty for Nonprobability Samples
537(2)
15.4 Nonprobability versus Low-Response Probability Samples
539(3)
15.5
Chapter Summary
542(2)
15.6 Exercises
544(13)
16 Survey Quality
557(22)
16.1 Coverage Error
559(3)
16.1.1 Measuring Coverage and Coverage Bias
559(1)
16.1.2 Coverage and Survey Mode
560(2)
16.1.3 Improving Coverage
562(1)
16.2 Nonresponse Error
562(2)
16.3 Measurement Error
564(6)
16.3.1 Measuring and Modeling Measurement Error
565(2)
16.3.2 Reducing Measurement Error
567(1)
16.3.3 Sensitive Questions
568(1)
16.3.4 Randomized Response
568(2)
16.4 Processing Error
570(1)
16.5 Total Survey Quality
571(2)
16.6
Chapter Summary
573(2)
16.7 Exercises
575(4)
A Probability Concepts Used in Sampling
579(14)
A.1 Probability
579(3)
A.1.1 Simple Random Sampling with Replacement
580(1)
A.1.2 Simple Random Sampling without Replacement
581(1)
A.2 Random Variables and Expected Value
582(3)
A.3 Conditional Probability
585(2)
A.4 Conditional Expectation
587(4)
A.5 Exercises
591(2)
Bibliography 593(48)
Index 641
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 Deans Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a statistical consultant and writer.