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Preface to the Fourth Edition

Part 1 Basic Concepts

1. Uses of Sample Surveys

1.1 Why Sample Surveys Are Used

1.2 Designing Sample Surveys

1.2.1 Sample Design

1.2.2 Survey Measurements

1.2.3 Survey Operations

1.2.4 Statistical Analysis and Report Writing

1.3 Preliminary Planning of a Sample Survey

Exercises

Bibliography

2. The Population and the Sample

2.1 The Population

2.1.1 Elementary Units

2.1.2 Population Parameters

2.2 The Sample

2.2.1 Probability and Nonprobability Sampling

2.2.2 Sampling Frames, Sampling Units, and Enumeration Units

2.2.3 Sample Measurements and Summary Statistics

2.2.4 Estimation of Population Characteristics

2.3 Sampling Distributions

2.4 Characteristics of Estimates of Population Parameters

2.4.1 Bias

2.4.2 Mean Square Error

2.4.3 Validity, Reliability, and Accuracy

2.5 Criteria for a Good Sample Design

2.6 Summary

Exercises

Bibliography

Part 2 Major Sampling Designs and Estimation Procedures

3. Simple Random Sampling

3.1 What Is a Simple Random Sample?

3.1.1 How to Take a Simple Random Sample

3.1.2 Probability of an Element Being Selected

3.2 Estimation of Population Characteristics Under Simple Random Sampling

3.2.1 Estimation Formulas

3.2.2 Numerical Computation of Estimates and Their Standard Errors

3.3 Sampling Distributions of Estimated Population Characteristics

3.4 Coefficients of Variation of Estimated Population Parameters

3.5 Reliability of Estimates

3.6 Estimation of Parameters for Subdomains

3.7 How Large a Sample Do We Need?

3.8 Why Simple Random Sampling Is Rarely Used

3.9 Summary

Exercises

Bibliography

4. Systematic Sampling

4.1 How To Take a Systematic Sample

4.2 Estimation of Population Characteristics

4.3 Sampling Distribution of Estimates

4.4 Variance of Estimates

4.5 A Modification That Always Yields Unbiased Estimates

4.6 Estimation of Variances

4.7 Repeated Systematic Sampling

4.7.1 Use of Stata for Estimation In Repeated Systematic Sampling

4.7.2 Use of SUDAAN for Estimation In Repeated Systematic Sampling

4.8 How Large a Sample Do We Need?

4.9 Using Frames That Are Not Lists

4.10 Summary

Exercises

Bibliography

5. Stratification and Stratified Random Sampling

5.1 What is a Stratified Random Sample?

5.2 How to Take a Stratified Random Sample

5.3 Why Stratified Sampling?

5.4 Population Parameters for Strata

5.5 Sample Statistics for Strata

5.6 Estimation of Population Parameters from Stratified Random Sampling

5.7 Summary

Exercises

Bibliography

6. Stratified Random Sampling: Further Issues

6.1 Estimation of Population Parameters

6.2 Sampling Distributions of Estimates

6.3 Estimation of Standard Errors

6.4 Estimation of Characteristics of Subgroups

6.5 Allocation of Sample to Strata

6.5.1 Equal Allocation

6.5.2 Proportional Allocation: Self-Weighting Samples

6.5.3 Optimal Allocation

6.5.4 Optimal Allocation and Economics

6.6 Stratification After Sampling

6.7 How Large a Sample is Needed?

6.8 Construction of Stratum Boundaries and Desired Number of Strata

6.9 Summary

Exercises

Bibliography

7. Ratio Estimation

7.1 Ratio Estimation Under Simple Random Sampling

7.2 Estimation of Ratios for Subdomains Under Simple Random Sampling

7.3 Poststratified Ratio Estimates Under Simple Random Sampling

7.4 Ratio Estimation of Totals Under Simple Random Sampling

7.5 Comparison of Ratio Estimate with Simple Inflation Estimate

7.6 Approximation to the Standard Error of the Ratio Estimated Total

7.7 Determination of Sample Size

7.8 Regression Estimation of Totals

7.9 Ratio Estimation in Stratified Random Sampling

7.10 Summary

Exercises

Bibliography

8. Cluster Sampling: Introduction and Overview

8.1 What is Cluster Sampling?

8.2 Why is Cluster Sampling Widely Used?

8.3 A Disadvantage of Cluster Sampling: High Standard Errors

8.4 How Cluster Sampling Is Treated in This Book

8.5 Summary

Exercises

Bibliography

9. Simple One-Stage Cluster Sampling

9.1 How to Take a Simple One-Stage Cluster Sample

9.2 Estimation of Population Characteristics

9.3 Sampling Distributions of Estimates

9.4 How Large a Sample Is Needed?

9.5 Reliability of Estimates and Costs Involved

9.6 Choosing a Sampling Design Based on Cost and Reliability

9.7 Summary

Exercises

Bibliography

10. Two-Stage Cluster Sampling: Clusters Sampled with Equal Probability

10.1 Situation in Which All Clusters Have the Same Number

*N*_{I} of Enumeration Units

10.1.1 How to Take a Simple Two-Stage Cluster Sample

10.1.2 Estimation of Population Characteristics

10.1.3 Estimation of Standard Errors

10.1.4 Sampling Distribution of Estimates

10.1.5 How Large a Sample Is Needed?

10.1.6 Choosing the Optimal Cluster Size *n* Considering Costs

10.1.7 Some Shortcut Formulas for Determining the Optimal Number *n*

10.2 Situation in Which Not All Clusters Have the Same Number

*N*_{i} of Enumeration Units

10.2.1 How to Take a Simple Two-Stage Cluster Sample for This Design

10.2.2 Estimation of Population Characteristics

10.2.3 Estimation of Standard Errors of Estimates

10.2.4 Sampling Distribution of Estimates

10.2.5 How Large a Sample Do We Need?

10.2.6 Choosing the Optimal Cluster Size *n* Considering Costs

10.3 Systematic Sampling as Cluster Sampling

10.4 Summary

Exercises

Bibliography

11. Cluster Sampling in Which Clusters Are Sampled with Unequal Probability: Probability Proportional to Size Sampling

11.1 Motivation for

*Not* Sampling Clusters with Equal Probability

11.2 Two General Classes of Estimators Valid for Sample Designs in Which Units Are Selected with Unequal Probability

11.2.1 The Horvitz–Thompson Estimator

11.2.2 The Hansen–Hurwitz Estimator

11.3 Probability Proportional to Size Sampling

11.3.1 Probability Proportional to Size Sampling with Replacement: Use of the Hansen–Hurwitz Estimator

11.3.2 PPS Sampling When the Measure of Size Variable Is not the Number of Enumeration Units

11.3.3 How to Take a PPS Sample with Replacement

11.3.4 Sequential Methods of PPS Sampling with Replacement—Chromy’s Probability with Minimum Replacement (PMR) Method

11.3.5 How Large a Sample is Needed for a Two-Stage Sample in Which Clusters Are Selected PPS with Replacement?

11.3.6 Telephone PPS Sampling: The Mitofsky–Waksberg Method of Random Digit Dialing

11.4 Further Comment on PPS Sampling

11.5 Summary

Exercises

Bibliography

12. Variance Estimation in Complex Sample Surveys

12.1 Linearization

12.2 Replication Methods

12.2.1 The Balanced Repeated Replication Method

12.2.2 Jackknife Estimation

12.2.3 Estimation of Interviewer Variability by Use of Replicated Sampling (Interpenetrating Samples)

12.3 Summary

Exercises

Technical Appendix

Bibliography

Part 3 Selected Topics in Sample Survey Methodology

13. Nonresponse and Missing Data in Sample Surveys

13.1 Effect of Nonresponse on Accuracy of Estimates

13.2 Methods of Increasing the Response Rate in Sample Surveys

13.2.1 Increasing the Number of Households Contacted Successfully

13.2.2 Increasing the Completion Rate in Mail Questionnaires

13.2.3 Decreasing the Number of Refusals in Face-to-Face Telephone Interviews

13.2.4 Using Endorsements

13.3 Mail Surveys Combined with Interviews of Nonrespondents

13.3.1 Determination of Optimal Fraction of Initial Nonrespondents to Subsample for Intensive Effort

13.3.2 Determination of Sample Size Needed for a Two-Stage Mail Survey

13.4 Other Uses of Double (or Two-Phase) Sampling Methodology

13.5 Item Nonresponse: Methods of Imputation

13.5.1 Mechanisms by Which Missing Values Arise

13.5.2 Some Methods for Analyzing Data in the Presence of Missing Values

13.5.3 Some Imputation Methods

13.6 Multiple Imputation

13.7 Summary

Exercises

Bibliography

14. Selected Topics in Sample Design and Estimation Methodology

14.1 World Health Organization EPI Surveys: A Modification of PPS Sampling for Use in Developing Countries

14.2 Quality Assurance Sampling

14.3 Sample Sizes for Longitudinal Studies

14.3.1 Simple Random Sampling

14.3.2 Simple One-Stage Cluster Sampling

14.3.3 Cluster Sampling with More Than One Domain

14.4 Estimation of Prevalence of Diseases from Screening Studies

14.5 Estimation of Rare Events: Network Sampling

14.6 Estimation of Rare Events: Dual Samples

14.7 Estimation of Characteristics for Local Areas: Synthetic Estimation

14.8 Extraction of Sensitive Information: Randomized Response Techniques

14.9 Summary

Exercises

Bibliography

15. Telephone Survey Sampling

15.1 Introduction

15.1.1 The Twentieth Century

15.1.2 The Twenty-First Century

15.2 History of Telephone Sampling in the United States

15.2.1 Early Design of Telephone Surveys

15.2.2 Random Digit Dialing

15.2.3 Mitofsky–Waksberg Sampling Method

15.2.4 List-Assisted Random Digit Dialing Methods

15.3 Within-Household Selection Techniques

15.3.1 Probability-Based Methods

15.3.2 Quasi-Probability Methods

15.3.3 Nonprobability Methods

15.3.4 Minimally Intrusive Method

15.4 Steps in the Telephone Survey Process

15.4.1 Computer-Assisted Telephone Interviewing

15.4.2 Quality Control in Telelphone Surveys

15.5 Drawing and Managing a Telephone Survey Sample

15.5.1 Drawing the Sample

15.5.2 Managing the Sample

15.5.3 Developing an Analysis File

15.5.4 Data Weighting and Adjustment

15.6 Post-Survey Data Enhancement Procedures

15.6.1 Data Weighting

15.6.2 Steps in the Weighting Process

15.6.3 Compensation for Exclusion of Nontelephone Households

15.7 Imputation of Missing Data

15.8 Declining Coverage and Response Rates

15.9 Addressing the Problems with Cell Phones

15.9.1 Research on Cell Phone Surveys

15.9.2 Sampling from the Cell Phone Frame

15.10 Address-Based Sampling

Exercises

Bibliography

16. Constructing the Survey Weights

16.1 Introduction

16.2 Objectives of Weighting

16.2.1 Basic Concepts

16.2.2 Weighting to Reduce Frame Bias

16.2.3 Weighting to Reduce Nonresponse Bias

16.2.4 Weighting to Reduce Sampling Variance

16.3 Constructing the Sampling Weights

16.3.1 Base Weights

16.3.2 Nonresponse Adjustments

16.3.3 Frame Coverage Adjustments

16.3.4 Constructing the Final Weights

16.4 Estimation and Analysis Issues

16.4.1 Effect of Weighting on the Variance

16.4.2 Using Weights in Analysis

16.5 Summary

Bibliography

17. Strategies for Design-Based Analysis of Sample Survey Data

17.1 Steps Required for Performing a Design-Based Analysis

17.2 Analysis Issues for “Typical” Sample Surveys

17.3 Summary

Technical Appendix

Bibliography

Appendix

Answers to Selected Exercises

Index