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Click to enlarge See the back cover 
Sampling of Populations: Methods and Applications, Fourth Edition 

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Comment from the Stata technical groupThe fourth edition of Sampling of Populations: Methods and Applications, by Paul S. Levy and Stanley Lemeshow, introduces the methods of survey statistics while grounding the analysis in concise empirical applications. Because many of the examples use Stata, the book is also a good introduction to survey methods using Stata. In fact, many of the updates in this edition feature Stata's increasing capabilities in survey methods. Levy and Lemeshow begin by describing the reasons why sample surveys are used and some of the costs and benefits to different designs. One chapter introduces the basic concepts of populations, samples, sampling distribution, and characteristics of population parameter estimates. The authors then take the reader on a tour of the major sampling designs: simple random sampling, systematic sampling, stratified random sampling, and cluster sampling. For each survey design, the authors derive estimators for standard population parameters. They illustrate formulas with empirical examples, many of which use Stata. They also present accessible treatments of ratio estimation, variance estimation, and several special topics, including nonresponse and missing data. The fourth edition also includes a new chapter on constructing survey weights for various designs and reweighting scenarios. 

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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
1.3 Preliminary Planning of a Sample Survey1.2.2 Survey Measurements 1.2.3 Survey Operations 1.2.4 Statistical Analysis and Report Writing Exercises Bibliography 2. The Population and the Sample
2.1 The Population
2.1.1 Elementary Units
2.2 The Sample2.1.2 Population Parameters
2.2.1 Probability and Nonprobability Sampling
2.3 Sampling Distributions2.2.2 Sampling Frames, Sampling Units, and Enumeration Units 2.2.3 Sample Measurements and Summary Statistics 2.2.4 Estimation of Population Characteristics 2.4 Characteristics of Estimates of Population Parameters
2.4.1 Bias
2.5 Criteria for a Good Sample Design2.4.2 Mean Square Error 2.4.3 Validity, Reliability, and Accuracy 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
3.2 Estimation of Population Characteristics Under Simple Random Sampling3.1.2 Probability of an Element Being Selected
3.2.1 Estimation Formulas
3.3 Sampling Distributions of Estimated Population Characteristics3.2.2 Numerical Computation of Estimates and Their Standard Errors 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
4.8 How Large a Sample Do We Need?4.7.2 Use of SUDAAN for Estimation In Repeated Systematic Sampling 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
6.6 Stratification After Sampling6.5.2 Proportional Allocation: SelfWeighting Samples 6.5.3 Optimal Allocation 6.5.4 Optimal Allocation and Economics 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 OneStage Cluster Sampling
9.1 How to Take a Simple OneStage 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. TwoStage 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 TwoStage Cluster Sample
10.2 Situation in Which Not All Clusters Have the Same Number N_{i} of Enumeration Units10.1.2 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
10.2.1 How to Take a Simple TwoStage Cluster Sample for This Design
10.3 Systematic Sampling as Cluster Sampling10.2.2 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 10.4 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
11.3 Probability Proportional to Size Sampling11.2.2 The Hansen–Hurwitz Estimator
11.3.1 Probability Proportional to Size Sampling with Replacement: Use of the Hansen–Hurwitz Estimator
11.4 Further Comment on PPS Sampling11.3.2 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 TwoStage Sample in Which Clusters Are Selected PPS with Replacement? 11.3.6 Telephone PPS Sampling: The Mitofsky–Waksberg Method of Random Digit Dialing 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
12.3 Summary12.2.2 Jackknife Estimation 12.2.3 Estimation of Interviewer Variability by Use of Replicated Sampling (Interpenetrating Samples) 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
13.3 Mail Surveys Combined with Interviews of Nonrespondents13.2.2 Increasing the Completion Rate in Mail Questionnaires 13.2.3 Decreasing the Number of Refusals in FacetoFace Telephone Interviews 13.2.4 Using Endorsements
13.3.1 Determination of Optimal Fraction of Initial Nonrespondents to Subsample for Intensive Effort
13.4 Other Uses of Double (or TwoPhase) Sampling Methodology13.3.2 Determination of Sample Size Needed for a TwoStage Mail Survey 13.5 Item Nonresponse: Methods of Imputation
13.5.1 Mechanisms by Which Missing Values Arise
13.6 Multiple Imputation13.5.2 Some Methods for Analyzing Data in the Presence of Missing Values 13.5.3 Some Imputation Methods 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
14.4 Estimation of Prevalence of Diseases from Screening Studies14.3.2 Simple OneStage Cluster Sampling 14.3.3 Cluster Sampling with More Than One Domain 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
15.2 History of Telephone Sampling in the United States 15.1.2 The TwentyFirst Century
15.2.1 Early Design of Telephone Surveys
15.3 WithinHousehold Selection Techniques 15.2.2 Random Digit Dialing 15.2.3 Mitofsky–Waksberg Sampling Method 15.2.4 ListAssisted Random Digit Dialing Methods
15.3.1 ProbabilityBased Methods
15.4 Steps in the Telephone Survey Process 15.3.2 QuasiProbability Methods 15.3.3 Nonprobability Methods 15.3.4 Minimally Intrusive Method
15.4.1 ComputerAssisted Telephone Interviewing
15.5 Drawing and Managing a Telephone Survey Sample 15.4.2 Quality Control in Telelphone Surveys
15.5.1 Drawing the Sample
15.6 PostSurvey Data Enhancement Procedures 15.5.2 Managing the Sample 15.5.3 Developing an Analysis File 15.5.4 Data Weighting and Adjustment
15.6.1 Data Weighting
15.7 Imputation of Missing Data 15.6.2 Steps in the Weighting Process 15.6.3 Compensation for Exclusion of Nontelephone Households 15.8 Declining Coverage and Response Rates 15.9 Addressing the Problems with Cell Phones
15.9.1 Research on Cell Phone Surveys
15.10 AddressBased Sampling 15.9.2 Sampling from the Cell Phone Frame Exercises Bibliography 16. Constructing the Survey Weights
16.1 Introduction
16.2 Objectives of Weighting
16.2.1 Basic Concepts
16.3 Constructing the Sampling Weights 16.2.2 Weighting to Reduce Frame Bias 16.2.3 Weighting to Reduce Nonresponse Bias 16.2.4 Weighting to Reduce Sampling Variance
16.3.1 Base Weights
16.4 Estimation and Analysis Issues 16.3.2 Nonresponse Adjustments 16.3.3 Frame Coverage Adjustments 16.3.4 Constructing the Final Weights
16.4.1 Effect of Weighting on the Variance
16.5 Summary 16.4.2 Using Weights in Analysis Bibliography 17. Strategies for DesignBased Analysis of Sample Survey Data
17.1 Steps Required for Performing a DesignBased Analysis
17.2 Analysis Issues for “Typical” Sample Surveys 17.3 Summary Technical Appendix Bibliography Appendix
Answers to Selected Exercises
Index

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