Applied Survey Sampling - Blair, Edward

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Résumé :
Section I: SAMPLING BASICS
Chapter 1: Introduction to Sampling
1.1 Introduction
1.2 A Brief History of Sampling
1.3 Sampling Concepts
1.3.1 Sources of Research Error
1.3.2 Probability versus Nonprobability Samples
1.4 Guidelines for Good Sampling
1.5 Chapter Summary and Overview of Book
Chapter 2: Defining and Framing the Population
2.1 Defining the Population
2.1.1 Defining Population Units
2.1.2 Setting Population Boundaries
2.2 Framing the Population
2.2.1 Obtaining a List
2.2.2 Problems With Lists
2.2.3 Coping With Omissions
2.2.4 Coping With Ineligibles
2.2.5 Coping With Duplications
2.2.6 Coping With Clustering
2.2.7 Framing Populations Without a List
2.3 Chapter Summary
Chapter 3: Drawing the Sample and Executing the
3.1 Drawing the Sample
3.1.1 Simple Random Sampling
3.1.2 Systematic Sampling
3.1.3 Physical Sampling
3.2 Executing the Research
3.2.1 Controlling Nonresponse Bias
3.2.2 Calculating Response Rates
3.3 Chapter summary
Section II: SAMPLE SIZE AND SAMPLE EFFICIENCY
Chapter 4: Setting Sample Size
4.1 Sampling Error Illustrated
4.2 Sample Size Based on Confidence Intervals
4.2.1 Computational Examples
4.2.2 How to Estimate s or p
4.3 Sample Size Based on Hypothesis Testing Power
4.4 Sample Size Based on the Value of Information
4.4.1 Why Information Has Value
4.4.2 Factors Related to the Value of Information
4.4.3 Sample Size and the Value of Information
4.5 Informal Methods for Setting Sample Size
4.5.1 Using Previous or Typical Sample Sizes
4.5.2 Using the Magic Number
4.5.3 Anticipating Subgroup Analyses
4.5.4 Using Resource Limitations
4.6 Chapter Summary
Chapter 5: Stratified Sampling
5.1 When Should Stratified Samples Be Used?
5.1.1 The Strata Are of Direct Interest
5.1.2 Variances Differ Across Strata
5.1.3 Costs Differ Across Strata
5.1.4 Prior Information Differs Across Strata
5.2 Other Uses of Stratification
5.3 How to Draw a Stratified Sample
5.4 Chapter Summary
Chapter 6: Cluster Sampling
6.1 When Are Cluster Samples Appropriate?
6.1.1 Travel Costs
6.1.2 Fixed Costs
6.1.3 Listing Costs
6.1.4 Locating Special Populations
6.2 Increased Sample Variability as a Result of Clustering
6.2.1 Measuring Homogeneity Within Clusters
6.2.2 Design Effects From Clustering
6.3 Optimum Cluster Size
6.3.1 Typical Cluster Sizes
6.4 Defining Clusters
6.5 How to Draw a Cluster Sample
6.5.1 Drawing Clusters With Equal Probabilities
6.5.2 Drawing Clusters With Probabilities Proportionate to Size
6.5.3 Drawing Stratified Cluster Samples
6.6 Chapter Summary
Section III: ADDITIONAL TOPICS IN SAMPLING
Chapter 7: Estimating Population Characteristics From Samples
7.1 Weighting Sample Data
7.1.1 Should Data Be Weighted?
7.2 Using Models to Guide Sampling and Estimation
7.2.1 Examples of Using Models
7.2.2 Using Models to Reduce the Variance of Estimates
7.2.3 Using Models to Cope With Violations of Probability Sampling Assumptions
7.2.4 Conclusions About the Use of Models
7.3 Measuring the Uncertainty of Estimates From Complex or Nonprobability Samples
7.4 Chapter Summary
Chapter 8: Sampling in Special Contexts
8.1 Sampling for Online Research
8.2 Sampling Visitors to a Place
8.2.1 Selecting Places for Intercept Research
8.2.2 Sampling Visitors Within Places
8.3 Sampling Rare Populations
8.3.1 Telephone Cluster Sampling
8.3.2 Disproportionate Stratified Sampling
8.3.3 Network Sampling

Biographie:
Section I: SAMPLING BASICS
Chapter 1: Introduction to Sampling
1.1 Introduction
1.2 A Brief History of Sampling
1.3 Sampling Concepts
1.3.1 Sources of Research Error
1.3.2 Probability versus Nonprobability Samples
1.4 Guidelines for Good Sampling
1.5 Chapter Summary and Overview of Book
Chapter 2: Defining and Framing the Population
2.1 Defining the Population
2.1.1 Defining Population Units
2.1.2 Setting Population Boundaries
2.2 Framing the Population
2.2.1 Obtaining a List
2.2.2 Problems With Lists
2.2.3 Coping With Omissions
2.2.4 Coping With Ineligibles
2.2.5 Coping With Duplications
2.2.6 Coping With Clustering
2.2.7 Framing Populations Without a List
2.3 Chapter Summary
Chapter 3: Drawing the Sample and Executing the
3.1 Drawing the Sample
3.1.1 Simple Random Sampling
3.1.2 Systematic Sampling
3.1.3 Physical Sampling
3.2 Executing the Research
3.2.1 Controlling Nonresponse Bias
3.2.2 Calculating Response Rates
3.3 Chapter summary
Section II: SAMPLE SIZE AND SAMPLE EFFICIENCY
Chapter 4: Setting Sample Size
4.1 Sampling Error Illustrated
4.2 Sample Size Based on Confidence Intervals
4.2.1 Computational Examples
4.2.2 How to Estimate s or p
4.3 Sample Size Based on Hypothesis Testing Power
4.4 Sample Size Based on the Value of Information
4.4.1 Why Information Has Value
4.4.2 Factors Related to the Value of Information
4.4.3 Sample Size and the Value of Information
4.5 Informal Methods for Setting Sample Size
4.5.1 Using Previous or Typical Sample Sizes
4.5.2 Using the Magic Number
4.5.3 Anticipating Subgroup Analyses
4.5.4 Using Resource Limitations
4.6 Chapter Summary
Chapter 5: Stratified Sampling
5.1 When Should Stratified Samples Be Used?
5.1.1 The Strata Are of Direct Interest
5.1.2 Variances Differ Across Strata
5.1.3 Costs Differ Across Strata
5.1.4 Prior Information Differs Across Strata
5.2 Other Uses of Stratification
5.3 How to Draw a Stratified Sample
5.4 Chapter Summary
Chapter 6: Cluster Sampling
6.1 When Are Cluster Samples Appropriate?
6.1.1 Travel Costs
6.1.2 Fixed Costs
6.1.3 Listing Costs
6.1.4 Locating Special Populations
6.2 Increased Sample Variability as a Result of Clustering
6.2.1 Measuring Homogeneity Within Clusters
6.2.2 Design Effects From Clustering
6.3 Optimum Cluster Size
6.3.1 Typical Cluster Sizes
6.4 Defining Clusters
6.5 How to Draw a Cluster Sample
6.5.1 Drawing Clusters With Equal Probabilities
6.5.2 Drawing Clusters With Probabilities Proportionate to Size
6.5.3 Drawing Stratified Cluster Samples
6.6 Chapter Summary
Section III: ADDITIONAL TOPICS IN SAMPLING
Chapter 7: Estimating Population Characteristics From Samples
7.1 Weighting Sample Data
7.1.1 Should Data Be Weighted?
7.2 Using Models to Guide Sampling and Estimation
7.2.1 Examples of Using Models
7.2.2 Using Models to Reduce the Variance of Estimates
7.2.3 Using Models to Cope With Violations of Probability Sampling Assumptions
7.2.4 Conclusions About the Use of Models
7.3 Measuring the Uncertainty of Estimates From Complex or Nonprobability Samples
7.4 Chapter Summary
Chapter 8: Sampling in Special Contexts
8.1 Sampling for Online Research
8.2 Sampling Visitors to a Place
8.2.1 Selecting Places for Intercept Research
8.2.2 Sampling Visitors Within Places
8.3 Sampling Rare Populations
8.3.1 Telephone Cluster Sampling
8.3.2 Disproportionate Stratified Sampling
8.3.3 Network Sampling

Sommaire:
Section I: SAMPLING BASICS
Chapter 1: Introduction to Sampling
1.1 Introduction
1.2 A Brief History of Sampling
1.3 Sampling Concepts
1.3.1 Sources of Research Error
1.3.2 Probability versus Nonprobability Samples
1.4 Guidelines for Good Sampling
1.5 Chapter Summary and Overview of Book
Chapter 2: Defining and Framing the Population
2.1 Defining the Population
2.1.1 Defining Population Units
2.1.2 Setting Population Boundaries
2.2 Framing the Population
2.2.1 Obtaining a List
2.2.2 Problems With Lists
2.2.3 Coping With Omissions
2.2.4 Coping With Ineligibles
2.2.5 Coping With Duplications
2.2.6 Coping With Clustering
2.2.7 Framing Populations Without a List
2.3 Chapter Summary
Chapter 3: Drawing the Sample and Executing the
3.1 Drawing the Sample
3.1.1 Simple Random Sampling
3.1.2 Systematic Sampling
3.1.3 Physical Sampling
3.2 Executing the Research
3.2.1 Controlling Nonresponse Bias
3.2.2 Calculating Response Rates
3.3 Chapter summary
Section II: SAMPLE SIZE AND SAMPLE EFFICIENCY
Chapter 4: Setting Sample Size
4.1 Sampling Error Illustrated
4.2 Sample Size Based on Confidence Intervals
4.2.1 Computational Examples
4.2.2 How to Estimate s or p
4.3 Sample Size Based on Hypothesis Testing Power
4.4 Sample Size Based on the Value of Information
4.4.1 Why Information Has Value
4.4.2 Factors Related to the Value of Information
4.4.3 Sample Size and the Value of Information
4.5 Informal Methods for Setting Sample Size
4.5.1 Using Previous or Typical Sample Sizes
4.5.2 Using the Magic Number
4.5.3 Anticipating Subgroup Analyses
4.5.4 Using Resource Limitations
4.6 Chapter Summary
Chapter 5: Stratified Sampling
5.1 When Should Stratified Samples Be Used?
5.1.1 The Strata Are of Direct Interest
5.1.2 Variances Differ Across Strata
5.1.3 Costs Differ Across Strata
5.1.4 Prior Information Differs Across Strata
5.2 Other Uses of Stratification
5.3 How to Draw a Stratified Sample
5.4 Chapter Summary
Chapter 6: Cluster Sampling
6.1 When Are Cluster Samples Appropriate?
6.1.1 Travel Costs
6.1.2 Fixed Costs
6.1.3 Listing Costs
6.1.4 Locating Special Populations
6.2 Increased Sample Variability as a Result of Clustering
6.2.1 Measuring Homogeneity Within Clusters
6.2.2 Design Effects From Clustering
6.3 Optimum Cluster Size
6.3.1 Typical Cluster Sizes
6.4 Defining Clusters
6.5 How to Draw a Cluster Sample
6.5.1 Drawing Clusters With Equal Probabilities
6.5.2 Drawing Clusters With Probabilities Proportionate to Size
6.5.3 Drawing Stratified Cluster Samples
6.6 Chapter Summary
Section III: ADDITIONAL TOPICS IN SAMPLING
Chapter 7: Estimating Population Characteristics From Samples
7.1 Weighting Sample Data
7.1.1 Should Data Be Weighted?
7.2 Using Models to Guide Sampling and Estimation
7.2.1 Examples of Using Models
7.2.2 Using Models to Reduce the Variance of Estimates
7.2.3 Using Models to Cope With Violations of Probability Sampling Assumptions
7.2.4 Conclusions About the Use of Models
7.3 Measuring the Uncertainty of Estimates From Complex or Nonprobability Samples
7.4 Chapter Summary
Chapter 8: Sampling in Special Contexts
8.1 Sampling for Online Research
8.2 Sampling Visitors to a Place
8.2.1 Selecting Places for Intercept Research
8.2.2 Sampling Visitors Within Places
8.3 Sampling Rare Populations
8.3.1 Telephone Cluster Sampling
8.3.2 Disproportionate Stratified Sampling
8.3.3 Network Sampling