Edward Blair, Johnny Blair

Applied Survey Sampling

• Broschiertes Buch

Produktbeschreibung

Providing readers with non-technical and applied explanations of both conceptual and practical aspects of sampling, this book covers the changing technology landscape of survey research and big data.

Produktdetails

• Verlag: SAGE Publications Inc
• Seitenzahl: 272
• Erscheinungstermin: 16. Dezember 2014
• Englisch
• Abmessung: 231mm x 192mm x 17mm
• Gewicht: 454g
• ISBN-13: 9781483334332
• ISBN-10: 1483334333
• Artikelnr.: 41601384

Autorenporträt

Edward Blair is the Michael J. Cemo professor of marketing and entrepreneurship and chair of the Department of Marketing and Entrepreneurship in the Bauer College of Business at the University of Houston. He has been chair of the American Statistical Association Committee on Energy Statistics, which advises the U.S. Energy Information Administration on statistical matters, and previously served on the U.S. Census Bureau Advisory Committee. He has been a National Science Foundation panelist, national conference chair for the American Marketing Association, editorial board member for Journal of Marketing Research, Journal of the Academy of Marketing Science, and Journal of Business Research, and instructor in sampling and survey methods for the American Marketing Association School of Marketing Research. His research interests include survey sampling and cognitive aspects of survey methodology.

Inhaltsangabe

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
8.3.4 Dual-Frame Sampling
8.3.5 Location Sampling
8.3.6 Online Data Collection for Rare Groups
8.4 Sampling Organizational Populations
8.5 Sampling Groups Such as Influence Groups or Elites
8.6 Panel Sampling
8.6.1 Initial Nonresponse in Panels
8.6.2 Differential Mortality Over Time
8.6.3 Panel Aging
8.6.4 Implications for Panel Sampling
8.6.5 Other Issues in Panel Sampling
8.7 Sampling in International Contexts
8.8 Big Data and Survey Sampling
8.8.1 Big Data as a Survey Complement
8.8.2 Big Data as a Survey Replacement
8.9 Incorporating Smartphones, Social Media, and Technological Changes
8.9.1 Smartphones and Surveys
8.9.2 Social Media and Surveys
8.9.3 A General Framework for Incorporating New Technologies
8.10 Chapter Summary
Chapter 9: Evaluating Samples
9.1 The Sample Report
9.2 How Good Must the Sample Be?
9.2.1 Concepts of Representation and Error
9.2.2 Requirements for Sample Quality Across Research Contexts
9.3 Chapter Summary