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E-raamat: Applied Survey Sampling

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 02-Dec-2014
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781483355146
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Applied Survey SamplingSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 02-Dec-2014
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781483355146
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Written for students and researchers who wish to understand the conceptual and practical aspects of sampling but are not necessarily statisticians, this accessible guide covers a wide range of topics, from the basics of sampling to special topics such as sampling rare populations, sampling organizational populations, and sampling visitors to a place. Using cases and examples to illustrate key findings, the book addresses recent changes in the survey environment, including declining response rates, the rise of Internet surveys, the need to accommodate cell phones in telephone surveys, and emerging uses of social media and big data.



Written for students and researchers who wish to understand the conceptual and practical aspects of sampling, Applied Survey Sampling, by Edward A. Blair and Johnny Blair, is designed to be accessible without requiring advanced statistical training. It covers a wide range of topics, from the basics of sampling to special topics such as sampling rare populations, sampling organizational populations, and sampling visitors to a place. Using cases and examples to illustrate sampling principles and procedures, the book thoroughly covers the fundamentals of modern survey sampling, and addresses recent changes in the survey environment such as declining response rates, the rise of Internet surveys, the need to accommodate cell phones in telephone surveys, and emerging uses of social media and big data.

Preface xiii
About the Authors xv
SECTION I SAMPLING BASICS
1(88)
Chapter 1 Introduction to Sampling
3(24)
1.1 Introduction
3(2)
1.2 A Brief History of Sampling
5(3)
1.3 Sampling Concepts
8(11)
1.3.1 Sources of Research Error
9(2)
1.3.2 Probability versus Nonprobability Samples
11(1)
Types of Probability Samples
11(1)
Calculating Sampling Probabilities
12(4)
Types of Nonprobability Samples
16(1)
Comparing Probability and Nonprobability Samples
17(2)
1.4 Guidelines for Good Sampling
19(2)
1.5
Chapter Summary and Overview of Book
21(6)
Exercises and Discussion Questions
24(3)
Chapter 2 Defining and Framing the Population
27(36)
2.1 Defining the Population
27(6)
2.1.1 Defining Population Units
28(2)
2.1.2 Setting Population Boundaries
30(1)
The Need for Operational Specificity in Population Boundaries
30(1)
Other Issues in Setting Population Boundaries
31(2)
2.2 Framing the Population
33(27)
2.2.1 Obtaining a List
33(4)
2.2.2 Problems With Lists
37(3)
2.2.3 Coping With Omissions
40(1)
Random Digit Dialing
40(5)
Incorporating Cellphones
45(2)
Address-Based Sampling
47(1)
Registration-Based Sampling
48(1)
Half-Open Intervals
49(1)
Dual-Frame Designs
50(2)
General Comments on Coping With Omissions
52(1)
2.2.4 Coping With Ineligibles
52(3)
2.2.5 Coping With Duplications
55(1)
2.2.6 Coping With Clustering
56(2)
Sampling Within Households
58(1)
Weighting Data to the Proper Population Unit
59(1)
2.2.7 Framing Populations Without a List
59(1)
2.3
Chapter Summary
60(3)
Exercises and Discussion Questions
61(2)
Chapter 3 Drawing the Sample and Executing the Research
63(26)
3.1 Drawing the Sample
63(10)
3.1.1 Simple Random Sampling
64(4)
3.1.2 Systematic Sampling
68(3)
3.1.3 Physical Sampling
71(1)
Sampling From Directories
71(1)
Sampling From File Drawers
72(1)
3.2 Executing the Research
73(14)
3.2.1 Controlling Nonresponse Bias
75(1)
Maximizing Response Rates
75(1)
Quota Sampling
76(2)
Probability Sampling With Quotas
78(2)
Weighting for Differential Response Rates
80(3)
Comparing Early Versus Late Respondents
83(1)
Follow-up Studies of Nonrespondents
83(1)
3.2.2 Calculating Response Rates
84(3)
3.3
Chapter Summary
87(2)
Exercises and Discussion Questions
87(2)
SECTION II SAMPLE SIZE AND SAMPLE EFFICIENCY
89(66)
Chapter 4 Setting Sample Size
91(22)
4.1 Sampling Error Illustrated
91(5)
4.2 Sample Size Based on Confidence Intervals
96(5)
4.2.1 Computational Examples
98(2)
4.2.2 How to Estimate σ or p
100(1)
4.3 Sample Size Based on Hypothesis Testing Power
101(1)
4.4 Sample Size Based on the Value of Information
102(4)
4.4.1 Why Information Has Value
103(1)
4.4.2 Factors Related to the Value of Information
104(1)
4.4.3 Sample Size and the Value of Information
105(1)
4.5 Informal Methods for Setting Sample Size
106(4)
4.5.1 Using Previous or Typical Sample Sizes
106(2)
4.5.2 Using the Magic Number
108(1)
4.5.3 Anticipating Subgroup Analyses
109(1)
4.5.4 Using Resource Limitations
109(1)
4.6
Chapter Summary
110(3)
Exercises and Discussion Questions
111(2)
Chapter 5 Stratified Sampling
113(16)
5.1 When Should Stratified Samples Be Used?
113(9)
5.1.1 The Strata Are of Direct Interest
115(1)
5.1.2 Variances Differ Across Strata
116(4)
5.1.3 Costs Differ Across Strata
120(2)
5.1.4 Prior Information Differs Across Strata
122(1)
5.2 Other Uses of Stratification
122(2)
5.3 How to Draw a Stratified Sample
124(2)
5.4
Chapter Summary
126(3)
Exercises and Discussion Questions
127(2)
Chapter 6 Cluster Sampling
129(26)
6.1 When Are Cluster Samples Appropriate?
131(3)
6.1.1 Travel Costs
131(1)
6.1.2 Fixed Costs
132(1)
6.1.3 Listing Costs
132(1)
6.1.4 Locating Special Populations
133(1)
6.2 Increased Sample Variability as a Result of Clustering
134(3)
6.2.1 Measuring Homogeneity Within Clusters
135(1)
6.2.2 Design Effects From Clustering
136(1)
6.3 Optimum Cluster Size
137(5)
6.3.1 Typical Cluster Sizes
140(1)
In-Home Surveys
140(1)
Repetitive Studies
140(1)
Shopping Mall Studies
140(1)
Graduate Student Projects
141(1)
Clustering Within Households
141(1)
6.4 Defining Clusters
142(2)
6.5 How to Draw a Cluster Sample
144(9)
6.5.1 Drawing Clusters With Equal Probabilities
144(1)
6.5.2 Drawing Clusters With Probabilities Proportionate to Size
145(6)
6.5.3 Drawing Stratified Cluster Samples
151(2)
6.6
Chapter Summary
153(2)
Exercises and Discussion Questions
154(1)
SECTION III ADDITIONAL TOPICS IN SAMPLING
155(78)
Chapter 7 Estimating Population Characteristics From Samples
157(22)
7.1 Weighting Sample Data
158(7)
7.1.1 Should Data Be Weighted?
161(4)
7.2 Using Models to Guide Sampling and Estimation
165(9)
7.2.1 Examples of Using Models
166(2)
7.2.2 Using Models to Reduce the Variance of Estimates
168(1)
Sample Allocation in Stratified Probability Designs
168(1)
Cutoff Sampling
169(1)
Small Area Estimation
170(1)
7.2.3 Using Models to Cope With Violations of Probability Sampling Assumptions
171(1)
Dealing With the Lack of an Adequate Frame
171(1)
Dealing With High Nonresponse
171(1)
Making Estimates for Nonfinite Populations
172(1)
7.2.4 Conclusions About the Use of Models
172(2)
7.3 Measuring the Uncertainty of Estimates From Complex or Nonprobability Samples
174(1)
7.4
Chapter Summary
175(4)
Exercises and Discussion Questions
177(2)
Chapter 8 Sampling in Special Contexts
179(42)
8.1 Sampling for Online Research
179(2)
8.2 Sampling Visitors to a Place
181(7)
8.2.1 Selecting Places for Intercept Research
182(1)
8.2.2 Sampling Visitors Within Places
183(5)
8.3 Sampling Rare Populations
188(11)
8.3.1 Telephone Cluster Sampling
189(2)
8.3.2 Disproportionate Stratified Sampling
191(2)
8.3.3 Network Sampling
193(2)
8.3.4 Dual-Frame Sampling
195(2)
8.3.5 Location Sampling
197(1)
8.3.6 Online Data Collection for Rare Groups
198(1)
8.4 Sampling Organizational Populations
199(1)
8.5 Sampling Groups Such as Influence Groups or Elites
200(1)
8.6 Panel Sampling
201(5)
8.6.1 Initial Nonresponse in Panels
202(1)
8.6.2 Differential Mortality Over Time
202(1)
8.6.3 Panel Aging
203(1)
8.6.4 Implications for Panel Sampling
203(2)
8.6.5 Other Issues in Panel Sampling
205(1)
8.7 Sampling in International Contexts
206(2)
8.8 Big Data and Survey Sampling
208(3)
8.8.1 Big Data as a Survey Complement
208(1)
8.8.2 Big Data as a Survey Replacement
209(2)
8.9 Incorporating Smartphones, Social Media, and Technological Changes
211(5)
8.9.1 Smartphones and Surveys
212(1)
8.9.2 Social Media and Surveys
213(2)
8.9.3 A General Framework for Incorporating New Technologies
215(1)
8.10
Chapter Summary
216(5)
Exercises and Discussion Questions
219(2)
Chapter 9 Evaluating Samples
221(12)
9.1 The Sample Report
221(3)
9.2 How Good Must the Sample Be?
224(7)
9.2.1 Concepts of Representation and Error
224(2)
9.2.2 Requirements for Sample Quality Across Research Contexts
226(1)
Imperfect Samples May Be Useful for Exploration or Screening
227(1)
Imperfect Samples May Be Useful for Testing Relationships
227(2)
Imperfect Samples Are Usable in Academic Research
229(1)
The Heaviest Burden on Sample Quality
230(1)
General Advice
230(1)
9.3
Chapter Summary
231(2)
Exercises and Discussion Questions
232(1)
References 233(6)
Author Index 239(2)
Subject Index 241
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.







Johnny Blair is an independent consultant. Previously, he was a principal scientist and senior survey methodologist at Abt Associates Inc., where he directed the Cognitive Testing Laboratory. He has conducted research on sampling rare populations, measurement error in proxy reporting, and cognitive interviewing for pretesting survey instruments. He has been a member of the Design and Analysis Committee, which provides statistical advice for the National Assessment of Educational Progress (NAEP), often referred to as The Nations Report Card. He has served on National Research Council panels to assess major government-sponsored surveys. His research publications include many book chapters and over 50 articles in academic journals and in the Proceedings of the Joint Statistical Meetings of the American Statistical Association Section on Survey Methods. He served two terms on the editorial board of Public Opinion Quarterly and is a frequent peer reviewer for several other research journals.





 
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