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E-raamat: Adaptive Survey Design

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(University of Michigan, Institute for Social Research, Ann Arbor, USA), (University of Michigan, Ann Arbor, USA), (Statistics Netherlands, The Hague, The Netherlands)
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Adaptive survey designs (ASDs) provide a framework for data-driven tailoring of data collection procedures to different sample members, often for cost and bias reduction. People vary in how likely they are to respond and in how they respond. This variation leads to opportunities to selectively deploy design features in order to control both nonresponse and measurement errors. ASD aims at the optimal matching of design features and the characteristics of respondents given the survey budget. Such a goal is sensible, but ASD requires investment in more advanced technical systems and management infrastructure and asks for the collection of relevant auxiliary data. So what are current best practices in ASD? And is ASD worthwhile when the same auxiliary data are employed in the estimation afterwards? In this book, the authors provide answers to these questions, and much more.

Arvustused

"Adaptive Survey Design is a mathematically rigorous discussion of a new set of tools for managing surveys more efficiently, written by three of the worlds leading experts on these tools. Surveys around the world face growing challengeshigher costs, lower response rates, use of mixed modes of data collectionand adaptive designs have emerged as one response to this more difficult climate. This book presents a thorough explanation of adaptive survey design and makes a convincing case for the value of such designs. The book represents an important contribution to the literature on survey design. " Roger Tourangeau, Vice President and Associate Director, Westat

"This book will be an invaluable resource for survey managers, survey statisticians, and survey methods students. It is set to become the landmark text on adaptive survey design. While adaptive designs have come of age and are now being used or tested by many survey agencies, there has been a lack of clear or consistent guidance. This book provides just such guidance, while also being refreshingly honest about aspects that still require more research. The practical experience of the authors shines through, particularly in the case studies. These, and the simple summaries at the end of each chapter, should prove most useful to anyone wanting to implement an adaptive design of their own. This book is a very useful addition to the survey methods literature." Professor Peter Lynn, University of Essex

"This is the first comprehensive book that is dedicated solely to such designs and I have no doubt that it will become the authoritative book on the subject with a lasting legacy effect. The book culminates many years of research by the authors with their colleagues and co-authors, and is a one-stop source for those interested in implementing such designs. It is a valuable up-to-date and accessible resource for both statisticians and survey practitioners

Acknowledgments xi
Authors xiii
Section I Introduction to Adaptive Survey Design
1 Introduction
3(6)
1.1 Why a Book?
3(1)
1.2 Intended Audience and Assumed Prior Knowledge
4(1)
1.3 Outline of the Book
5(4)
2 Adaptive Survey Design: What Is It?
9(28)
2.1 Introduction
9(10)
2.1.1 Survey Costs
10(1)
2.1.2 Survey Errors
11(2)
2.1.3 Other Challenges
13(2)
2.1.4 Need for More Flexible Survey Designs to Address Uncertainty in Data Collection
15(2)
2.1.5 Common Survey Design Paradigm
17(1)
2.1.6 New Opportunities
18(1)
2.2 ASD and RD
19(7)
2.2.1 Adaptive Survey Design
20(4)
2.2.2 Responsive Design
24(1)
2.2.3 RD with ASD Features
25(1)
2.3 Objectives of ASDs
26(2)
2.4 Example Case Studies
28(4)
2.4.1 National Intimate Partner and Sexual Violence Surveys
28(1)
2.4.1.1 Propensity-Based Assignment to Interviewers
28(1)
2.4.1.2 Propensity-Based Stopping of Sample Cases ("Interactive Case Management")
29(1)
2.4.1.3 Phase Duration
29(1)
2.4.2 The Dutch Labor Force Survey
30(1)
2.4.3 National Survey of Family Growth
31(1)
2.5 Summary
32(5)
Section II Preparing an Adaptive Survey Design
3 Stratification
37(20)
3.1 Introduction
37(2)
3.2 Goals of Stratification
39(2)
3.3 Defining Strata
41(11)
3.3.1 Response Propensity Variation
42(1)
3.3.2 Regression Diagnostics
43(3)
3.3.3 Simulation
46(1)
3.3.4 Other Methods for Creating Strata
47(1)
3.3.5 Examples
47(1)
3.3.5.1 The National Survey of Family Growth
47(2)
3.3.5.2 Labor Force Survey
49(2)
3.3.6 Summary and Recommendations
51(1)
3.4 Available Data
52(3)
3.4.1 Sampling Frames
52(1)
3.4.2 Commercial Data
53(1)
3.4.3 Paradata
54(1)
3.5 Summary
55(2)
4 Interventions and Design Features
57(20)
4.1 Overview
57(3)
4.2 The Interventions
60(4)
4.3 The Dosage
64(1)
4.4 The Sequence
65(2)
4.5 Examples
67(6)
4.5.1 The National Survey of Family Growth
67(4)
4.5.1.1 Case Prioritization
71(1)
4.5.1.2 Phased Design Features
71(1)
4.5.1.3 Interviewer-Level Management Intervention
72(1)
4.5.2 The Dutch LFS
73(1)
4.6 Conclusion
73(4)
5 Models for Nonresponse in Adaptive Survey Design
77(12)
5.1 Introduction
77(1)
5.2 Goals of Statistical Models in ASD
78(1)
5.3 Models
78(4)
5.3.1 Reasons for Using Models for Nonresponse
78(2)
5.3.2 Key Components in Models for Nonresponse
80(2)
5.4 Monitoring Nonresponse
82(4)
5.4.1 Survey-Level Measures
83(1)
5.4.2 Estimate-Level Measures
84(2)
5.5 Summary
86(3)
Section III Implementing an Adaptive Survey Design
6 Costs and Logistics
89(22)
6.1 Overview
89(1)
6.2 Costs
90(9)
6.2.1 Cost Models
91(5)
6.2.2 Cost Model Parameter Estimation
96(3)
6.3 Logistics
99(5)
6.3.1 Stages of Implementing Adaptive Survey Designs
100(1)
6.3.2 Monitoring
101(3)
6.4 Examples
104(5)
6.4.1 The Dutch LFS-Continued
104(2)
6.4.2 NSFG--Continued
106(3)
6.5 Summary
109(2)
7 Optimization of Adaptive Survey Design
111(14)
7.1 Introduction
111(2)
7.2 Approaches for ASD Optimization
113(1)
7.3 Numerical Optimization Problems
114(5)
7.3.1 Mathematical and Statistical Optimization
114(2)
7.3.2 Simulation on Existing Data
116(3)
7.4 Trial and Error
119(4)
7.5 Summary
123(2)
8 Robustness of Adaptive Survey Designs
125(18)
8.1 Introduction
125(3)
8.2 Metrics to Assess the Robustness of ASDs
128(2)
8.3 Sensitivity Analyses
130(6)
8.3.1 Strategies to Evaluate Robustness of Designs
130(1)
8.3.2 The Dutch LFS: An Example
131(5)
8.4 Bayesian Adaptive Survey Design Network
136(3)
8.5 Summary
139(4)
Section IV Advanced Features of Adaptive Survey Design
9 Indicators to Support Prioritization and Optimization
143(34)
9.1 Introduction
143(2)
9.2 Overall Indicators
145(9)
9.2.1 Type 1 Indicators (Based on Covariates Only)
145(6)
9.2.2 Type 2 Indicators (Based on Covariates and Survey Variables)
151(3)
9.3 Indicators Decomposing the Variance of Response Propensities
154(5)
9.3.1 Partial Variable Level
155(3)
9.3.2 Partial Category Level
158(1)
9.4 Nonresponse Bias
159(11)
9.4.1 Response Probabilities and Propensities
159(5)
9.4.2 Bias Approximations of Unadjusted and Adjusted Response Means
164(2)
9.4.3 Bias Intervals under Not-Missing-at-Random Nonresponse
166(4)
9.5 Indicators and Their Relation to Nonresponse Bias
170(4)
9.6 Summary
174(3)
10 Adaptive Survey Design and Adjustment for Nonresponse
177(22)
10.1 Introduction
177(2)
10.2 Empirical Evidence for Bias Reduction After Adjustment
179(6)
10.3 Theoretical Conditions for Bias Reduction After Adjustment
185(2)
10.4 Adjustment of Nonresponse to ASDs
187(5)
10.5 Example
192(4)
10.6 Summary
196(3)
Section V The Future of Adaptive Survey Design
11 Adaptive Survey Design and Measurement Error
199(24)
11.1 Introduction
199(2)
11.2 Single-Purpose Surveys
201(12)
11.2.1 Framework
202(2)
11.2.2 Mathematical Optimization
204(5)
11.2.3 Example
209(4)
11.3 Multi-Purpose Surveys and Panels
213(8)
11.3.1 Response Quality Indicators and Propensities
213(2)
11.3.2 Quality and Cost Functions Based on Response Quality Propensities
215(2)
11.3.3 Mathematical Optimization
217(1)
11.3.4 Example
218(3)
11.4 Summary
221(2)
12 The Future of Adaptive Survey Design
223(8)
References 231(12)
Index 243
Andy Peytchev is a research assistant professor in the University of Michigans Program in Survey Methodology and the Joint Program in Survey Methodology at the University of Maryland.

Barry Schouten is senior methodologist at Statistics Netherlands and professor at Utrecht University.

James Wagner is research associate professor in the University of Michigans Program in Survey Methodology and the Joint Program in Survey Methodology at the University of Maryland.
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