Total Survey Error in Practice
Improving Quality in the Era of Big Data
(Sprache: Englisch)
Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data sets
This book provides an overview of the TSE...
This book provides an overview of the TSE...
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Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data setsThis book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation, and analysis. It recognizes that survey data affects many public policy and business decisions and thus focuses on the framework for understanding and improving survey data quality. The book also addresses issues with data quality in official statistics and in social, opinion, and market research as these fields continue to evolve, leading to larger and messier data sets. This perspective challenges survey organizations to find ways to collect and process data more efficiently without sacrificing quality. The volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The Concept of TSE and the TSE Paradigm, Implications for Survey Design, Data Collection and Data Processing Applications, Evaluation and Improvement, and Estimation and Analysis. Each chapter introduces and examines multiple error sources, such as sampling error, measurement error, and nonresponse error, which often offer the greatest risks to data quality, while also encouraging readers not to lose sight of the less commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with larger total error.
This book:
* Features various error sources, and the complex relationships between them, in 25 high-quality chapters on the most up-to-date research in the field of TSE
* Provides comprehensive
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reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects
* Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors
* Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research
Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods.
Paul P. Biemer, PhD, is distinguished fellow at RTI International and associate director of Survey Research and Development at the Odum Institute, University of North Carolina, USA.
Edith de Leeuw, PhD, is professor of survey methodology in the Department of Methodology and Statistics at Utrecht University, the Netherlands.
Stephanie Eckman, PhD, is fellow at RTI International, USA.
Brad Edwards is vice president, director of Field Services, and deputy area director at Westat, USA.
Frauke Kreuter, PhD, is professor and director of the Joint Program in Survey Methodology, University of Maryland, USA; professor of statistics and methodology at the University of Mannheim, Germany; and head of the Statistical Methods Research Department at the Institute for Employment Research, Germany.
Lars E. Lyberg, PhD, is senior advisor at Inizio, Sweden.
N. Clyde Tucker, PhD, is principal survey methodologist at the American Institutes for Research, USA.
Brady T. West, PhD, is research associate professor in the Survey Research Center, locate
* Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors
* Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research
Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods.
Paul P. Biemer, PhD, is distinguished fellow at RTI International and associate director of Survey Research and Development at the Odum Institute, University of North Carolina, USA.
Edith de Leeuw, PhD, is professor of survey methodology in the Department of Methodology and Statistics at Utrecht University, the Netherlands.
Stephanie Eckman, PhD, is fellow at RTI International, USA.
Brad Edwards is vice president, director of Field Services, and deputy area director at Westat, USA.
Frauke Kreuter, PhD, is professor and director of the Joint Program in Survey Methodology, University of Maryland, USA; professor of statistics and methodology at the University of Mannheim, Germany; and head of the Statistical Methods Research Department at the Institute for Employment Research, Germany.
Lars E. Lyberg, PhD, is senior advisor at Inizio, Sweden.
N. Clyde Tucker, PhD, is principal survey methodologist at the American Institutes for Research, USA.
Brady T. West, PhD, is research associate professor in the Survey Research Center, locate
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Inhaltsverzeichnis zu „Total Survey Error in Practice “
Notes on Contributors xixPreface xxv
Section 1 The Concept of TSE and the TSE Paradigm 1
1 The Roots and Evolution of the Total Survey Error Concept 3
Lars E. Lyberg and Diana Maria Stukel
1.1 Introduction and Historical Backdrop 3
1.2 Specific Error Sources and Their Control or Evaluation 5
1.3 Survey Models and Total Survey Design 10
1.4 The Advent of More Systematic Approaches Toward Survey Quality 12
1.5 What the Future Will Bring 16
References 18
2 Total Twitter Error: Decomposing Public Opinion Measurement on Twitter from a Total Survey Error Perspective 23
Yuli Patrick Hsieh and Joe Murphy
2.1 Introduction 23
2.3 Components of Twitter Error 27
2.4 Studying Public Opinion on the Twittersphere and the Potential Error Sources of Twitter Data: Two Case Studies 31
2.5 Discussion 40
2.6 Conclusion 42
References 43
3 Big Data: A Survey Research Perspective 47
Reg Baker
3.1 Introduction 47
3.2 Definitions 48
3.3 The Analytic Challenge: From Database Marketing to Big Data and Data Science 56
3.4 Assessing Data Quality 58
3.5 Applications in Market, Opinion, and Social Research 59
3.6 The Ethics of Research Using Big Data 62
3.7 The Future of Surveys in a Data-Rich Environment 62
References 65
4 The Role of Statistical Disclosure Limitation in Total Survey Error 71
Alan F. Karr
4.1 Introduction 71
4.2 Primer on SDL 72
4.3 TSE-Aware SDL 75
4.4 Edit-Respecting SDL 79
4.5 SDL-Aware TSE 83
4.6 Full Unification of Edit, Imputation, and SDL 84
4.7 "Big Data" Issues 87
4.8 Conclusion 89
Acknowledgments 91
References 92
Section 2 Implications for Survey Design 95
5 The Undercoverage-Nonresponse Tradeoff 97
Stephanie Eckman and Frauke
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Kreuter
5.1 Introduction 97
5.2 Examples of the Tradeoff 98
5.3 Simple Demonstration of the Tradeoff 99
5.4 Coverage and Response Propensities and Bias 100
5.5 Simulation Study of Rates and Bias 102
5.6 Costs 110
5.7 Lessons for Survey Practice 111
References 112
6 Mixing Modes: Tradeoffs Among Coverage, Nonresponse, and Measurement Error 115
Roger Tourangeau
6.1 Introduction 115
6.2 The Effect of Offering a Choice of Modes 118
6.3 Getting People to Respond Online 119
6.4 Sequencing Different Modes of Data Collection 120
6.5 Separating the Effects of Mode on Selection and Reporting 122
6.6 Maximizing Comparability Versus Minimizing Error 127
6.7 Conclusions 129
References 130
7 Mobile Web Surveys: A Total Survey Error Perspective 133
Mick P. Couper, Christopher Antoun, and Aigul Mavletova
7.1 Introduction 133
7.2 Coverage 135
7.3 Nonresponse 137
7.4 Measurement Error 142
7.5 Links Between Different Error Sources 148
7.6 The Future of Mobile Web Surveys 149
References 150
8 The Effects of a Mid-Data Collection Change in Financial Incentives on Total Survey Error in the National Survey of Family Growth: Results from a Randomized Experiment 155
James Wagner, Brady T. West, Heidi Guyer, Paul Burton, Jennifer Kelley, Mick P. Couper, and William D. Mosher
8.1 Introduction 155
8.2 Literature Review: Incentives in Face-to-Face Surveys 156
8.3 Data and Methods 159
8.4 Results 163
8.5 Conclusion 173
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5.1 Introduction 97
5.2 Examples of the Tradeoff 98
5.3 Simple Demonstration of the Tradeoff 99
5.4 Coverage and Response Propensities and Bias 100
5.5 Simulation Study of Rates and Bias 102
5.6 Costs 110
5.7 Lessons for Survey Practice 111
References 112
6 Mixing Modes: Tradeoffs Among Coverage, Nonresponse, and Measurement Error 115
Roger Tourangeau
6.1 Introduction 115
6.2 The Effect of Offering a Choice of Modes 118
6.3 Getting People to Respond Online 119
6.4 Sequencing Different Modes of Data Collection 120
6.5 Separating the Effects of Mode on Selection and Reporting 122
6.6 Maximizing Comparability Versus Minimizing Error 127
6.7 Conclusions 129
References 130
7 Mobile Web Surveys: A Total Survey Error Perspective 133
Mick P. Couper, Christopher Antoun, and Aigul Mavletova
7.1 Introduction 133
7.2 Coverage 135
7.3 Nonresponse 137
7.4 Measurement Error 142
7.5 Links Between Different Error Sources 148
7.6 The Future of Mobile Web Surveys 149
References 150
8 The Effects of a Mid-Data Collection Change in Financial Incentives on Total Survey Error in the National Survey of Family Growth: Results from a Randomized Experiment 155
James Wagner, Brady T. West, Heidi Guyer, Paul Burton, Jennifer Kelley, Mick P. Couper, and William D. Mosher
8.1 Introduction 155
8.2 Literature Review: Incentives in Face-to-Face Surveys 156
8.3 Data and Methods 159
8.4 Results 163
8.5 Conclusion 173
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Autoren-Porträt
Paul P. Biemer, PhD, is distinguished fellow at RTI International and associate director of Survey Research and Development at the Odum Institute, University of North Carolina, USA.Edith de Leeuw, PhD, is professor of survey methodology in the Department of Methodology and Statistics at Utrecht University, the Netherlands.
Stephanie Eckman, PhD, is fellow at RTI International, USA.
Brad Edwards is vice president, director of Field Services, and deputy area director at Westat, USA.
Frauke Kreuter, PhD, is professor and director of the Joint Program in Survey Methodology, University of Maryland, USA; professor of statistics and methodology at the University of Mannheim, Germany; and head of the Statistical Methods Research Department at the Institute for Employment Research, Germany.
Lars E. Lyberg, PhD, is senior advisor at Inizio, Sweden.
N. Clyde Tucker, PhD, is principal survey methodologist at the American Institutes for Research, USA.
Brady T. West, PhD, is research associate professor in the Survey Research Center, located within the Institute for Social Research at the University of Michigan (U-M), and also serves as statistical consultant on the Consulting for Statistics, Computing and Analytics Research (CSCAR) team at U-M, USA.
Bibliographische Angaben
- 2017, 1. Auflage, 624 Seiten, Maße: 18,1 x 26,4 cm, Gebunden, Englisch
- Herausgegeben: Paul P. Biemer, Edith D. de Leeuw, Stephanie Eckman, Brad Edwards, Frauke Kreuter, Lars E. Lyberg, N. Clyde Tucker, Brady T. West
- Verlag: Wiley & Sons
- ISBN-10: 1119041678
- ISBN-13: 9781119041672
Sprache:
Englisch
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