Total Survey Error in Practice (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
624
Utgivningsdatum
2017-02-10
Upplaga
1
Förlag
Wiley-Blackwell
Medarbetare
West, Brady T. (red.)/Tucker, N. Clyde (red.)/Eckman, Stephanie (red.)
Dimensioner
254 x 184 x 38 mm
Vikt
1247 g
Antal komponenter
1
ISBN
9781119041672
Total Survey Error in Practice (inbunden)

Total Survey Error in Practice

Improving Quality in the Era of Big Data

Inbunden Engelska, 2017-02-10
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An edited volume for an upcoming conference on Total Survey Error (TSE), this book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation and analysis.
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Övrig information

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.

Innehållsförteckning

Notes on Contributors xix Preface 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 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 References 175 9 A Total Survey Error Perspective on Surveys in Multinational, Multiregional, and Multicultural Contexts 179 Beth-Ellen Pennell, Kristen Cibelli Hibben, Lars E. Lyberg, Peter Ph. Mohler, and Gelaye Worku 9.1 Introduction 179 9.2 TSE in Multinational, Multiregional, and Multicultural Surveys 180 9.3 Challenges Related to Representation and Measurement Error Components in Comparative Surveys 184 9.4 QA and QC in 3MC Surveys 192 References 196 10 Smartphone Participation in Web Surveys: Choosing Between the Potential for Coverage, Nonresponse, and Measurement Error 203 Gregg Peterson, Jamie Griffin, John LaFrance, and JiaoJiao Li 10.1 Introduction 203 10.2 Prevalence of