Total survey error in practice : improving quality in the era of bid data /: improving quality in the era of bid data. (2016)

• Record Type: Book
• Further Information: Note: Edited by Paul P. Biemer [and seven others].
• Editors: Biemer, Paul P
• Contents: 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 Smartphone Participation in Web Surveys 206 10.3 Smartphone Participation Choices 209 10.4 Instrument Design Choices 212 10.5 Device and Design Treatment Choices 216 10.6 Conclusion 218 10.7 Future Challenges and Research Needs 219 Appendix 10.A: Data Sources 220 Appendix 10.B: Smartphone Prevalence in Web Surveys 221 Appendix 10.C: Screen Captures from Peterson et al. (2013) Experiment 225 Appendix 10.D: Survey Questions Used in the Analysis of the Peterson et al. (2013) Experiment 229 References 231 11 Survey Research and the Quality of Survey Data Among Ethnic Minorities 235 ; Joost Kappelhof 11.1 Introduction 235 11.2 On the Use of the Terms Ethnicity and Ethnic Minorities 236 11.3 On the Representation of Ethnic Minorities in Surveys 237 11.4 Measurement Issues 242 11.5 Comparability, Timeliness, and Cost Concerns 244 11.6 Conclusion 247 References 248 Section 3 Data Collection and Data Processing Applications 253 12 Measurement Error in Survey Operations Management: Detection, Quantification, Visualization, and Reduction 255 ; Brad Edwards, Aaron Maitland, and Sue Connor 12.1 TSE Background on Survey Operations 256 12.2 Better and Better: Using Behavior Coding (CARIcode) and Paradata to Evaluate and Improve Question (Specification) Error and Interviewer Error 257 12.3 Field-Centered Design: Mobile App for Rapid Reporting and Management 261 12.4 Faster and Cheaper: Detecting Falsification With GIS Tools 265 12.5 Putting It All Together: Field Supervisor Dashboards 268 12.6 Discussion 273 References 275 13 Total Survey Error for Longitudinal Surveys 279 ; Peter Lynn and Peter J. Lugtig 13.1 Introduction 279 13.2 Distinctive Aspects of Longitudinal Surveys 280 13.3 TSE Components in Longitudinal Surveys 281 13.4 Design of Longitudinal Surveys from a TSE Perspective 285 13.5 Examples of Tradeoffs in Three Longitudinal Surveys 290 13.6 Discussion 294 References 295 14 Text Interviews on Mobile Devices 299 ; Frederick G. Conrad, Michael F. Schober, Christopher Antoun, Andrew L. Hupp, and H. Yanna Yan 14.1 Texting as a Way of Interacting 300 14.2 Contacting and Inviting Potential Respondents through Text 303 14.3 Texting as an Interview Mode 303 14.4 Costs and Efficiency of Text Interviewing 312 14.5 Discussion 314 References 315 15 Quantifying Measurement Errors in Partially Edited Business Survey Data 319 ; Thomas Laitila, Karin Lindgren, Anders Norberg, and Can Tongur 15.1 Introduction 319 15.2 Selective Editing 320 15.3 Effects of Errors Remaining After SE 325 15.4 Case Study: Foreign Trade in Goods Within the European Union 328 15.5 Editing Big Data 334 15.6 Conclusions 335 References 335 Section 4 Evaluation and Improvement 339 16 Estimating Error Rates in an Administrative Register and Survey Questions Using a Latent Class Model 341 ; Daniel L. Oberski 16.1 Introduction 341 16.2 Administrative an
• Edition: 1st
• Publisher Details: Hoboken, New Jersey : John Wiley & Sons, Inc
• Publication Date: 2016
• Extent: 1 online resource
• Subjects: 001.433; Error analysis (Mathematics); Surveys
• Languages: English
• ISBNs: 9781119041696
• Related ISBNs: 9781119041689
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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