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.

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 3Lars 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 23Yuli Patrick Hsieh and Joe Murphy 2.1 Introduction 23 2.2 Social Media: An Evolving Online Public Sphere 25 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 47Reg 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 71Alan 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 97Stephanie 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 115Roger 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 133Mick 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 155James 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 179Beth-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 203Gregg 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 235Joost 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 Ethnic Minorities 241 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 255Brad 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 279Peter 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 299Frederick 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 319Thomas 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 341Daniel L. Oberski 16.1 Introduction 341 16.2 Administrative and Survey Measures of Neighborhood 342 16.3 A Latent Class Model for Neighborhood of Residence 345 16.4 Results 348 Appendix 16.A: Program Input and Data 355 Acknowledgments 357 References 357 17 ASPIRE: An Approach for Evaluating and Reducing the Total Error in Statistical Products with Application to Registers and the National Accounts 359Paul P. Biemer, Dennis Trewin, Heather Bergdahl, and Yingfu Xie 17.1 Introduction and Background 359 17.2 Overview of ASPIRE 360 17.3 The ASPIRE Model 362 17.4 Evaluation of Registers 367 17.5 National Accounts 371 17.6 A Sensitivity Analysis of GDP Error Sources 376 17.7 Concluding Remarks 379 Appendix 17.A: Accuracy Dimension Checklist 381 References 384 18 Classification Error in Crime Victimization Surveys: A Markov Latent Class Analysis 387Marcus E. Berzofsky and Paul P. Biemer 18.1 Introduction 387 18.2 Background 389 18.3 Analytic Approach 392 18.4 Model Selection 396 18.5 Results 399 18.6 Discussion and Summary of Findings 404 18.7 Conclusions 407 Appendix 18.A: Derivation of the Composite False-Negative Rate 407 Appendix 18.B: Derivation of the Lower Bound for False-Negative Rates from a Composite Measure 408 Appendix 18.C: Examples of Latent GOLD Syntax 408 References 410 19 Using Doorstep Concerns Data to Evaluate and Correct for Nonresponse Error in a Longitudinal Survey 413Ting Yan 19.1 Introduction 413 19.2 Data and Methods 416 19.3 Results 418 19.4 Discussion 428 Acknowledgment 430 References 430 20 Total Survey Error Assessment for Sociodemographic Subgroups in the 2012 U.S. National Immunization Survey 433Kirk M. Wolter, Vicki J. Pineau, Benjamin Skalland, Wei Zeng, James A. Singleton, Meena Khare, Zhen Zhao, David Yankey, and Philip J. Smith 20.1 Introduction 433 20.2 TSE Model Framework 434 20.3 Overview of the National Immunization Survey 437 20.4 National Immunization Survey: Inputs for TSE Model 440 20.5 National Immunization Survey TSE Analysis 445 20.6 Summary 452 References 453 21 Establishing Infrastructure for the Use of Big Data to Understand Total Survey Error: Examples from Four Survey Research Organizations Overview 457Brady T. West Part 1 Big Data Infrastructure at the Institute for Employment Research (IAB) 458Antje Kirchner, Daniela Hochfellner, Stefan Bender Acknowledgments 464 References 464 Part 2 Using Administrative Records Data at the U.S. Census Bureau: Lessons Learned from Two Research Projects Evaluating Survey Data 467Elizabeth M. Nichols, Mary H. Mulry, and Jennifer Hunter Childs Acknowledgments and Disclaimers 472 References 472 Part 3 Statistics New Zealand's Approach to Making Use of Alternative Data Sources in a New Era of Integrated Data 474Anders Holmberg and Christine Bycroft References 478 Part 4 Big Data Serving Survey Research: Experiences at the University of Michigan Survey Research Center 478Grant Benson and Frost Hubbard Acknowledgments and Disclaimers 484 References 484 Section 5 Estimation and Analysis 487 22 Analytic Error as an Important Component of Total Survey Error: Results from a Meta-Analysis 489Brady T. West, Joseph W. Sakshaug, and Yumi Kim 22.1 Overview 489 22.2 Analytic Error as a Component of TSE 490 22.3 Appropriate Analytic Methods for Survey Data 492 22.4 Methods 495 22.5 Results 497 22.6 Discussion 505 Acknowledgments 508 References 508 23 Mixed-Mode Research: Issues...