{"product_id":"total-survey-error-in-practice-isbn-9781119041672","title":"Total Survey Error in Practice","description":"\u003cp\u003e\u003cb\u003eFeaturing 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\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eThis book:\u003c\/p\u003e \u003cp\u003e• 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\u003c\/p\u003e \u003cp\u003e• Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects\u003c\/p\u003e \u003cp\u003e• 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\u003c\/p\u003e \u003cp\u003e• 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\u003c\/p\u003e \u003cp\u003e\u003ci\u003eTotal Survey Error in Practice \u003c\/i\u003eis 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.\u003c\/p\u003e \u003cp\u003eNotes on Contributors xix\u003c\/p\u003e \u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 1 The Concept of TSE and the TSE Paradigm 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Roots and Evolution of the Total Survey Error Concept 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLars E. Lyberg and Diana Maria Stukel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction and Historical Backdrop 3\u003c\/p\u003e \u003cp\u003e1.2 Specific Error Sources and Their Control or Evaluation 5\u003c\/p\u003e \u003cp\u003e1.3 Survey Models and Total Survey Design 10\u003c\/p\u003e \u003cp\u003e1.4 The Advent of More Systematic Approaches Toward Survey Quality 12\u003c\/p\u003e \u003cp\u003e1.5 What the Future Will Bring 16\u003c\/p\u003e \u003cp\u003eReferences 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Total Twitter Error: Decomposing Public Opinion Measurement on Twitter from a Total Survey Error Perspective 23\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYuli Patrick Hsieh and Joe Murphy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 23\u003c\/p\u003e \u003cp\u003e2.2 Social Media: An Evolving Online Public Sphere 25\u003c\/p\u003e \u003cp\u003e2.3 Components of Twitter Error 27\u003c\/p\u003e \u003cp\u003e2.4 Studying Public Opinion on the Twittersphere and the Potential Error Sources of Twitter Data: Two Case Studies 31\u003c\/p\u003e \u003cp\u003e2.5 Discussion 40\u003c\/p\u003e \u003cp\u003e2.6 Conclusion 42\u003c\/p\u003e \u003cp\u003eReferences 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Big Data: A Survey Research Perspective 47\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eReg Baker\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Definitions 48\u003c\/p\u003e \u003cp\u003e3.3 The Analytic Challenge: From Database Marketing to Big Data and Data Science 56\u003c\/p\u003e \u003cp\u003e3.4 Assessing Data Quality 58\u003c\/p\u003e \u003cp\u003e3.5 Applications in Market, Opinion, and Social Research 59\u003c\/p\u003e \u003cp\u003e3.6 The Ethics of Research Using Big Data 62\u003c\/p\u003e \u003cp\u003e3.7 The Future of Surveys in a Data-Rich Environment 62\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Role of Statistical Disclosure Limitation in Total Survey Error 71\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlan F. Karr\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 71\u003c\/p\u003e \u003cp\u003e4.2 Primer on SDL 72\u003c\/p\u003e \u003cp\u003e4.3 TSE-Aware SDL 75\u003c\/p\u003e \u003cp\u003e4.4 Edit-Respecting SDL 79\u003c\/p\u003e \u003cp\u003e4.5 SDL-Aware TSE 83\u003c\/p\u003e \u003cp\u003e4.6 Full Unification of Edit, Imputation, and SDL 84\u003c\/p\u003e \u003cp\u003e4.7 “Big Data” Issues 87\u003c\/p\u003e \u003cp\u003e4.8 Conclusion 89\u003c\/p\u003e \u003cp\u003eAcknowledgments 91\u003c\/p\u003e \u003cp\u003eReferences 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 2 Implications for Survey Design 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The Undercoverage–Nonresponse Tradeoff 97\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eStephanie Eckman and Frauke Kreuter\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 97\u003c\/p\u003e \u003cp\u003e5.2 Examples of the Tradeoff 98\u003c\/p\u003e \u003cp\u003e5.3 Simple Demonstration of the Tradeoff 99\u003c\/p\u003e \u003cp\u003e5.4 Coverage and Response Propensities and Bias 100\u003c\/p\u003e \u003cp\u003e5.5 Simulation Study of Rates and Bias 102\u003c\/p\u003e \u003cp\u003e5.6 Costs 110\u003c\/p\u003e \u003cp\u003e5.7 Lessons for Survey Practice 111\u003c\/p\u003e \u003cp\u003eReferences 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Mixing Modes: Tradeoffs Among Coverage, Nonresponse, and Measurement Error 115\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRoger Tourangeau\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 115\u003c\/p\u003e \u003cp\u003e6.2 The Effect of Offering a Choice of Modes 118\u003c\/p\u003e \u003cp\u003e6.3 Getting People to Respond Online 119\u003c\/p\u003e \u003cp\u003e6.4 Sequencing Different Modes of Data Collection 120\u003c\/p\u003e \u003cp\u003e6.5 Separating the Effects of Mode on Selection and Reporting 122\u003c\/p\u003e \u003cp\u003e6.6 Maximizing Comparability Versus Minimizing Error 127\u003c\/p\u003e \u003cp\u003e6.7 Conclusions 129\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Mobile Web Surveys: A Total Survey Error Perspective 133\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMick P. Couper, Christopher Antoun, and Aigul Mavletova\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 133\u003c\/p\u003e \u003cp\u003e7.2 Coverage 135\u003c\/p\u003e \u003cp\u003e7.3 Nonresponse 137\u003c\/p\u003e \u003cp\u003e7.4 Measurement Error 142\u003c\/p\u003e \u003cp\u003e7.5 Links Between Different Error Sources 148\u003c\/p\u003e \u003cp\u003e7.6 The Future of Mobile Web Surveys 149\u003c\/p\u003e \u003cp\u003eReferences 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 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\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJames Wagner, Brady T. West, Heidi Guyer, Paul Burton, Jennifer Kelley, Mick P. Couper, and William D. Mosher\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 155\u003c\/p\u003e \u003cp\u003e8.2 Literature Review: Incentives in Face-to-Face Surveys 156\u003c\/p\u003e \u003cp\u003e8.3 Data and Methods 159\u003c\/p\u003e \u003cp\u003e8.4 Results 163\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 173\u003c\/p\u003e \u003cp\u003eReferences 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 A Total Survey Error Perspective on Surveys in Multinational, Multiregional, and Multicultural Contexts 179\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBeth-Ellen Pennell, Kristen Cibelli Hibben, Lars E. Lyberg, Peter Ph. Mohler, and Gelaye Worku\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 179\u003c\/p\u003e \u003cp\u003e9.2 TSE in Multinational, Multiregional, and Multicultural Surveys 180\u003c\/p\u003e \u003cp\u003e9.3 Challenges Related to Representation and Measurement Error Components in Comparative Surveys 184\u003c\/p\u003e \u003cp\u003e9.4 QA and QC in 3MC Surveys 192\u003c\/p\u003e \u003cp\u003eReferences 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Smartphone Participation in Web Surveys: Choosing Between the Potential for Coverage, Nonresponse, and Measurement Error 203\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGregg Peterson, Jamie Griffin, John LaFrance, and JiaoJiao Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 203\u003c\/p\u003e \u003cp\u003e10.2 Prevalence of Smartphone Participation in Web Surveys 206\u003c\/p\u003e \u003cp\u003e10.3 Smartphone Participation Choices 209\u003c\/p\u003e \u003cp\u003e10.4 Instrument Design Choices 212\u003c\/p\u003e \u003cp\u003e10.5 Device and Design Treatment Choices 216\u003c\/p\u003e \u003cp\u003e10.6 Conclusion 218\u003c\/p\u003e \u003cp\u003e10.7 Future Challenges and Research Needs 219\u003c\/p\u003e \u003cp\u003eAppendix 10.A: Data Sources 220\u003c\/p\u003e \u003cp\u003eAppendix 10.B: Smartphone Prevalence in Web Surveys 221\u003c\/p\u003e \u003cp\u003eAppendix 10.C: Screen Captures from Peterson et al. (2013) Experiment 225\u003c\/p\u003e \u003cp\u003eAppendix 10.D: Survey Questions Used in the Analysis of the Peterson et al. (2013) Experiment 229\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Survey Research and the Quality of Survey Data Among Ethnic Minorities 235\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJoost Kappelhof\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 235\u003c\/p\u003e \u003cp\u003e11.2 On the Use of the Terms Ethnicity and Ethnic Minorities 236\u003c\/p\u003e \u003cp\u003e11.3 On the Representation of Ethnic Minorities in Surveys 237 Ethnic Minorities 241\u003c\/p\u003e \u003cp\u003e11.4 Measurement Issues 242\u003c\/p\u003e \u003cp\u003e11.5 Comparability, Timeliness, and Cost Concerns 244\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 247\u003c\/p\u003e \u003cp\u003eReferences 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 3 Data Collection and Data Processing Applications 253\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Measurement Error in Survey Operations Management: Detection, Quantification, Visualization, and Reduction 255\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBrad Edwards, Aaron Maitland, and Sue Connor\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 TSE Background on Survey Operations 256\u003c\/p\u003e \u003cp\u003e12.2 Better and Better: Using Behavior Coding (CARIcode) and Paradata to Evaluate and Improve Question (Specification) Error and Interviewer Error 257\u003c\/p\u003e \u003cp\u003e12.3 Field-Centered Design: Mobile App for Rapid Reporting and Management 261\u003c\/p\u003e \u003cp\u003e12.4 Faster and Cheaper: Detecting Falsification With GIS Tools 265\u003c\/p\u003e \u003cp\u003e12.5 Putting It All Together: Field Supervisor Dashboards 268\u003c\/p\u003e \u003cp\u003e12.6 Discussion 273\u003c\/p\u003e \u003cp\u003eReferences 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Total Survey Error for Longitudinal Surveys 279\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePeter Lynn and Peter J. Lugtig\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 279\u003c\/p\u003e \u003cp\u003e13.2 Distinctive Aspects of Longitudinal Surveys 280\u003c\/p\u003e \u003cp\u003e13.3 TSE Components in Longitudinal Surveys 281\u003c\/p\u003e \u003cp\u003e13.4 Design of Longitudinal Surveys from a TSE Perspective 285\u003c\/p\u003e \u003cp\u003e13.5 Examples of Tradeoffs in Three Longitudinal Surveys 290\u003c\/p\u003e \u003cp\u003e13.6 Discussion 294\u003c\/p\u003e \u003cp\u003eReferences 295\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Text Interviews on Mobile Devices 299\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFrederick G. Conrad, Michael F. Schober, Christopher Antoun, Andrew L. Hupp, and H. Yanna Yan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Texting as a Way of Interacting 300\u003c\/p\u003e \u003cp\u003e14.2 Contacting and Inviting Potential Respondents through Text 303\u003c\/p\u003e \u003cp\u003e14.3 Texting as an Interview Mode 303\u003c\/p\u003e \u003cp\u003e14.4 Costs and Efficiency of Text Interviewing 312\u003c\/p\u003e \u003cp\u003e14.5 Discussion 314\u003c\/p\u003e \u003cp\u003eReferences 315\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Quantifying Measurement Errors in Partially Edited Business Survey Data 319\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThomas Laitila, Karin Lindgren, Anders Norberg, and Can Tongur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 319\u003c\/p\u003e \u003cp\u003e15.2 Selective Editing 320\u003c\/p\u003e \u003cp\u003e15.3 Effects of Errors Remaining After SE 325\u003c\/p\u003e \u003cp\u003e15.4 Case Study: Foreign Trade in Goods Within the European Union 328\u003c\/p\u003e \u003cp\u003e15.5 Editing Big Data 334\u003c\/p\u003e \u003cp\u003e15.6 Conclusions 335\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 4 Evaluation and Improvement 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Estimating Error Rates in an Administrative Register and Survey Questions Using a Latent Class Model 341\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDaniel L. Oberski\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 341\u003c\/p\u003e \u003cp\u003e16.2 Administrative and Survey Measures of Neighborhood 342\u003c\/p\u003e \u003cp\u003e16.3 A Latent Class Model for Neighborhood of Residence 345\u003c\/p\u003e \u003cp\u003e16.4 Results 348\u003c\/p\u003e \u003cp\u003eAppendix 16.A: Program Input and Data 355\u003c\/p\u003e \u003cp\u003eAcknowledgments 357\u003c\/p\u003e \u003cp\u003eReferences 357\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 ASPIRE: An Approach for Evaluating and Reducing the Total Error in Statistical Products with Application to Registers and the National Accounts 359\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePaul P. Biemer, Dennis Trewin, Heather Bergdahl, and Yingfu Xie\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction and Background 359\u003c\/p\u003e \u003cp\u003e17.2 Overview of ASPIRE 360\u003c\/p\u003e \u003cp\u003e17.3 The ASPIRE Model 362\u003c\/p\u003e \u003cp\u003e17.4 Evaluation of Registers 367\u003c\/p\u003e \u003cp\u003e17.5 National Accounts 371\u003c\/p\u003e \u003cp\u003e17.6 A Sensitivity Analysis of GDP Error Sources 376\u003c\/p\u003e \u003cp\u003e17.7 Concluding Remarks 379\u003c\/p\u003e \u003cp\u003eAppendix 17.A: Accuracy Dimension Checklist 381\u003c\/p\u003e \u003cp\u003eReferences 384\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Classification Error in Crime Victimization Surveys: A Markov Latent Class Analysis 387\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMarcus E. Berzofsky and Paul P. Biemer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 387\u003c\/p\u003e \u003cp\u003e18.2 Background 389\u003c\/p\u003e \u003cp\u003e18.3 Analytic Approach 392\u003c\/p\u003e \u003cp\u003e18.4 Model Selection 396\u003c\/p\u003e \u003cp\u003e18.5 Results 399\u003c\/p\u003e \u003cp\u003e18.6 Discussion and Summary of Findings 404\u003c\/p\u003e \u003cp\u003e18.7 Conclusions 407\u003c\/p\u003e \u003cp\u003eAppendix 18.A: Derivation of the Composite False-Negative Rate 407\u003c\/p\u003e \u003cp\u003eAppendix 18.B: Derivation of the Lower Bound for False-Negative Rates from a Composite Measure 408\u003c\/p\u003e \u003cp\u003eAppendix 18.C: Examples of Latent GOLD Syntax 408\u003c\/p\u003e \u003cp\u003eReferences 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Using Doorstep Concerns Data to Evaluate and Correct for Nonresponse Error in a Longitudinal Survey 413\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTing Yan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 413\u003c\/p\u003e \u003cp\u003e19.2 Data and Methods 416\u003c\/p\u003e \u003cp\u003e19.3 Results 418\u003c\/p\u003e \u003cp\u003e19.4 Discussion 428\u003c\/p\u003e \u003cp\u003eAcknowledgment 430\u003c\/p\u003e \u003cp\u003eReferences 430\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Total Survey Error Assessment for Sociodemographic Subgroups in the 2012 U.S. National Immunization Survey 433\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKirk M. Wolter, Vicki J. Pineau, Benjamin Skalland, Wei Zeng, James A. Singleton, Meena Khare, Zhen Zhao, David Yankey, and Philip J. Smith\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 433\u003c\/p\u003e \u003cp\u003e20.2 TSE Model Framework 434\u003c\/p\u003e \u003cp\u003e20.3 Overview of the National Immunization Survey 437\u003c\/p\u003e \u003cp\u003e20.4 National Immunization Survey: Inputs for TSE Model 440\u003c\/p\u003e \u003cp\u003e20.5 National Immunization Survey TSE Analysis 445\u003c\/p\u003e \u003cp\u003e20.6 Summary 452\u003c\/p\u003e \u003cp\u003eReferences 453\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Establishing Infrastructure for the Use of Big Data to Understand Total Survey Error: Examples from Four Survey Research Organizations Overview 457\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBrady T. West\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Big Data Infrastructure at the Institute for Employment Research (IAB) \u003c\/b\u003e\u003cb\u003e458\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAntje Kirchner, Daniela Hochfellner, Stefan Bender\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAcknowledgments 464\u003c\/p\u003e \u003cp\u003eReferences 464\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Using Administrative Records Data at the U.S. Census Bureau: Lessons Learned from Two Research Projects Evaluating Survey Data \u003c\/b\u003e\u003cb\u003e467\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eElizabeth M. Nichols, Mary H. Mulry, and Jennifer Hunter Childs\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAcknowledgments and Disclaimers 472\u003c\/p\u003e \u003cp\u003eReferences 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3 Statistics New Zealand’s Approach to Making Use of Alternative Data Sources in a New Era of Integrated Data \u003c\/b\u003e\u003cb\u003e474\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnders Holmberg and Christine Bycroft\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 478\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4 Big Data Serving Survey Research: Experiences at the University of Michigan Survey Research Center \u003c\/b\u003e\u003cb\u003e478\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGrant Benson and Frost Hubbard\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eAcknowledgments and Disclaimers 484\u003c\/p\u003e \u003cp\u003eReferences 484\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSection 5 Estimation and Analysis 487\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Analytic Error as an Important Component of Total Survey Error: Results from a Meta-Analysis 489\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBrady T. West, Joseph W. Sakshaug, and Yumi Kim\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Overview 489\u003c\/p\u003e \u003cp\u003e22.2 Analytic Error as a Component of TSE 490\u003c\/p\u003e \u003cp\u003e22.3 Appropriate Analytic Methods for Survey Data 492\u003c\/p\u003e \u003cp\u003e22.4 Methods 495\u003c\/p\u003e \u003cp\u003e22.5 Results 497\u003c\/p\u003e \u003cp\u003e22.6 Discussion 505\u003c\/p\u003e \u003cp\u003eAcknowledgments 508\u003c\/p\u003e \u003cp\u003eReferences 508\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Mixed-Mode Research: Issues in Design and Analysis 511\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJoop Hox, Edith de Leeuw, and Thomas Klausch\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 511\u003c\/p\u003e \u003cp\u003e23.2 Designing Mixed-Mode Surveys 512\u003c\/p\u003e \u003cp\u003e23.3 Literature Overview 514\u003c\/p\u003e \u003cp\u003e23.4 Diagnosing Sources of Error in Mixed-Mode Surveys 516\u003c\/p\u003e \u003cp\u003e23.5 Adjusting for Mode Measurement Effects 523\u003c\/p\u003e \u003cp\u003e23.6 Conclusion 527\u003c\/p\u003e \u003cp\u003eReferences 528\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 The Effect of Nonresponse and Measurement Error on Wage Regression across Survey Modes: A Validation Study 531\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAntje Kirchner and Barbara Felderer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 531\u003c\/p\u003e \u003cp\u003e24.2 Nonresponse and Response Bias in Survey Statistics 532\u003c\/p\u003e \u003cp\u003e24.3 Data and Methods 534\u003c\/p\u003e \u003cp\u003e24.4 Results 541\u003c\/p\u003e \u003cp\u003e24.5 Summary and Conclusion 546\u003c\/p\u003e \u003cp\u003eAcknowledgments 547\u003c\/p\u003e \u003cp\u003eAppendix 24.A 548\u003c\/p\u003e \u003cp\u003eAppendix 24.B 549\u003c\/p\u003e \u003cp\u003eReferences 554\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Errors in Linking Survey and Administrative Data 557\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJoseph W. Sakshaug and Manfred Antoni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 557\u003c\/p\u003e \u003cp\u003e25.2 Conceptual Framework of Linkage and Error Sources 559\u003c\/p\u003e \u003cp\u003e25.3 Errors Due to Linkage Consent 561\u003c\/p\u003e \u003cp\u003e25.4 Erroneous Linkage with Unique Identifiers 565\u003c\/p\u003e \u003cp\u003e25.5 Erroneous Linkage with Nonunique Identifiers 567\u003c\/p\u003e \u003cp\u003e25.6 Applications and Practical Guidance 568\u003c\/p\u003e \u003cp\u003e25.7 Conclusions and Take-Home Points 571\u003c\/p\u003e \u003cp\u003eReferences 571\u003c\/p\u003e \u003cp\u003eIndex 575\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePaul P. Biemer, PhD, \u003c\/b\u003eis distinguished fellow at RTI International and associate director of Survey Research and Development at the Odum Institute, University of North Carolina, USA.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEdith de Leeuw, PhD, \u003c\/b\u003eis professor of survey methodology in the Department of Methodology and Statistics at Utrecht University, the Netherlands.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eStephanie Eckman, PhD, \u003c\/b\u003eis fellow at RTI International, USA.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBrad Edwards \u003c\/b\u003eis vice president, director of Field Services, and deputy area director at Westat, USA.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eFrauke Kreuter, PhD, \u003c\/b\u003eis 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eLars E. Lyberg, PhD, \u003c\/b\u003eis senior advisor at Inizio, Sweden.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eN. Clyde Tucker, PhD, \u003c\/b\u003eis principal survey methodologist at the American Institutes for Research, USA.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBrady T. West, PhD, \u003c\/b\u003eis 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eFeaturing 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\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eThis book:\u003c\/p\u003e \u003cp\u003e• 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\u003c\/p\u003e \u003cp\u003e• Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects\u003c\/p\u003e \u003cp\u003e• 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\u003c\/p\u003e \u003cp\u003e• 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\u003c\/p\u003e \u003cp\u003e\u003ci\u003eTotal Survey Error in Practice \u003c\/i\u003eis 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990397501669,"sku":"NP9781119041672","price":123.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119041672.jpg?v=1761787665","url":"https:\/\/k12savings.com\/products\/total-survey-error-in-practice-isbn-9781119041672","provider":"K12savings","version":"1.0","type":"link"}