Total survey error in practice:
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| Format: | Book |
| Language: | English |
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Hoboken, NJ
Wiley
[2017]
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| Series: | Wiley series in survey methodology
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| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xxvii, 593 Seiten Illustrationen, Diagramme |
| ISBN: | 9781119041672 |
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| 245 | 1 | 0 | |a Total survey error in practice |c edited by Paul P. Biemer, Edith de Leeuw, Stephanie Eckman, Brad Edwards, Frauke Kreuter, Lars E. Lyberg, N. Clyde Tucker, Brady T. West |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c [2017] | |
| 264 | 4 | |c © 2017 | |
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| 700 | 1 | |a Eckman, Stephanie |4 edt | |
| 700 | 1 | |a Edwards, Brad |4 edt | |
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| 700 | 1 | |a Lyberg, Lars |4 edt | |
| 700 | 1 | |a Tucker, Clyde |0 (DE-588)171589785 |4 edt | |
| 700 | 1 | |a West, Brady T. |4 edt | |
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Record in the Search Index
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| adam_text | 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
Yuii Patrick Hsieh and Joe Murphy
2.1 Introduction 23
2.1.1 Social Media: A Potential Alternative to Surveys? 23
2.1.2 TSE as a Launching Point for Evaluating Social Media Error 24
2.2 Social Media: An Evolving Online Public Sphere 25
2.2.1 Nature, Norms, and Usage Behaviors of Twitter 25
2.2.2 Research on Public Opinion on Twitter 26
2.3 Components of Twitter Error 27
2.3.1 Coverage Error 28
2.3.2 Query Error 28
2.3.3 Interpretation Error 29
2.3.4 The Deviation of Unstructured Data Errors from TSE 30
2.4 Studying Public Opinion on the Twittersphere and the Potential Error Sources
of Twitter Data: Two Case Studies 31
2.4.1 Research Questions and Methodology of Twitter Data Analysis 32
2.4.2 Potential Coverage Error in Twitter Examples 33
2.4.3 Potential Query Error in Twitter Examples 36
2.4.3.1 Implications of Including or Excluding RTs for Error 36
2.4.3.2 Implications of Query Iterations for Error 37
Contents
2.4.4 Potential Interpretation Error in Twitter Examples 39
2.5 Discussion 40
2.5.1 A Framework That Better Describes Twitter Data Errors 40
2.5.2 Other Subclasses of Errors to Be Investigated 41
2.6 Conclusion 42
2.6.1 What Advice We Offer for Researchers and Research Consumers 42
2.6.2 Directions for Future Research 42
References 43
3 Big Data: A Survey Research Perspective 47
Reg Baker
3.1 Introduction 47
3.2 Definitions 48
3.2.1 Sources 49
3.2.2 Attributes 49
3.2.2.1 Volume 50
3.2.2.2 Variety 50
3.2.2.3 Velocity 50
3.2.2.4 Veracity 50
3.2.2.5 Variability 52
3.2.2.6 Value 52
3.2.2.7 Visualization 52
3.2.3 The Making of Big Data 52
3.3 The Analytic Challenge: From Database Marketing to Big Data and Data Science 56
3.4 Assessing Data Quality 58
3.4.1 Validity 53
3.4.2 Missingness 59
3.4.3 Representation 59
3.5 Applications in Market, Opinion, and Social Research 59
3.5.1 Adding Value through Linkage 60
3.5.2 Combining Big Data and Surveys in Market Research 61
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
4.3.1 Additive Noise 75
4.3.2 Data Swapping 78
4.4 Edit-Respecting SDL 79
4.4.1 Simulation Experiment 80
4.4.2 A Deeper Issue 82
4.5 SDL-Aware TSE 83
4.6 Full Unification of Edit, Imputation, and SDL
4.7 “Big Data” Issues 87
84
Contents I vH
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
5.5.1 Simulation Setup 102
5.5.2 Results for Coverage and Response Rates 105
5.5.3 Results for Undercoverage and Nonresponse Bias 106
5.5.3.1 Scenario 1 107
5.5.3.2 Scenario 2 108
5.5.3.3 Scenario 3 108
5.5.3.4 Scenario 4 109
5.5.3.5 Scenario 7 109
5.5.4 Summary of Simulation Results 110
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
6.5.1 Conceptualizing Mode Effects 122
6.5.2 Separating Observation from Nonobservation Error 123
6.5.2.1 Direct Assessment of Measurement Errors 123
6.5.2.2 Statistical Adjustments 124
6.5.2.3 Modeling Measurement Error 126
6.6 Maximizing Comparability Versus Minimizing Error 127
6.7 Conclusions 129
References 130
1 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
Contents
7.3 Nonresponse 137
7.3.1 Unit Nonresponse 137
7.3.2 Breakoffs 139
7.3.3 Completion Times 140
7.3.4 Compliance with Special Requests 141
7.4 Measurement Error 142
7.4.1 Grouping of Questions 143
7.4.1.1 Question-Order Effects 143
7.4.1.2 Number of Items on a Page 143
7.4.1.3 Grids versus Item-By-Item 143
7.4.2 Effects of Question Type 145
7.4.2.1 Socially Undesirable Questions 145
7.4.2.2 Open-Ended Questions 146
7.4.3 Response and Scale Effects 146
7.4.3.1 Primacy Effects 146
7.4.3.2 Slider Bars and Drop-Down Questions 147
7.4.3.3 Scale Orientation 147
7.4.4 Item Missing Data 148
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
8.2.1 Nonresponse Rates 156
8.2.2 Nonresponse Bias 157
8.2.3 Measurement Error 158
8.2.4 Survey Costs 159
8.2.5 Summary 159
8.3 Data and Methods 159
8.3.1 NSFG Design: Overview 159
8.3.2 Design of Incentive Experiment 161
8.3.3 Variables 161
8.3.4 Statistical Analysis 162
8.4 Results 163
8.4.1 Nonresponse Error 163
8.4.2 Sampling Error and Costs 166
8.4.3 Measurement Error 170
8.5 Conclusion 173
8.5.1 Summary 173
8.5.2 Recommendations for Practice 174
References 175
9 A Total Survey Error Perspective on Surveys in Multinational, Multiregionai,
and Multicultural Contexts 179
Beth’Ellen Pennell, Kristen Cibelli Hlbben, Lars E. Lyberg, Peter Ph. Mohler,
and Gelaye Worku
9.1 Introduction 179
9.2 TSE in Multinational, Multiregionai, and Multicultural Surveys 180
9.3 Challenges Related to Representation and Measurement Error Components
in Comparative Surveys 184
9.3.1 Representation Error 184
9.3.1.1 Coverage Error 184
9.3.1.2 Sampling Error 185
9.3.1.3 Unit Nonresponse Error 186
9.3.1.4 Adjustment Error 187
9.3.2 Measurement Error 187
9.3.2.1 Validity 188
9322 Measurement Error - The Response Process 188
9.3.2.3 Processing Error 191
9.4 QA and QC in 3MC Surveys 192
9.4.1 The Importance of a Solid Infrastructure 192
9.4.2 Examples of QA and QC Approaches Practiced by Some 3MC Surveys 193
9.4.3 QA/QC Recommendations 295
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
10.1.1 Focus on Smartphones 204
10.1.2 Smartphone Participation: Web-Survey Design Decision Tree 204
10.1.3 Chapter Outline 205
10.2 Prevalence of Smartphone Participation in Web Surveys 206
10.3 Smartphone Participation Choices 209
10.3.1 Disallowing Smartphone Participation 209
10.3.2 Discouraging Smartphone Participation 211
10.4 Instrument Design Choices 212
10.4.1 Doing Nothing 213
10.4.2 Optimizing for Smartphones 213
10.5 Device and Design Treatment Choices 216
10.5.1 PC/Legacy versus Smartphone Designs 216
10.5.2 PC/Legacy versus PC/New 216
10.5.3 Smartphone/Legacy versus Smartphone/New 217
10.5.4 Device and Design Treatment Options 217
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
Contents
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
11.3.1 Coverage of Ethnic Minorities 238
11.3.2 Factors Affecting Nonresponse Among Ethnic Minorities 239
11.3.3 Postsurvey Adjustment Issues Related to Surveys Among
Ethnic Minorities 241
11.4 Measurement Issues 242
11.4.1 The Tradeoff When Using Response-Enhancing Measures 243
11.5 Comparability, Timeliness, and Cost Concerns 244
11.5.1 Comparability 245
11.5.2 Timeliness and Cost Considerations 246
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
12.2.1 CARI Coding at Westat 259
12.2.2 CARI Experiments 260
12.3 Field-Centered Design: Mobile App for Rapid Reporting and Management 261
12.3.1 Mobile App Case Study 262
12.3.2 Paradata Quality 264
12.4 Faster and Cheaper: Detecting Falsification With GIS Tools 265
12.5 Putting It All Together: Field Supervisor Dashboards 268
12.5.1 Dashboards in Operations 268
12.5.2 Survey Research Dashboards 269
12.5.2.1 Dashboards and Paradata 269
12.5.2.2 Relationship to TSE 269
12.5.3 The Stovepipe Problem 270
12.5.4 The Dashboard Solution 270
12.5.5 Case Study 270
12.5.5.1 Single Sign-On 270
12.5.5.2 Alerts 271
12.5.5.3 General Dashboard Design 271
12.6 Discussion 273
References 275
13 Total Survey Error for Longitudinal Surveys 279
Peter Lynn and Peter 1 Lugtig
13.1 Introduction 279
13.2 Distinctive Aspects of Longitudina! Surveys 280
13.3 TSE Components in Longitudinal Surveys 281
13.4 Design of Longitudinal Surveys from a TSE Perspective 285
13.4.1 Is the Panel Study Fixed-Time or Open-Ended? 286
13.4.2 Who To Follow Over Time? 286
13.4.3 Should the Survey Use Interviewers or Be Self-Administered? 287
13.4.4 How Long Should Between-Wave Intervals Be? 288
13.4.5 How Should Longitudinal Instruments Be Designed? 289
13.5 Examples of Tradeoffs in Three Longitudinal Surveys 290
13.5.1 Tradeoff between Coverage, Sampling and Nonresponse Error
in LISS Panel 290
13.5.2 Tradeoff between Nonresponse and Measurement Error in BHPS 292
13.5.3 Tradeoff between Specification and Measurement Error in SIPP 293
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
14.1.1 Properties and Aifordances 300
14.1.1.1 Stable Properties 300
14.1.1.2 Properties That Vary across Devices and Networks 301
14.2 Contacting and Inviting Potential Respondents through Text 303
14.3 Texting as an Interview Mode 303
14.3.1 Coverage and Sampling Error 304
14.3.2 Nonresponse Error 307
14.3.3 Measurement Error: Conscientious Responding and Disclosure
in Texting Interviews 308
14.3.4 Measurement Error: Interface Design for Texting Interviews 310
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
15.2.1 Editing and Measurement Error 320
15.2.2 Definition and the General Idea of Selective Editing 321
15.2.3 Selekt 322
15.2.4 Experiences from Implementations of Selekt 323
15.3 Effects of Errors Remaining After SE 325
15.3.1 Sampling Below the Threshold: The Two-Step Procedure 326
15.3.2 Randomness of Measurement Errors 326
Contents
15.3.3 Modeling and Estimation of Measurement Errors 327
15.3.4 Output Editing 328
15.4 Case Study: Foreign Trade in Goods Within the European Union 328
15.4.1 Sampling Below the Cutoff Threshold for Editing 330
15.4.2 Results 330
15.4.3 Comments on Results 332
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 Ober$ki
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
16.4.1 Model Fit 348
16.4.2 Error Rate Estimates 350
16.5 Discussion and Conclusion 354
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 359
Paul 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
17.3.1 Decomposition of the TSE into Component Error Sources 362
17.3.2 Risk Classification 364
17.3.3 Criteria for Assessing Quality 364
17.3.4 Ratings System 365
17.4 Evaluation of Registers 367
17.4.1 Types of Registers 367
17.4.2 Error Sources Associated with Registers 368
17.4.3 Application of ASPIRE to the TPR 370
17.5 National Accounts 371
17.5.1 Error Sources Associated with the NA 372
17.5.2 Application of ASPIRE to the Quarterly Swedish NA 374
17.6 A Sensitivity Analysis of GDP Error Sources 376
17.6.1 Analysis of Computer Programming, Consultancy, and Related Services 376
17.6.2 Analysis of Product Motor Vehicles 378
17.6.3 Limitations of the Sensitivity Analysis 379
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 387
Marcus E Berzofsky and Paul P. Biemer
18.1 Introduction 387
18.2 Background 389
18.2.1 Surveys of Crime Victimization 389
18.2.2 Error Evaluation Studies 390
18.3 Analytic Approach 392
18.3.1 The NCVS and Its Relevant Attributes 392
18.3.2 Description of Analysis Data Set, Victimization Indicators,
and Covariates 392
18.3.3 Technical Description of the MLC Model and Its Assumptions 394
18.4 Model Selection 396
18.4.1 Model Selection Process 396
18.4.2 Model Selection Results 398
18.5 Results 399
18.5.1 Estimates of Misclassification 399
18.5.2 Estimates of Classification Error Among Demographic Groups 399
18.6 Discussion and Summary of Findings 404
18.6.1 High False-Negative Rates in the NCVS 404
18.6.2 Decreasing Prevalence Rates Over Time 405
18.6.3 Classification Error among Demographic Groups 405
18.6.4 Recommendations for Analysts 406
18.6.5 Limitations 406
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
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 413
Ting Yan
19.1 Introduction 413
19.2 Data and Methods 416
19.2.1 Data 416
19.2.2 Analytic Use of Doorstep Concerns Data 416
19.3 Results 418
19.3.1 Unit Response Rates in Later Waves and Average Number of Don’t Know
and Refused Answers 418
19.3.2 Total Nonresponse Bias and Nonresponse Bias Components 421
19.3.3 Adjusting for Nonresponse 421
19.4 Discussion 428
Contents
Acknowledgment 430
References 430
20 Total Survey Error Assessment for Sociodemographic Subgroups in the 2012
U.S. National Immunization Survey 433
Kirk M. Wolter, Vicki J. Pineau, Benjamin Skaiiand, Wei Zeng, James A. Singleton,
Meena Khore, 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
20.4.1 Stage 1: Sample-Frame Coverage Error 441
20.4.2 Stage 2: Nonresponse Error 443
20.4.3 Stage 3: Measurement Error 444
20.5 National Immunization Survey TSE Analysis 445
20.5.1 TSE Analysis for the Overall Age-Eligible Population 445
20.5.2 TSE Analysis by Sociodemographic Subgroups 448
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 457
Brady T. West
Part 1 Big Data Infrastructure at the Institute for Employment Research
(IAB) 458
Kirchner, Daniela Hochfellner, Stefan Bender
21.1.1 Dissemination of Big Data for Survey Research at the Institute for
Employment Research 458
21.1.2 Big Data Linkages at the IAB and Total Survey Error 459
21.1.2.1 Individual-Level Data: Linked Panel “Labour Market and Social Security”
Survey Data and Administrative Data (PASS-ADIAB) 459
21.1.2.2 Establishment Data: The IAB Establishment Panel and Administrative
Registers as Sampling Frames 461
21.1.3 Outlook 463
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 467
Elizabeth M. Nichols, Mary H. Mulry, and Jennifer Hunter Childs
21.2.1 Census Bureau Research and Programs 467
21.2.2 Using Administrative Data to Estimate Measurement Error
in Survey Reports 468
21.2.2.1 Address and Person Matching Challenges 469
21.2.2.2 Event Matching Challenges 470
21.2.2.3 Weighting Challenges 471
21.2.2.4 Record Update Challenges 471
21.2.2.5 Authority and Confidentiality Challenges 472
21.2.3 Summary 472
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 474
Anders Hofmberg and Christine Bycroft
213.1 Data Availability and Development of Data Infrastructure in New Zealand 475
213.2 Quality Assessment and Different Types of Errors 476
2133 Integration of Infrastructure Components and Developmental Streams 477
References 478
Part 4 Big Data Serving Survey Research: Experiences at the University
of Michigan Survey Research Center 478
Grant Benson and Frost Hubbard
21.4.1 Introduction 478
21.4.2 Marketing Systems Group (MSG) 479
21.4.2.1 Using MSG Age Information to Increase Sampling Efficiency 480
21.4.3 MCH Strategic Data (MCH) 481
21.4.3.1 Assessing MCH s Teacher Frame with Manual Listing Procedures 482
21.4.4 Conclusion 484
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 489
Brady 7. 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
22.4.1 Coding of Published Articles 495
22.4.2 Statistical Analyses 495
22.5 Results 497
22.5.1 Descriptive Statistics 497
22.5.2 Bivariate Analyses 499
22.5.3 Trends in Error Rates Over Time 502
22.6 Discussion 505
22.6.1 Summary of Findings 505
22.6.2 Suggestions for Practice 506
22.6.3 Limitations 506
22.6.4 Directions for Future Research 507
Acknowledgments 508
References 508
23 Mixed-Mode Research: Issues in Design and Analysis 511
Joop Hox, Edith de Leeuw, and Thomas Klausch
23.1 Introduction 511
23.2 Designing Mixed-Mode Surveys 512
233 Literature Overview 514
23.4 Diagnosing Sources of Error in Mixed-Mode Surveys 516
23.4.1 Distinguishing Between Selection and Measurement Effects:
The Multigroup Approach 516
Contents
23.4.1.1 Multigroup Latent Variable Approach 516
23.4.1.2 Multigroup Observed Variable Approach 520
23.4.2 Distinguishing Between Selection and Measurement Effects:
The Counterfactual or Potential Outcome Approach 521
23.4.3 Distinguishing Between Selection and Measurement Effects:
The Reference Survey Approach 522
23.5 Adjusting for Mode Measurement Effects 523
23.5.1 The Multigroup Approach to Adjust for Mode Measurement Effects 523
23.5.1.1 Multigroup Latent Variable Approach 523
23.5.1.2 Multigroup Observed Variable Approach 525
23.5.2 The Counterfactual (Potential Outcomes) Approach to Adjust for Mode
Measurement Effects 525
23.5.3 The Reference Survey Approach to Adjust for Mode Measurement Effects 526
23.6 Conclusion 527
References 528
24 The Effect of Nonresponse and Measurement Error on Wage Regression
across Survey Modes: A Validation Study 531
Kirchner and Barbara Fetderer
24.1 Introduction 531
24.2 Nonresponse and Response Bias in Survey Statistics 532
24.2.1 Bias in Regression Coefficients 532
24.2.2 Research Questions 533
24.3 Data and Methods 534
24.3.1 Survey Data 534
24.3.1.1 Sampling and Experimental Design 534
24.3.1.2 Data Collection 535
24.3.2 Administrative Data 536
24.3.2.1 General Information 536
24.3.2.2 Variable Selection 537
24.3.2.3 Limitations 537
24.3.2.4 Combined Data 537
24.3.3 Bias in Univariate Statistics 538
24.3.3.1 Bias: The Dependent Variable 538
24.3.3.2 Bias: The Independent Variables 538
24.3.4 Analytic Approach 539
24.4 Results 541
24.4.1 The Effect of Nonresponse and Measurement Error on Regression Coefficients 541
24.4.2 Nonresponse Adjustments 543
24.5 Summary and Conclusion 546
Acknowledgments 547
Appendix 24.A 548
Appendix 24.B 549
References 554
25 Errors in Unking Survey and Administrative Data 557
Joseph W. Sakshaug and Manfred Antoni
25.1 Introduction 557
25.2 Conceptual Framework of Linkage and Error Sources 559
Contents I xvii
25.3 Errors Due to Linkage Consent 561
25.3.1 Evidence of Linkage Consent Bias 562
25.3.2 Optimizing Linkage Consent Rates 563
25.3.2.1 Placement of the Linkage Consent Request 563
25.3.2.2 Wording of the Linkage Consent Request 563
25.3.2.3 Active Versus Passive Consent 564
25.3.2.4 Obtaining Linkage Consent in Longitudinal Surveys 564
25.4 Erroneous Linkage with Unique Identifiers 565
25.5 Erroneous Linkage with Nonunique Identifiers 567
25.5.1 Common Nonunique Identifiers When Linking Data on People 567
25.5.2 Common Nonunique identifiers When Linking Data on Establishments 567
25.6 Applications and Practical Guidance 568
25.6.1 Applications 568
25.6.2 Practical Guidance 569
25.6.2.1 Initial Data Quality 570
25.6.2.2 Preprocessing 570
25.7 Conclusions and Take-Home Points 571
References 571
Index 575
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 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 apd improving survey,data quality. The book also addresses issues with data quality in official
statistics and in social, opinion, and market research fcs thèse 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 an| repprtfpg 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 arid 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.
M
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 v ՝■* j
• Provides comprehensive reviews of the literature on error sources as Veil as data collection approaches and estimation
methods to reduce their effects i ·՛ i- A · 1
, , ֊»,
• 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 i ■ i ^ 1
Total Survey Error in Practice is a reference for survey researchers anld data scientists in research areas that include social science,
public opinion, public policy, and business. It can also be used as a tektbookor supplementary material for a graduate-level course
in survey research methods. 1 . t ՛.■■.՝.
j:.:j ·. · f
TW՜՛.; ։».։ ,՛ : ՛ ;
Paul P. Biemer, PhD, is distinguished fellow at RTÏ International and associate director of Survey Research and Development at
the Odum Institute, University of North Carolina, USA. ՛
J*- ՛
(
Edith de Leeuw, PhD, is professor of survey methodology in the Department of Methodology and Statistics at Utrecht University,
the Netherlands.
v
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 sürvey 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.
У
Cover Image:
Courtesy of the editors
www.wiley.com
WIL E Y 1 rrs*
ISBN 978-1-119-04167-2
|
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| spelling | Total survey error in practice edited by Paul P. Biemer, Edith de Leeuw, Stephanie Eckman, Brad Edwards, Frauke Kreuter, Lars E. Lyberg, N. Clyde Tucker, Brady T. West Hoboken, NJ Wiley [2017] © 2017 xxvii, 593 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Wiley series in survey methodology Error analysis (Mathematics) Surveys Biemer, Paul P. edt Leeuw, Edith Desirée de 1952- (DE-588)1026780705 edt Eckman, Stephanie edt Edwards, Brad edt Kreuter, Frauke (DE-588)1033254037 edt Lyberg, Lars edt Tucker, Clyde (DE-588)171589785 edt West, Brady T. edt Erscheint auch als Online-Ausgabe, epub 978-1-119-04169-6 Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029314052&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029314052&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Total survey error in practice Error analysis (Mathematics) Surveys |
| title | Total survey error in practice |
| title_auth | Total survey error in practice |
| title_exact_search | Total survey error in practice |
| title_full | Total survey error in practice edited by Paul P. Biemer, Edith de Leeuw, Stephanie Eckman, Brad Edwards, Frauke Kreuter, Lars E. Lyberg, N. Clyde Tucker, Brady T. West |
| title_fullStr | Total survey error in practice edited by Paul P. Biemer, Edith de Leeuw, Stephanie Eckman, Brad Edwards, Frauke Kreuter, Lars E. Lyberg, N. Clyde Tucker, Brady T. West |
| title_full_unstemmed | Total survey error in practice edited by Paul P. Biemer, Edith de Leeuw, Stephanie Eckman, Brad Edwards, Frauke Kreuter, Lars E. Lyberg, N. Clyde Tucker, Brady T. West |
| title_short | Total survey error in practice |
| title_sort | total survey error in practice |
| topic | Error analysis (Mathematics) Surveys |
| topic_facet | Error analysis (Mathematics) Surveys |
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