Description
Book SynopsisProvides new insights into the accuracy and value of online panels for completing surveys Over the last decade, there has been a major global shift in survey and market research towards data collection, using samples selected from online panels. Yet despite their widespread use, remarkably little is known about the quality of the resulting data.
This edited volume is one of the first attempts to carefully examine the quality of the survey data being generated by online samples. It describes some of the best empirically-based research on what has become a very important yet controversial method of collecting data. Online Panel Research presents 19 chapters of previously unpublished work addressing a wide range of topics, including coverage bias, nonresponse, measurement error, adjustment techniques, the relationship between nonresponse and measurement error, impact of smartphone adoption on data collection, Internet rating panels, and operational issues.
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Table of Contents
Preface xv
Acknowledgments xvii
About the Editors xix
About the Contributors xxiii
1 Online panel research: History, concepts, applications and a look at the future 1
Mario Callegaro, Reg Baker, Jelke Bethlehem, Anja S. Göritz, Jon A. Krosnick, and Paul J. Lavrakas
1.1 Introduction 1
1.2 Internet penetration and online panels 2
1.3 Definitions and terminology 2
1.4 A brief history of online panels 4
1.5 Development and maintenance of online panels 6
1.6 Types of studies for which online panels are used 15
1.7 Industry standards, professional associations’ guidelines, and advisory groups 15
1.8 Data quality issues 17
1.9 Looking ahead to the future of online panels 17
2 A critical review of studies investigating the quality of data obtained with online panels based on probability and nonprobability samples 23
Mario Callegaro, Ana Villar, David Yeager, and Jon A. Krosnick
2.1 Introduction 23
2.2 Taxonomy of comparison studies 24
2.3 Accuracy metrics 27
2.4 Large-scale experiments on point estimates 28
2.5 Weighting adjustments 35
2.6 Predictive relationship studies 36
2.7 Experiment replicability studies 38
2.8 The special case of pre-election polls 42
2.9 Completion rates and accuracy 43
2.10 Multiple panel membership 43
2.11 Online panel studies when the offline population is less of a concern 46
2.12 Life of an online panel member 47
2.13 Summary and conclusion 48
Part I COVERAGE 55
Introduction to Part I 56
Mario Callegaro and Jon A. Krosnick
3 Assessing representativeness of a probability-based online panel in Germany 61
Bella Struminskaya, Lars Kaczmirek, Ines Schaurer, and Wolfgang Bandilla
3.1 Probability-based online panels 61
3.2 Description of the GESIS Online Panel Pilot 62
3.3 Assessing recruitment of the Online Panel Pilot 66
3.4 Assessing data quality: Comparison with external data 68
3.5 Results 74
3.6 Discussion and conclusion 80
4 Online panels and validity: Representativeness and attrition in the Finnish eOpinion panel 86
Kimmo Grönlund and Kim Strandberg
4.1 Introduction 86
4.2 Online panels: Overview of methodological considerations 87
4.3 Design and research questions 88
4.4 Data and methods 90
4.5 Findings 92
4.6 Conclusion 100
5 The untold story of multi-mode (online and mail) consumer panels: From optimal recruitment to retention and attrition 104
Allan L. McCutcheon, Kumar Rao, and Olena Kaminska
5.1 Introduction 104
5.2 Literature review 107
5.3 Methods 108
5.4 Results 115
5.5 Discussion and conclusion 124
Part II NONRESPONSE 127
Introduction to Part II 128
Jelke Bethlehem and Paul J. Lavrakas
6 Nonresponse and attrition in a probability-based online panel for the general population 135
Peter Lugtig, Marcel Das, and Annette Scherpenzeel
6.1 Introduction 135
6.2 Attrition in online panels versus offline panels 137
6.3 The LISS panel 139
6.4 Attrition modeling and results 142
6.5 Comparison of attrition and nonresponse bias 148
6.6 Discussion and conclusion 150
7 Determinants of the starting rate and the completion rate in online panel studies 154
Anja S. Göritz
7.1 Introduction 154
7.2 Dependent variables 155
7.3 Independent variables 156
7.4 Hypotheses 156
7.5 Method 163
7.6 Results 164
7.7 Discussion and conclusion 166
8 Motives for joining nonprobability online panels and their association with survey participation behavior 171
Florian Keusch, Bernad Batinic, and Wolfgang Mayerhofer
8.1 Introduction 171
8.2 Motives for survey participation and panel enrollment 173
8.3 Present study 176
8.4 Results 179
8.5 Conclusion 185
9 Informing panel members about study results: Effects of traditional and innovative forms of feedback on participation 192
Annette Scherpenzeel and Vera Toepoel
9.1 Introduction 192
9.2 Background 193
9.3 Method 196
9.4 Results 199
9.5 Discussion and conclusion 207
Part III MEASUREMENT ERROR 215
Introduction to Part III 216
Reg Baker and Mario Callegaro
10 Professional respondents in nonprobability online panels 219
D. Sunshine Hillygus, Natalie Jackson, and McKenzie Young
10.1 Introduction 219
10.2 Background 220
10.3 Professional respondents and data quality 221
10.4 Approaches to handling professional respondents 223
10.5 Research hypotheses 224
10.6 Data and methods 225
10.7 Results 226
10.8 Satisficing behavior 229
10.9 Discussion 232
11 The impact of speeding on data quality in nonprobability and freshly recruited probability-based online panels 238
Robert Greszki, Marco Meyer, and Harald Schoen
11.1 Introduction 238
11.2 Theoretical framework 239
11.3 Data and methodology 242
11.4 Response time as indicator of data quality 243
11.5 How to measure "speeding"? 246
11.6 Does speeding matter? 251
11.7 Conclusion 257
Part IV WEIGHTING ADJUSTMENTS 263
Introduction to Part IV 264
Jelke Bethlehem and Mario Callegaro
12 Improving web survey quality: Potentials and constraints of propensity score adjustments 273
Stephanie Steinmetz, Annamaria Bianchi, Kea Tijdens, and Silvia Biffignandi
12.1 Introduction 273
12.2 Survey quality and sources of error in nonprobability web surveys 274
12.3 Data, bias description, and PSA 277
12.4 Results 284
12.5 Potentials and constraints of PSA to improve nonprobability web survey quality: Conclusion 286
13 Estimating the effects of nonresponses in online panels through imputation 299
Weiyu Zhang
13.1 Introduction 299
13.2 Method 302
13.3 Measurements 303
13.4 Findings 303
13.5 Discussion and conclusion 308
Part V NONRESPONSE AND MEASUREMENT ERROR 311
Introduction to Part V 312
Anja S. Göritz and Jon A. Krosnick
14 The relationship between nonresponse strategies and measurement error: Comparing online panel surveys to traditional surveys 313
Neil Malhotra, Joanne M. Miller, and Justin Wedeking
14.1 Introduction 313
14.2 Previous research and theoretical overview 314
14.3 Does interview mode moderate the relationship between nonresponse strategies and data quality? 317
14.4 Data 318
14.5 Measures 320
14.6 Results 324
14.7 Discussion and conclusion 332
15 Nonresponse and measurement error in an online panel: Does additional effort to recruit reluctant respondents result in poorer quality data? 337
Caroline Roberts, Nick Allum, and Patrick Sturgis
15.1 Introduction 337
15.2 Understanding the relation between nonresponse and measurement error 338
15.3 Response propensity and measurement error in panel surveys 341
15.4 The present study 342
15.5 Data 343
15.6 Analytical strategy 344
15.7 Results 350
15.8 Discussion and conclusion 357
Part VI SPECIAL DOMAINS 363
Introduction to Part VI 364
Reg Baker and Anja S. Göritz
16 An empirical test of the impact of smartphones on panel-based online data collection 367
Frank Drewes
16.1 Introduction 367
16.2 Method 369
16.3 Results 371
16.4 Discussion and conclusion 385
17 Internet and mobile ratings panels 387
Philip M. Napoli, Paul J. Lavrakas, and Mario Callegaro
17.1 Introduction 387
17.2 History and development of Internet ratings panels 388
17.3 Recruitment and panel cooperation 390
17.4 Compliance and panel attrition 394
17.5 Measurement issues 396
17.6 Long tail and panel size 398
17.7 Accuracy and validation studies 400
17.8 Statistical adjustment and modeling 401
17.9 Representative research 402
17.10 The future of Internet audience measurement 403
Part VII OPERATIONAL ISSUES IN ONLINE PANELS 409
Introduction to Part VII 410
Paul J. Lavrakas and Anja S. Göritz
18 Online panel software 413
Tim Macer
18.1 Introduction 413
18.2 What does online panel software do? 414
18.3 Survey of software providers 415
18.4 A typology of panel research software 416
18.5 Support for the different panel software typologies 417
18.6 The panel database 418
18.7 Panel recruitment and profile data 421
18.8 Panel administration 423
18.9 Member portal 425
18.10 Sample administration 428
18.11 Data capture, data linkage and interoperability 430
18.12 Diagnostics and active panel management 433
18.13 Conclusion and further work 436
19 Validating respondents’ identity in online samples: The impact of efforts to eliminate fraudulent respondents 441
Reg Baker, Chuck Miller, Dinaz Kachhi, Keith Lange, Lisa Wilding-Brown, and Jacob Tucker
19.1 Introduction 441
19.2 The 2011 study 443
19.3 The 2012 study 444
19.4 Results 446
19.5 Discussion 449
19.6 Conclusion 450
References 451
Appendix 19.A 452
Index 457
