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Provides 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…mehr
Provides 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 Online Panel Research presents 19 chapters of previously unpublished work addressing a wide range of topics, including coverage bias, nonresponse, measurement error, adjustment techniques, the relationship between nonresponse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. The datasets used to prepare the analyses reported in the chapters are available on the accompanying website: www.wiley.com/go/online_panel * Covers controversial topics such as professional respondents, speeders, and respondent validation. * Addresses cutting-edge topics such as the challenge of smartphone survey completion, software to manage online panels, and Internet and mobile ratings panels. * Discusses and provides examples of comparison studies between online panels and other surveys or benchmarks. * Describes adjustment techniques to improve sample representativeness. * Addresses coverage, nonresponse, attrition, and the relationship between nonresponse and measurement error with examples using data from the United States and Europe. * Addresses practical questions such as motivations for joining an online panel and best practices for managing communications with panelists. * Presents a meta-analysis of determinants of response quantity. * Features contributions from 50 international authors with a wide variety of backgrounds and expertise. This book will be an invaluable resource for opinion and market researchers, academic researchers relying on web-based data collection, governmental researchers, statisticians, psychologists, sociologists, and other research practitioners.
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Autorenporträt
Mario Callegaro, Survey Research Scientist, Quantitative Marketing, Google Inc., UK Reg Baker, President & Chief Operating Officer, Market Strategies International, USA Paul J. Lavrakas, Nielsen Media Research, Research Psychologist/Research Methodologist, USA Jon A. Krosnick, Professor of Political Science, Communication, Psychology, Stanford University, USA Jelke Bethlehem, Department of Quantitative Economics, University of Amsterdam, The Netherlands Anja Göritz, University of Erlangen-Nuremberg, Department of Economics and Social Psychology, Germany
Inhaltsangabe
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
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
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