Online Panel Research (eBook, PDF)
A Data Quality Perspective
Redaktion: Callegaro, Mario; Lavrakas, Paul J.; Krosnick, Jon A.; Göritz, Anja S.; Bethlehem, Jelke; Baker, Reginald P.
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Online Panel Research (eBook, PDF)
A Data Quality Perspective
Redaktion: Callegaro, Mario; Lavrakas, Paul J.; Krosnick, Jon A.; Göritz, Anja S.; Bethlehem, Jelke; Baker, Reginald P.
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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
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- Online Panel Research (eBook, ePUB)68,99 €
- Ed Swires-HennessyPresenting Data (eBook, PDF)24,99 €
- David R. HeiseSurveying Cultures (eBook, PDF)97,99 €
- Jelke BethlehemHandbook of Nonresponse in Household Surveys (eBook, PDF)160,99 €
- Paul S. LevySampling of Populations (eBook, PDF)138,99 €
- Duane F. AlwinMargins of Error (eBook, PDF)122,99 €
- Jennifer MadansQuestion Evaluation Methods (eBook, PDF)81,99 €
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Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 512
- Erscheinungstermin: 25. März 2014
- Englisch
- ISBN-13: 9781118763506
- Artikelnr.: 40712421
- Verlag: John Wiley & Sons
- Seitenzahl: 512
- Erscheinungstermin: 25. März 2014
- Englisch
- ISBN-13: 9781118763506
- Artikelnr.: 40712421
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
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