Web Survey Bibliography
Online panels are at present one of fastest growing data collection modes for market and opinion research (AAPOR report on online panels). With the use of online panels, concern grew about the recruitment of panel members, and especially the emergence of large nonprobability panels. The combination of self-selection and incentives, both important characteristics of non-probability based internet surveys, and the increased use of such non-probability based internet panels, lead to an increased fear of the emergence of ‘professional respondents’ (e.g. Comley, 2005) and the negative consequences of this for data quality. So far, there are very few empirical studies into this topic. The goal of this study is (1) to investigate whether ‘professional’ respondents can be distinguished in online panels using latent class analysis, (2) describe who the professional are and provide a demographical and psychographical profile, and (3) investigate whether there is a difference in the quality of the data provided by these ‘professional’ respondents and the other respondents in the panel. In our study, we analyzed a unique data set of the NOPVO (Nederlands Online Panel Vergelijkings Onderzoek; Vonk et al, 2006) that includes 19 large Dutch online panels, which together capture 90% of the respondents to online market research in the Netherlands. A latent class analysis showed that four types of respondents can be distinguished, ranging from the ‘professional’ respondent to the ‘altruistic-voluntary’ respondent. Also, different respondent types can be clearly characterized using demographical and psychographical variables. The ultimate question is whether the existence of professional respondents is a threat to the data quality of internet panels. Do professional respondents satisfice more, do they take more short-cuts? Indeed, small differences in data quality can be detected between the groups. However, these differences disappear when controlling for socio-demographic variables.
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Web survey bibliography (4086)
- Displaying Videos in Web Surveys: Implications for Complete Viewing and Survey Responses; 2017; Mendelson, J.; Lee Gibson, J.; Romano Bergstrom, J. C.
- Using experts’ consensus (the Delphi method) to evaluate weighting techniques in web surveys not...; 2017; Toepoel, V.; Emerson, H.
- Mind the Mode: Differences in Paper vs. Web-Based Survey Modes Among Women With Cancer; 2017; Hagan, T. L.; Belcher, S. M.; Donovan, H. S.
- Answering Without Reading: IMCs and Strong Satisficing in Online Surveys; 2017; Anduiza, E.; Galais, C.
- Ideal and maximum length for a web survey; 2017; Revilla, M.; Ochoa, C.
- Social desirability bias in self-reported well-being measures: evidence from an online survey; 2017; Caputo, A.
- Web-Based Survey Methodology; 2017; Wright, K. B.
- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
- Lessons from recruitment to an internet based survey for Degenerative Cervical Myelopathy: merits of...; 2017; Davies, B.; Kotter, M. R.
- Web Survey Gamification - Increasing Data Quality in Web Surveys by Using Game Design Elements; 2017; Schacht, S.; Keusch, F.; Bergmann, N.; Morana, S.
- Effects of sampling procedure on data quality in a web survey; 2017; Rimac, I.; Ogresta, J.
- Comparability of web and telephone surveys for the measurement of subjective well-being; 2017; Sarracino, F.; Riillo, C. F. A.; Mikucka, M.
- Achieving Strong Privacy in Online Survey; 2017; Zhou, Yo.; Zhou, Yi.; Chen, S.; Wu, S. S.
- A Meta-Analysis of the Effects of Incentives on Response Rate in Online Survey Studies; 2017; Mohammad Asire, A.
- Telephone versus Online Survey Modes for Election Studies: Comparing Canadian Public Opinion and Vote...; 2017; Breton, C.; Cutler, F.; Lachance, S.; Mierke-Zatwarnicki, A.
- Examining Factors Impacting Online Survey Response Ratesin Educational Research: Perceptions of Graduate...; 2017; Saleh, A.; Bista, K.
- Usability Testing for Survey Research; 2017; Geisen, E.; Romano Bergstrom, J. C.
- Paradata as an aide to questionnaire design: Improving quality and reducing burden; 2017; Timm, E.; Stewart, J.; Sidney, I.
- Fieldwork monitoring and managing with time-related paradata; 2017; Vandenplas, C.
- Interviewer effects on onliner and offliner participation in the German Internet Panel; 2017; Herzing, J. M. E.; Blom, A. G.; Meuleman, B.
- Interviewer Gender and Survey Responses: The Effects of Humanizing Cues Variations; 2017; Jablonski, W.; Krzewinska, A.; Grzeszkiewicz-Radulska, K.
- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
- Where, When, How and with What Do Panel Interviews Take Place and Is the Quality of Answers Affected...; 2017; Niebruegge, S.
- Comparing the same Questionnaire between five Online Panels: A Study of the Effect of Recruitment Strategy...; 2017; Schnell, R.; Panreck, L.
- Nonresponses as context-sensitive response behaviour of participants in online-surveys and their relevance...; 2017; Wetzlehuetter, D.
- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- Measuring Subjective Health and Life Satisfaction with U.S. Hispanics; 2017; Lee, S.; Davis, R.
- Humanizing Cues in Internet Surveys: Investigating Respondent Cognitive Processes; 2017; Jablonski, W.; Grzeszkiewicz-Radulska, K.; Krzewinska, A.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
- The Effect of Respondent Commitment on Response Quality in Two Online Surveys; 2017; Cibelli Hibben, K.
- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
- Push2web or less is more? Experimental evidence from a mixed-mode population survey at the community...; 2017; Neumann, R.; Haeder, M.; Brust, O.; Dittrich, E.; von Hermanni, H.
- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- Redirected Inbound Call Sampling (RICS); A New Methodology ; 2017; Krotki, K.; Bobashev, G.; Levine, B.; Richards, S.
- An Empirical Process for Using Non-probability Survey for Inference; 2017; Tortora, R.; Iachan, R.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Rates, Delays, and Completeness of General Practitioners’ Responses to a Postal Versus Web-Based...; 2017; Sebo, P.; Maisonneuve, H.; Cerutti, B.; Pascal Fournier, J.; Haller, D. M.
- Necessary but Insufficient: Why Measurement Invariance Tests Need Online Probing as a Complementary...; 2017; Meitinger, K.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Is There a Future for Surveys; 2017; Miller, P. V.
- Reducing speeding in web surveys by providing immediate feedback; 2017; Conrad, F.; Tourangeau, R.; Couper, M. P.; Zhang, C.
- Social Desirability and Undesirability Effects on Survey Response latencies; 2017; Andersen, H.; Mayerl, J.
- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.