Web Survey Bibliography
Title A study on panel engagement in a mobile survey app
Author Scharioth, N.; Tschida, K.
Year 2016
Access date 29.04.2016
Presentation PDF (145KB)
Abstract
Relevance & Research Question: Mobile market research provides a way to engage with consumers and citizens in an interactive and fast fashion. This raises a number of methodological questions. How often can such app respondents be surveyed? What role do incentives play with regards to response speed and the final response rates? Does speed come at the expense of truthfulness? How effective are push notifications in relation to email notifications?
Methods & Data: We tested these questions by polling a sample of 1,668 active biopinio app users in December 2015. The sample was divided into 6 demographically similar groups (n=278), each of which was incentivized and notified in a distinct fashion, but with an otherwise identical questionnaire:
Group A: secure win, push and email notifications
Group B: secure win for first 200 participants, push and email notifications
Group C: lottery with 20% chance of winning, push and email notifications
Group D: lottery without transparent chance of winning, push and email notifications
Group E: lottery without transparent chance of winning, push notifications only
Group F: lottery without transparent chance of winning, email notifications only
Groups A-D differ in the type of incentivization the user was offered. Groups E and F have the same incentivization as Group D, but were notified either just by push notification or just by email. A measure for truthfulness was included through a disguised repeat question in all groups.
Results: Given the incentivization structure we expected Group B to respond the quickest (which was the case). There is little indication that speed came at the expense of truthfulness. The response rate varied between 64% and 80% with a guaranteed win resulting in the highest participation rate. Push notifications on their own are the least effective notification tool. App participants indicate a willingness to respond at high frequency (several surveys per month or even week).
Added Value: The results provide insights into how to optimize survey design, incentivization and frequency for mobile settings. App users' high engagement can be utilized for new types of market research designs.
Methods & Data: We tested these questions by polling a sample of 1,668 active biopinio app users in December 2015. The sample was divided into 6 demographically similar groups (n=278), each of which was incentivized and notified in a distinct fashion, but with an otherwise identical questionnaire:
Group A: secure win, push and email notifications
Group B: secure win for first 200 participants, push and email notifications
Group C: lottery with 20% chance of winning, push and email notifications
Group D: lottery without transparent chance of winning, push and email notifications
Group E: lottery without transparent chance of winning, push notifications only
Group F: lottery without transparent chance of winning, email notifications only
Groups A-D differ in the type of incentivization the user was offered. Groups E and F have the same incentivization as Group D, but were notified either just by push notification or just by email. A measure for truthfulness was included through a disguised repeat question in all groups.
Results: Given the incentivization structure we expected Group B to respond the quickest (which was the case). There is little indication that speed came at the expense of truthfulness. The response rate varied between 64% and 80% with a guaranteed win resulting in the highest participation rate. Push notifications on their own are the least effective notification tool. App participants indicate a willingness to respond at high frequency (several surveys per month or even week).
Added Value: The results provide insights into how to optimize survey design, incentivization and frequency for mobile settings. App users' high engagement can be utilized for new types of market research designs.
Access/Direct link Conference Homepage (presentation)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
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.
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- 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.