Web Survey Bibliography
Title Unintentional Mobile Respondents in Official Statis tics and Their Effect on Data Quality
Author Bakker, J.
Year 2016
Access date 03.06.2016
Abstract
An increasing proportion of web surveys are completed on tablets and smartphones, even if the questionnaires are not designed for mobile completion. Earlier research has shown that differences in data quality between answers completed on different devices are often small. Most of these studies have however relied on experimentation on volunteer samples to compare differences in data quality across different devices. In this study, we use rich register data available from the sampling frame available to Statistics Netherlands, to look at data quality for general population surveys. Specifically, we will look at both selection and measurement effects across different devices for a number of Statistics Netherlands’ social surveys. In this paper, we compare desktop/laptop, tablet and smartphone respondents in terms of background variables, unit and item non-response, and a range of data quality indicators like percentage breakoffs, average completion times, question order effects, social desirability, straightlining, the length and rounding of open answers and percentage don’t know answers. We give recommendations for optimization of the designs over devices.
Access/Direct link Conference Homepage (abstract)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography (4086)
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- Social desirability bias in self-reported well-being measures: evidence from an online survey; 2017; Caputo, A.
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- 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.
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- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
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- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
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- 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.
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- 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.
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- Is There a Future for Surveys; 2017; Miller, P. V.
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- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
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- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.