Web Survey Bibliography

Title Nonresponses as context-sensitive response behaviour of participants in online-surveys and their relevance for data quality
Year 2017
Access date 15.09.2017
Abstract Starting point and focus: It is not possible to ignore the internet as a quick, practicable and economic source of information and nearly unlimited communication channel, as a mass medium (online news), a mainstream medium (social media) as well as an individual medium (email). The number of web surveys and methods of taking web surveys increased with the utilisation of the internet. For instance, the Arbeitskreis Deutscher Markt- und Sozialforschungsinstitute e.V. recorded a continuous increase from 1% quantitative web surveys of their members in 1998 to 16% in 2004, 38% in 2010 and 43% in 2014. However, webbased surveys – as extensive discussions show – are not free of controversy. Questable data quality, typically regarding the representativeness of the data (coverage error / missing data) and difficulties to achieve unbiased responses (measurement errors) caused by the equipment used (mode-effects) is more and more common. Errors caused by continuous rising proportions of drop-outs and item-nonresponses in online surveys, are relevant in almost the same manner. However, these sources of error are repeatedly neglected to a certain degree. 
As the starting point of the paper, it is assumed that drop-out rates and item-nonresponse rates in online surveys differ as context-sensitive (whether at home or not and using a smart-phone or not) response behaviour. This means that systematic errors linked to the interview situation (in terms of location and device) are conceivable. Respectively, the presentation aims to illustrate, how/to what extend the context of the interview situation has to be considered for data cleansing and analysis of data captured online to avoid, as far as possible, biased results. 
Methods and Data: To test this assumption, an online survey about “participation of university students” is used. To provoke drop-outs on the one hand and on the other hand test the consequences of different motivation strategies (prospect of profit, appeals, manipulation of the progress bar) that are easily inserted and therefore often used in online surveys, an experimental design was applied. For this purpose, an unusually long questionnaire (23 online-pages, 121 items) was developed, wherein different motivation strategies were included. 14.2% of the students (n=17,491) invited to take part in the survey reacted to the invitation, 1916 (11%) answered at least one question; just 7.3% (n=1282) reached the final page.
Results: Drop-out-rates and item-nonresponse-rates differ, depending on the above specified survey context: not being at home and using a smart-phone increases both. The motivation strategies used work differently: they solely reduce the risk of non-responses of those who did not use a smart-phone while at home. However, data cleansing does not affect the sample composition concerning studyrelated characteristics. Detailed analyses show that the influence of the defined survey context on substantial findings varies. Based on this the presentation will emphasize the importance of recording and considering the context-information of data collection for data cleansing, analysis and interpretation of results and will discuss how this
Year of publication2017
Bibliographic typeConferences, workshops, tutorials, presentations
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Web survey bibliography (8390)

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