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Web Survey Bibliography

Title A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys
Year 2017
Access date 10.09.2017
Abstract Due to low costs, improvements in coverage, and technological advances many surveys are now being conducted in whole or in part via self-administered web questionnaires. Increasingly, respondents are choosing to complete web surveys on touch-screen mobile devices such as tablets and smartphones. Recent estimates show that the proportion of respondents completing a survey on a mobile device can be 30% or more for some surveys (Lugtig, Toepoel, and Amin, 2016; Saunders, 2015). Mobile apps are also being used by survey respondents who are panel members and by interviewers to administer household screening surveys. Because of these technological advances, the ways that respondents and interviewers interact with surveys are changing. 
With the pace of change in survey administration, we need to consider whether traditional pretesting methodologies address the types of potential quality concerns these newer modes introduce. For example, modern web surveys support dynamic survey features such as hover-over definitions, calculate total buttons, videos/images, error messages, dynamic look-ups, touch-screen, swiping to navigate, GPS, and other capabilities. Each of these features changes the respondent-survey interaction, which can affect the quality of the data collected in a survey.
The purpose of this paper is to introduce emerging survey pretesting methodologies and compare these with traditional methods in the light of modern data collection technologies to consider where the standard approaches for pretesting can be improved. We begin by discussing the key limitations of traditional pretesting methods such as expert review, cognitive interviewing, and pilot testing for evaluating “modern” surveys. We then provide an overview of emerging pretesting methods including usability testing, eye tracking, and crowdsourcing. We discuss the advantages offered by these methods – particularly in terms of budget and schedule—and provide empirical examples of how these methods can improve data quality. We conclude with a theoretical mode for the optimal combination of traditional and newer methods for pretesting modern surveys.
Year of publication2017
Bibliographic typeConferences, workshops, tutorials, presentations