Surveys completed on mobile web devices (smartphones) have been found to take longer than surveys completed on a PC. This has been found both in surveys where respondents can choose which device they use and in surveys where respondents are randomly assigned to devices. A number of potential explanations have been offered for these findings, including (1) slower transmission over cellular or Wi-Fi networks, (2) the difficulty of reading questions and selecting responses on a small device, and (3) the increased mobility of mobile web users who have more distractions while answering web surveys. In a secondary analysis of student surveys, we find that only about one-fifth of the time difference can be accounted for by transmission time (between-page time) with the balance being within-page time differences. Using multilevel models, we explore possible page-level (question-level) and respondent-level factors that may contribute to the time difference. We find that much of the time difference can be accounted for by the additional scrolling required on mobile devices, especially for grid questions.

This article reviews the existing literature on the collection of paradata in web surveys and extends the research in this area beyond the commonly studied measurement error problem to paradata that can be collected for managing and mitigating other important sources of error. To do so, and in keeping with the nature of paradata as process-oriented, we develop a typology of web survey paradata that incorporates information from all steps in the web survey process. We first define web survey paradata and describe general phases of paradata that run parallel to the steps in fielding a typical web survey. Within each phase, we enumerate several errors within the total survey error paradigm that can be examined with paradata, discussing case studies and motivating examples that illustrate innovative uses of paradata across the web survey process. We conclude with a discussion of open questions and opportunities for further work in this area. Overall, we develop this typology keeping technological advancements at the center of our discussion, but with flexibility to continuously incorporate new developments and trends in both technology and study design. Our typology encourages researchers to think about paradata as tools that can be used to investigate a broader range of outcomes than previously studied.

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