Learning from mouse movements: Improving questionnaire and respondents' user experience through passive data collection

Beschreibung

"Web surveys have become a standard mode of survey administration, in part because they offer greater technological capabilities so that aspects of the questionnaire's design can be dynamically controlled. Designers often use these features to help guide respondents through a survey, but web‐based designs also allow researchers to collect and analyze paradata. In particular, researchers in a variety of fields have analyzed mouse movements, including total distance traveled, cursor trajectory, and specific patterns of movement to measure users' difficulty, uncertainty, and level of interest. The current study investigates automated procedures for detecting and quantifying difficulty in web surveys. It computes indicators of difficulty that have been identified by prior research to assess whether they are sensitive to experimentally induced difficulty. The current study builds on recent methodological advances in psychological research that use mouse‐tracking measures to assess the tentative commitments to, and conflict between, response alternatives." (Author's abstract, IAB-Doku) ((en))

Zitationshinweis

Horwitz, Rachel, Sarah Brockhaus, Felix Henninger, Pascal Kieslich, Malte Schierholz, Florian Keusch & Frauke Kreuter (2020): Learning from mouse movements: Improving questionnaire and respondents' user experience through passive data collection. In: P. C. Beatty, D. Collins, L. Kaye, J. L. Padilla, G. Willis & A. Wilmot (Eds.) (2020): Advances in questionnaire design, development, evaluation and testing, Nürnberg, S. 403-426. DOI:10.1002/9781119263685.ch16