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Inhaltsbereich: Institut für Arbeitsmarkt- und Berufsforschung

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

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Abstract

"Web surveys have become a standard, and often preferred, mode of survey administration in part because the technology underlying them is much more adaptable. Survey designers often use these technical features to help guide respondents through a survey, by incorporating automated skips, for example. Other features, such as mouse movements, can be used to identify individual respondents that may require attention. Specifically, researchers in a variety of fields have used the total distance traveled, the cursor's trajectory, and specific patterns of movement to measure interest, uncertainty, and respondent difficulty.
The current study aims to develop automated procedures for detecting and quantifying difficulty indicators in web surveys. It will use, and build on, indicators that have been identified by prior research. In addition, the current study relies on recent methodological advances in psychology that propose mouse-tracking measures for assessing the tentative commitments to, and conflict between, response alternatives." (Author's abstract, IAB-Doku) ((en))

Author


Bibliographical information

Horwitz, Rachel; Brockhaus, Sarah; Henninger, Felix; Kieslich, Pascal; Schierholz, Malte; Keusch, Florian; Kreuter, Frauke (2017): Learning from mouse movements: Improving questionnaire and respondents' user experience through passive data collection. (IAB-Discussion Paper, 34/2017), N├╝rnberg, 25 p.
 

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