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

Author

Listed:
  • Horwitz, Rachel

    (U.S. Census Bureau)

  • Brockhaus, Sarah

    (LMU München ; Univ. Mannheim)

  • Henninger, Felix

    (Univ. Mannheim)

  • Kieslich, Pascal

    (Univ. Mannheim)

  • Schierholz, Malte

    (Institute for Employment Research (IAB), Nuremberg, Germany ; Univ. Mannheim)

  • Keusch, Florian

    (Univ. Mannheim)

  • Kreuter, Frauke

    (Institute for Employment Research (IAB), Nuremberg, Germany ; Univ. Mannheim ; Univ. of Maryland)

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))

Suggested Citation

  • 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 201734, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:201734
    as

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    File URL: https://doku.iab.de/discussionpapers/2017/dp3417.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Schierholz, Malte & Brenner, Lorraine & Cohausz, Lea & Damminger, Lisa & Fast, Lisa & Hörig, Ann-Kathrin & Huber, Anna-Lena & Ludwig, Theresa & Petry, Annabell & Tschischka, Laura, 2018. "Eine Hilfsklassifikation mit Tätigkeitsbeschreibungen für Zwecke der Berufskodierung : Leitgedanken und Dokumentation," IAB-Discussion Paper 201813, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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