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Optimizing Scatterplot-Matrices for Decision-Support:

In: Information Systems and Neuroscience

Author

Listed:
  • Lisa Perkhofer

    (University of Applied Sciences Upper Austria)

  • Peter Hofer

    (University of Applied Sciences Upper Austria)

Abstract

The scatterplot matrix is defined to be a standard method for multivariate data visualization; nonetheless, their use for decision-support in a corporate environment is scarce. Amongst others, longstanding criticism lies in the lack of empirical testing to investigate optimal design specifications as well as areas of application from a business related perspective. Thus, on the basis of an innovative approach to assess a visualization’s fitness for efficient and effective decision-making given a user’s situational cognitive load, this study investigates the usability of a scatterplot matrix while performing typical tasks associated with multidimensional datasets (correlation and distribution assessment). A laboratory experiment recording eye-tracking data investigates the design of the matrix and its influence on the decision-maker’s ability to process the presented information. Especially, the information content presented in the diagonal as well as the size of the matrix are tested and linked to the user’s individual processing capabilities. Results show that the design of the scatterplot as well as the size of the matrix influenced the decision-making greatly.

Suggested Citation

  • Lisa Perkhofer & Peter Hofer, 2021. "Optimizing Scatterplot-Matrices for Decision-Support:," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 63-76, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-88900-5_8
    DOI: 10.1007/978-3-030-88900-5_8
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