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Evaluating the Dot-Based Contingency Wheel: Results from a Usability and Utility Study

  • Margit Pohl
  • Florian Scholz
  • Simone Kriglstein
  • Bilal Alsallakh
  • Silvia Miksch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8521)

Abstract

The Dot-Based Contingency Wheel is an interactive visual-analytics method designed to discover and analyze positive associations in an asymmetrically large n ×m contingency table. Such tables summarize the relation between two categorical variables and arise in both scientific and business domains. This paper presents the results of a pilot evaluation study based on interviews conducted with ten users to assess both the conceptual design as well as the usability and utility of the Dot-Based Contingency Wheel. The results illustrate that the Wheel as a metaphor has some advantages, especially its interactive features and ability to provide an overview of large tables. On the other hand, we found major issues with this metaphor, especially how it represents the relations between the variables. Based on these results, the metaphor was redesigned as Contingency Wheel++, which uses simplified and more familiar visual representations to tackle the major issues we identified.

Keywords

Visual Analytics Evaluation User Interface Interview Contingency Tables 

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References

  1. 1.
    Hartigan, J., Kleiner, B.: Mosaics for contingency tables. In: Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, New York, vol. 22, pp. 286–273 (1981)Google Scholar
  2. 2.
    Bendix, F., Kosara, R., Hauser, H.: Parallel sets: visual analysis of categorical data. In: Proc. of the IEEE Symposium on Information Visualization, pp. 133–140 (2005)Google Scholar
  3. 3.
    Benzécri, J.P.: Correspondence Analysis Handbook. Marcel Dekker, New York (1990)Google Scholar
  4. 4.
    Alsallakh, B., Gröller, E., Miksch, S., Suntinger, M.: Contingency wheel: Visual analysis of large contingency tables. In: Proceedings of the International Workshop on Visual Analytics, EuroVA (2011)Google Scholar
  5. 5.
    Carpendale, S.: Evaluating information visualizations. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 19–45. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Courage, C., Baxter, K.: Understanding Your Users: A Practical Guide to User Requirements Methods, Tools, and Techniques. Morgan Kaufmann (2005)Google Scholar
  7. 7.
    Wilson, C.: User Experience Re-Mastered: Your Guide to Getting the Right Design. Morgan Kaufmann (2009)Google Scholar
  8. 8.
    Stone, D., Jarrett, C., Woodroffe, M., Minocha, S.: User Interface Design and Evaluation User Interface Design and Evaluation. Morgan Kaufman (2009)Google Scholar
  9. 9.
    Adams, A., Cox, A.: Questionnaires, in-depth interviews and focus groups. In: Cairns, P., Cox, A. (eds.) Research Methods for Human-Computer Interaction, pp. 17–34. Cambridge University Press, Cambridge (2008)Google Scholar
  10. 10.
    Bortz, J., Döring, N.: Forschungsmethoden und Evaluation für Human- und Sozialwissenschaftler, 4th edn. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Alsallakh, B., Aigner, W., Miksch, S., Gröller, E.: Reinventing the contingency wheel: Scalable visual analytics of large categorical data. IEEE Transactions on Visualization and Computer Graphics, Proceedings of IEEE VAST 2012 18(12), 2849–2858 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Margit Pohl
    • 1
  • Florian Scholz
    • 1
  • Simone Kriglstein
    • 1
  • Bilal Alsallakh
    • 2
  • Silvia Miksch
    • 2
  1. 1.Institute for Design and Assessment of TechnologyVienna University of TechnologyAustria
  2. 2.Institute of Software Technology & Interactive SystemsVienna University of TechnologyAustria

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