Evaluating the Dot-Based Contingency Wheel: Results from a Usability and Utility Study Margit Pohl 1 , Florian Scholz 1 , Simone Kriglstein 1 , Bilal Alsallakh 2 , and Silvia Miksch 2 1 Institute for Design and Assessment of Technology, Vienna University of Technology, Austria {margit,florian,simone.kriglstein}@igw.tuwien.ac.at 2 Institute of Software Technology & Interactive Systems, Vienna University of Technology, Austria {alsallakh,miksch}@ifs.tuwien.ac.at 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 sum- marize 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. 1 Introduction Categorical data appear in many data tables both in scientific and in business domains. In contrast to numerical variables, the values of a categorical vari- able have no inherent order. Therefore, common analysis techniques that handle numerical variables are usually inapplicable to analyze categorical data. The analysis of categorical data is usually based on contingency tables. A two-way contingency table is a matrix that records how often each combination of cate- gories from two categorical variables appears in the database. An example would be the combination of color of hair and color of the eyes. A contingency table would, in this example, contain the frequency of the co-occurrence of blue eyes and blond hair, brown eyes and brown hair etc. An example for a contingency table in the context of medical applications would be types of diseases vs. groups S. Yamamoto (Ed.): HIMI 2014, Part I, LNCS 8521, pp. 76–86, 2014. c  Springer International Publishing Switzerland 2014