Alsallakh, BilalGroeller, EduardMiksch, SilviaSuntinger, MartinSilvia Miksch and Giuseppe Santucci2014-01-272014-01-272011978-3-905673-82-1https://doi.org/10.2312/PE/EuroVAST/EuroVA11/053-056We present the Contingency Wheel, a visual method for finding and analyzing associations in a large nxm contingency table with m less than 100 and n being two to three orders of magnitude larger than m. The method is demonstrated on a large table from the Book-Crossing dataset, which counts the number of ratings each book received from each country. It enables finding books that received a disproportionately high number of ratings from a specific country. It further allows to visually analyze what these books have in common, and with which countries they are also highly associated. Pairs of similar countries can further be identified (in the sense that many books are associated with both countries). Compared with existing visual methods, our approach enables analyzing and gaining insight into larger tables.Categories and Subject Descriptors: categorical data analysis, visual representations and interaction techniques.