Correspondence Analysis (CA) is used to interpret correlations between categorical variables in the areas of social science and market research. To do so, coherences of variables are converted to a three-dimensional point cloud and plotted as several different 2D-mappings, each containing two axes. The major challenge is to correctly interpret these plottings. Due to a missing axis, distances can easily be under- or overestimated. This can lead to a misinterpretation and thus a misclustering of data.
To address this problem we present CAVIR, an approach for CA in Virtual Reality. It supports users with a three-dimensional representation of the point cloud and different options to show additional information, to measure Euclidean distances, and to cluster points. Besides, the motion parallax and a free rotation of the entire point cloud enable the CA expert to always have a correct view of the data.
Best Presentation Award!