Correspondence Analysis (CA) is frequently used to interpret correlations between categorical variables in the area of market research. To do so, coherences of variables are converted to a three-dimensional point cloud and plotted as three different 2D-mappings. 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 misclustering and misinterpretation of data and thus to faulty conclusions. To address this problem we present CAVIR, an approach for CA in Virtual Reality. It supports users with a virtual three-dimensional representation of the point cloud and different options to show additional information, to measure Euclidean distances, and to cluster points. Besides, the free rotation of the entire point cloud enables the CA user to always have a correct view of the data.