Although RC(M)-association models have become a generally useful tool for the analysis of cross-classified data, the graphical representation resulting from such an analysis can at times be misleading. The relationships present between row category points and column category points cannot be interpreted by inter point distances but only through projection. In order to avoid incorrect interpretation by a distance rule, joint plots should be made that either represent the row categories or the column categories as vectors. In contrast, the present study proposes models in which the distances between row and column points can be interpreted directly, with a large (small) distance corresponding to a small (large) value for the association. The models provide expressions for the odds ratios in terms of distances, which is a feature that makes the proposed models attractive reparametrizations to the usual RC(M)-parametrization. Comparisons to existing data analysis techniques plus an overview of related models and their connections are also provided.