Goodman recently presented a class of models for the analysis of association between two discrete, ordinal variables. The association was measured in terms of the odds ratios in 2 × 2 subtables formed from adjacent rows and adjacent columns of the cross-classification, and models were devised that allowed the odds ratios to depend on an overall effect, on row effects, on column effects, and on other effects. This article presents some generalizations of this approach appropriate for multiway cross-classifications, including (a) models for the analysis of conditional association, (b) models for the analysis of partial association, and (c) models for the analysis of symmetric association. Three cross-classifications are analyzed with these models and methods, and rather simple interpretations of the association in each are provided.