In the analysis of cross-classified data, the quantities of^{ }interest are frequently odds ratios. Although odds ratios are^{ }functions of the interaction parameters in association models,^{ }the usual way of normalizing and identifying these parameters^{ }means that their relationship with the odds ratios of interest^{ }is indirect. This can lead to interpretative confusions. The^{ }author points to the benefits of defining the interaction parameters^{ }of a model to have a one-to-one relationship with the odds ratios^{ }of interest, thus overcoming problems of interpretation. Three^{ }examples are presented to illustrate the argument.

**Key Words:** log-linear and log-multiplicative models • odds ratios • crosstabulations • design matrices • comparative analysis