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