If you use the Margins macro to fit the model and do the comparisons as shown in the note I referred to, the interpretation is fairly straightforward. In the binary example in the note, the difference in the two A levels is given at each B level in the "Contrasts of A B Margins" table. As can be seen there, the A1-A0 difference in B=1 is 0.53-0.46 = 0.07. So, the probability of the event (Y=1) increases by 0.07 in that level of B when you increase A from 0 to 1. Similarly, the A1-A0 difference in B=0 is 0.72-0.39 = 0.33, meaning that the event probability increases by 0.33 when you increase A in level 0 of B. The difference in these two probability differences is 0.07-0.33 = -0.26 which indicates that the event probability change in B=1 is 0.26 smaller than the probability change in B=0. This is a measure of the interaction between A and B on the event probability. If the change in the event probability in the two B levels were the same, then there would be no interaction. The tests of all of these differences are significant as shown in the Pr>ChiSq column. You could, of course, report these differences as percent changes if you prefer.
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