I am looking at a $\chi^2$ (crosstabs) test looking at interactions between types of dyads in an animal group. My categories are Male-Male, Male-Female, and Female-Female dyads in the rows and "Were Seen to Interact" and "Were Never Seen to Interact".
The matrix looks something like:
21 7 85 19 77 1
There is a very significant p value. My question is, when running the post hoc tests, is it appropriate to compare each category to the sum of the other two (i.e. Male-Male, vs Male-Female+Female-Female) when looking for which category is driving the difference, or should I compare the categories to each other (Male-Male vs Male-Female, then Male-Male vs Female-Female)?
I'm looking for an answer to the question of whether one of the types of dyads are more likely to fail to interact. I think the statement "There are more Males-Female dyads that were never seen to interact than would be expected by chance" would be easier to comprehend than if I had to write out the comparisons of the three pairs. I just don't know if it's allowable.