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I have a dataset of the number of social media posts by platform type (row - 3 levels) and account type (column - 5 levels). Using SAS, I've run a Fisher's exact test which has turned out to be significant and now I want to determine where the actual differences in proportion lie. For example, I'd like to determine if the proportion of posts by Account 1 on Platform1 are significantly different in comparison to all other account types. I've performed a post hoc test where I made a new dichotomous variable, 1=Account 1, 0=all other accounts and then run a Fisher's exact on this 3x2 table.

  1. I'm wondering if based on my aims, it's correct to group the columns (Account 1 vs. others, etc.) as I have for a post hoc test. Most examples of pairwise comparisons as post hoc tests that I've seen tend to group the rows rather than the columns.

  2. From my output, I get 5 p values (1 per each account type). For example, if the p value of my first pairwise comparison (see example above) is significant, then does this mean that there is a significant difference in the proportion of posts for Account1 compared to all other accounts for each of the 3 platforms?

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