Should I expect changes in my predicted probabilities when increasing the number of categories I am predicting membership in? I calculated the predicted probability of membership in group A and not A with binary logistic regression.  Then I broke the not A category into two distinct buckets of which it is comprised - B , C. I then used multinomial logistic regression to calculate the predicted probabilities of membership in A, B, C.
These two different predictions showed some variation in my probabilities.




Category
Binary Log.Prob
Revised Categories
Multinomial Log. Prob




A
74%
A  (same as orig)
67%


Not A
26%
B (B + C = not A)
12%




C
21%




For example the estimate of A changed from 74% to 67% and the not A estimation changed from 26% to 33% (B + C).  Is this expected? And what might account for these differences?

This is fine. The either of the B or C categories might look more or less like A than the combined BC category. In that case, the model might be more or less willing to predict high probabilities of A, since there is a greater or lesser chance of being B or C.
