My statistical model is
Dependent ~ A * Sex + B * Sex. (i.e.,
Dependent ~ A + B + A:Sex + B:Sex). I have prior reason to expect that
Sex will interact significantly with
B, but no reason to expect that
B will interact. When I run my data, I get these results.
A(significant main effect)
That is, there is a significant main effect of
A, and an interaction between
Sex. My understanding of ANOVA is that since
B:Sex is significant, I need to segregate the data by the levels of one of the factors (I will choose
Sex) and test the effect of
B on each subset separately. But, should I interpret the main effect of
A before doing that? Or should I segregate the data, and retain the independent variables
B in the test for each subset?