SS: What I have found is that X interacts with Y, and that interaction term is significantly contributing to the model's performance (i.e., the difference between fit1 and fit1A (where the interaction parameter is removed) is significant). I took this to mean I cannot individually remove X from the model and compare the two fits; therefore, I concluded X is significant for fit1. The same is true for fit2 (i.e. X2 interacts with Y). Please let me know if you see a flaw in my conclusion.
that's all fine, but in order to see whether X1 and X2 matter both, you still have to do the comparison i talked about (but include the interactions for X1*Y and X2*Y in the model I called superfit in my previous email; i.e. compare fit1A vs. superfitA and fit2A vs superfitA). 3. could I use the same formula to examine only one "Age" group (removing
"Age" as a factor in the formula, of course), even if I am going to later proceed and re-examine the data for a larger young/old set? I am not sure that I understand what you mean. Nothing prevents you from exploring e.g. only data from "young" people, but be aware that whatever conclusions you draw out of the examination of that subset of your data, may not generalize to the entire sample (and hence not to the population represented by the entire sample). SS: the data shows an effect of "Age" (which has two levels "young" and "old"). I wanted to test a subset of the data that included only "young" and see whether there is an interaction between X and Y, for them as a subset. I was thinking that the result may be generalized to only "young" population, and not the entire sample. Just want to make sure that I am not doing something that has problems that I am not aware of. I hope I was able to state the question more clearly.
yes, this is ok. be aware, however, that by splitting the data set you loose power. so failure to detect the X*Y interaction MAY be due to lack of power. an additional test you could confirm is a three-way interaction for the entire data set X*Y*Age. If it is NON-significant that is a further indication that the X*Y interaction holds for both age groups. finally, I always advise visualization of the data set (as a way to compare the effects of X*Y for young vs. old people). is this useful? I am cc-ing the language list, so that other people can correct me. Florian Thanks very much.
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