I am debating how to construct an interaction plot with my supervisor.
We have a dataset comprising 8 independent variables. We are trying to analyse the effect of 2 of the 8 independent variables on the dependent variable.
My supervisor is suggesting that to draw an interaction plot, we first fit a full model using all of the 8 independent variables we have, and for every possible combination of the levels of the 2 independent variables that we are particularly interested in (let's call them
var2), calculate their predicted value based on the full model that we constructed earlier. However when applying this method I was running into a problem because in order for my statistical software to make a prediction, I had to assign values for the 8-2=6 variables that are left in the dataset, which are undetermined. So I suggested to my supervisor that instead of relying on the full model for calculating predicted values, I fit a model like the one below:
y = var1 + var2 + var1*var2
(i.e. instead of
y = var1 + var2+ var3 + var4 + var5 + var6 + var7 + var8 + var1*var2)
My supervisor, however, disagrees with my view and is telling me to go on by using the mean values of
var3, var4, var5, var6, var7, var8, which I can calculate from our original dataset, to come up with predictions.
Is there something wrong with my method of analysing the effect of interaction? I prefer my method because the interaction plot looks much better with my method. However, if my method is theoretically wrong then I guess I have to stick with what is said by my supervisor.