Hello,
I would like to offer one comment to this excellent thread on AIC...
Dave Hewitt wrote, "Mixing inferential paradigms is poor, and I
don't think we need shrines, scripture, or dogma in this discussion. For
one, a direct comparison of correct analyses from the two paradigms can
lead to different conclusions (I've seen it). How then can any theory
support putting them together? Often an author does this because they
show the same story, which is "analysis dredging." If you show both and
they disagree, and you don't choose one, there is no theory to guide you
to a proper conclusion (of course, the data may not be sufficient to
provide such)."
If I'm following Dave's thread correct, I disagree with him that "no
theory supports putting them together." Indeed, if 2 different measures
of fit (i.e., delta AIC value and r^2) support different conclusions, it
would be wrong (i.e., disingenuous) to withhold one value. I think you
need to provide both and either present an argument why one is better
than the other or indicate the this means your data set may not be
robust enough to answer the question at hand.
Otherwise, I agree with the general sentiment that running all models
may not be the preferred approach, but is suitable when apriori
knowledge is insufficient for justifying the inclusion of some models
while precluding others.
Michael Cooperman
