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Re: st: Interpretation of Curvilinear Effects


From   Richard Williams <Richard.A.Williams.5@ND.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>, "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Interpretation of Curvilinear Effects
Date   Tue, 09 Jun 2009 15:32:18 -0400

At 02:42 PM 6/9/2009, John Antonakis wrote:
Hi:

First, don't use stepwise regression--it is the plague; no worse. Many
journals simply won't even review manuscript with such data-driven
methods (unless used for a particular goal--ridge, least-angular
regression). For instance, see:
It may depend on how you package it.  On the one hand, stepwise 
regression is the work of Satan; on the other hand diagnostic tests 
are good.  So, one might just run the -estat ovtest- command and, 
based on it, decide that some sort of non-linearity should be allowed 
for in the model.
Also, I agree that plotting is a good idea, but really, how much 
different is that than stepwise regression?  Either way, you are 
basically looking at the data and deciding what to do with it.  Both 
eyeballing the data and stepwise regression have the potential to 
make your significance tests deceptive, because you are using 
knowledge gained from the data itself and hence potentially 
capitalizing on chance in building your model.  I guess the human 
judgment aspect of plotting appeals to me over stepwise, but I also 
think it raises some of the same problems and concerns.
Also, I don't know why you would need stepwise regression to justify 
the possible inclusion of an x^2 term in the model.  You can often 
think of good substantive reasons for a curvilinear relationship.  It 
therefore seems reasonable to me to test whether an x^2 term belongs 
in the model.  I would certainly rather have some a priori theory in 
there about an x^2 term rather than just seeing if it happens to make 
it into a stepwise regression.
My own view is that mindless stepwise is indeed the work of 
Satan.  However, I think stepwise is potentially useful as a 
diagnostic device, e.g. is there reason to believe that my model may 
have omitted important variables?  Even so, I think it is better if 
you have clear alternative or rival hypotheses in mind and explicitly 
test them rather than go on a fishing expedition with sw.

-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

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