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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.


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Richard Williams, Notre Dame Dept of Sociology
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