Re: st: What is the effect of centering on marginal effects?

[David Hoaglin <dchoaglin@gmail.com>]

Dear Will,

A more careful statement is needed.

In some sets of data, the constant predictor is involved in a
collinearity relation (conceivably more than one such).  In those
situations, centering the other predictors involved will definitely
reduce (but perhaps not resolve) the problem(s).

If the constant predictor is not involved in any collinearity
relations that are present, centering will not affect them.

David Hoaglin

On Fri, Aug 3, 2012 at 8:13 AM, William Hauser <whauseriii@gmail.com> wrote:
> Dear all,
>
> I'm fairly confident that mean centering does nothing to resolve
> collinearity.  I believe it does fool some of the diagnostic tools
> though and that's probably why the belief that it somehow solves the
> problem persists.  Mean centering simply shifts the collinearity onto
> the intercept term.  Mean centering adds no new information to the
> model and that's the problem - the data lack the necessary information
> for the model to partial out the effects in a precise and stable
> manner.  Perhaps this is effect is different for interaction terms,
> but I fail to see how that's the case.
>
> Collinearity means the independent effects of the collinear variables
> cannot be precisely estimated.  The point of interaction terms is that
> they be analyzed jointly anyway.  The use of the margins and
> marginsplot commands accomplish this with such ease (and polish) that
> I would heartily recommend their use.
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