On Mon, 23 Apr 2001, jim clark wrote:
> On 22 Apr 2001, Donald Burrill wrote:
> > If I were doing it, I'd begin with a "full model" (or "augmented model",
> > in Judd & McClelland's terms) containing three predictors:
> > y = b0 + b1*X + b2*A + b3*(AX) + error
> > where A had been recoded to (0,1) and (AX) = A*X. [1]
>
> A number of sources (e.g., Aiken & West's Multiple regression:
> testing and interpreting interactions) would recommend centering X
> first (i.e., subtracting out its mean to produce deviation scores).
Yes, this is always an option. Usually recommended to avoid certain
computational problems that may arise if the distribution of X has a
particularly low coefficient of variation, for example, and if the model
contains many variables (and in particular interactions among them).
Such problems are unlikely to arise in so simple a model as [1], and are
more effectively dealt with when they do arise by deliberately
orthogonalizing the predictors. I've never quite understood why
deviations from a sample mean, which is after all a random function of
the particular sample one has, should be preferred either to the original
values of X (unless there ARE distributional problems) or to deviations
from some value inherently more meaningful than a sample mean.
> You might also consider whether dummy coding (0,1), as recommended by
> Donald, would be best or perhaps effect coding (-1, 1).
Also a possibility, of course. Note that the interpretations of the
several coefficients (b0, b2, and b3 in particular) change with changes
in coding of the dichotomy A.
-- DFB.
------------------------------------------------------------------------
Donald F. Burrill [EMAIL PROTECTED]
348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED]
MSC #29, Plymouth, NH 03264 603-535-2597
184 Nashua Road, Bedford, NH 03110 603-472-3742
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