On Wed, 10 May 2000, Johannes Hartig wrote:
> I guess I have to accept there is no way to customize within * between
> interactions in GLM. Thanks for the tip using regression, but I think
> in future I'd rather try to give a meaning to the default interactions
> ;-) This leads me back to the second part of my original question: Is
> there some good statistical reason *against* removing e.g. a
> covariate * within-factor interaction from a repeated measures model?
Well, that depends. If the data contain an interestingly strong and
significant interaction, one wouldn't want to remove it, would one?
The existence of such an interaction implies that the slope of the
regression line of (response varible) vs. (covariate) differs from cell
to cell of the design; I'd surely want to examine those differences
before deciding to throw them out. (They might, for instance, be trying
to tell me that I should be dealing with, say, the logarithm or the
square root of the covariate, rather than with the covariate in its
original form.)
On the other hand, if you are determined that the within-cell
regression slope for this covariate will be the same in all cells (that
is, the regression lines will be strictly and exactly parallel throughout
the design), regardless of what the data may be trying to convey, then
removing the interaction will do that. (Doing that will also provide
useful information for comparing the model with interaction to the model
without interaction, of course.)
It is always a fair approach to ask, and then to show, how much
the inclusion (or not) of one or more terms in a model affects the
results of the analysis.
-- DFB.
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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 603-471-7128
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