"Donald F. Burrill" schrieb:
> 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.
Thank you very much for your answer. This is quite what I thought
and why I looked for a way to include or remove specific effects.
And this is why I'm astonished by the fact SPSS does not allow me
to customize within * between factors in its standard procedures.
I guess I'll rather try different Software than using regression with
dummy variables or something, but thanks to all for the tips!
Best wishes,
Johannes
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