On Tue, 20 Jun 2000, Rich Ulrich wrote:
> On 19 Jun 2000 18:01:28 -0700, [EMAIL PROTECTED] (Dónal Murtagh) wrote:
>
> < ... >
> > Firstly, thank you for your comments. Am I right in saying that the two
> > (equivalent) options I have are:
>
> These are not quite equivalent options since the first one really
> stinks --
Sorry, Rich, I must take issue with yoku. If the first option really
stinks, so does the second: they are, in fact, equivalent, as Donal
describes the second (with dichotomies for X1 and X2).
> If you are considering drawing conclusions about causation,
This is a fair enough warning, I suppose; but I don't recall reading
anything in the original post that implied that it was desired to show
causation. (Can't think of anything that expressly denied it either;
but I still think you're reading it into, rather than out of, the
problem.)
> you need *random assignment* and the two Groups of performance are the
> furthest thing from random.
For that matter, had it been specified that the treatments were assigned
at random? In any case, I'd be interested in knowing how you would
propose that performance might be assigned at random. ;-)
> Let's see: the simple notion of regression-to-the-mean says that the
> Best performers should fall back, the Worst performers should improve;
> that's a weird main-effect, which should wreak havoc with interpreting
> other effects.
> Or: If the Pre is powerful enough to measure potential, then a
> continued-growth model says that Best performers should improve more,
> even given no treatments.
Ummm... I think you have to postulate that the POST is powerful enough,
unless you're assuming that the Pre and Post measures are identical
(which they may be, of course; though that introduces other measurement
issues).
> For simple change-scores (and ANOVA interactions) from dichotomous
> groups, you assume that neither of those possibilities are true, if
> you want to be able to interpret them.
Only if you want to be able to interpret them SIMPLY.
> The Regression model at least places the contrasts into the realm
> of comparing the regression lines.
Yes, provided one is modelling
the pretest as a continuum rather than as a coded dichotomy, as Donal
described it.
> Your fundamental knowledge of what is happening will probably come
> from examining and comparing the scatterplots, pre-post, for the two
> treatments. (Another thing to note from the picture: Are there
> ceiling/basement effects on the performance test?)
Good advice. I concur.
> - Treating it as a continuum is better by a lot, even if you were
> sure that the Performance scale
> was close to the ANOVA-analytic ideal, a normal distribution.
Did you mean the ERRORS (or residuals) in the Performance scale, perhaps?
-- Don.
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