Posted by
Rich Ulrich on
Apr 23, 2019; 8:20pm
URL: http://spssx-discussion.165.s1.nabble.com/Linear-Mixed-Model-in-SPSS-Guidance-tp5737698p5737713.html
I've used that approach that Art suggests - If both analyses get the
same results - using raw data, and also rank-transformed data (for "ordinal") -
then I've given the results one way, commenting that the choice does
not make a difference.
Also, I have reported results using that strategy where the choice was raw
versus log-transformed, or whatever. I do not care much for rank-transformed
analyses for several reasons. One of those reasons is that they do not behave
well (in fact, may introduce artifacts) when the analysis is multi-variable. An
/appropriate/ power-transform (log, root, reciprocal) will always serve better.
Martin Sherman -
(a) leaves open the chance that Age is correlated with the Outcome score;
(b) reports that Age is of special interest to the PI; and
(c) says that the Age-dichotomy is created from a continuous score.
If Age /is/ a confounder for the score, I am sure that I would want to
look at the scattergrams for raw age vs Score, separately for Pre and Post,
to confirm that the "bell shape" holds up and that nothing peculiar is
going on at any extremes. (How extreme are those outliers?)
I think I would try to do the analyses with Age as continuous, though I can
see that a dichotomy can sometimes make it easier to display what you
want to display.
--
Rich Ulrich
There are times, like in this instance, there are different approaches such
as covariance vs change scores, or ordinal vs interval assumptions on level
of measurement. In my experiencing, it is often informative to try both
approaches and compare the substantive conclusions. If the substantive
conclusions differ, a report would need to discuss the differences and the
implications for further reasoning. If the substantive conclusions are
quite similar, i have usually suggested using the approach most familiar to
the wider audience and mentioning that the alternative approach led to
similar thinking.
-----
Art Kendall
Social Research Consultants
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