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Re: Linear Mixed Model in SPSS Guidance

Posted by Rich Ulrich on Apr 23, 2019; 4:00pm
URL: http://spssx-discussion.165.s1.nabble.com/Linear-Mixed-Model-in-SPSS-Guidance-tp5737698p5737704.html

Books have been written about the analysis of change scores. The choices
may be described as "change" (repeated measures), "regressed change"
(ANCOVA), and "other" (special, awkward considerations that hopefully
do not arise).

The problems for inference that are most frequent arise when the
initial groups are not matched on the Outcome score -- And you have
Age as a factor, which is very often correlated with everything. Is that
a problem for your data? (Of course, there also should be Random
assignment to the treatments shown by similar means for those
groups, or the inference problem is even worse.)

When initial scores are not matched, THEN, especially, you  need to worry
that the "scaling" of an outcome might be "wrong" so that it introduces
apparent effects that are artifacts of scoring. 

Artifacts: For instance, if everybody doubles their score from Pre to Post
on a scale where you should have taken the logs, then the initially-higher
scoring group will show greater change. Or, the opposite, for a scale with
a max:  If there is a "ceiling effect", then the initially-higher group has
little room to improve and will show less change.

--
Rich Ulrich


From: SPSSX(r) Discussion <[hidden email]> on behalf of Martin Sherman <[hidden email]>
Sent: Tuesday, April 23, 2019 9:00 AM
To: [hidden email]
Subject: Linear Mixed Model in SPSS Guidance
 
Dear List:  I am working on  pretest/post-test study with two between group factors, Treatment (Therapy A vs. Therapy B) and Age (younger vs. Older) on various outcome variables (all continuous). I originally considered doing a  repeated measures analysis but after reading up on the pros and cons of such an analysis I decided that a linear mixed model would be more appropriate given the correlation between the pre-test scores and the post-test scores. To further my understanding I reviewed the text by Verbeke and Molenberghs (Linear Mixed Models for Longitudinal Data). Getting through the text proved to be a challenge (many many equations beyond my pay grade). So I starting looking for some dummy downed explanations on how to set up my statistical model. So far that have not generated any comparable examples of my design  (2 x 2 x (2)). I am hoping there are some folks on the listserve that might be able to point me in some directions that will prove to be beneficial. I have googled but I have not found any helpful tutorials. Per chance if anyone has a good tutorial for my design I would appreciate hearing from you.  Thanks,  martin sherman

Martin F. Sherman, Ph.D.
Professor of Psychology
Loyola University Maryland
4501 North Charles Street
222 B Beatty Hall
Baltimore, MD 21210
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