Randomization was used with 100 per treatment group. The two groups were equated on all demos and on the pretest. The age variable was equal between the two treatment groups before creating the
binary age variable (of special interest to the PI-younger vs. older). All pretest score distributions and post test scores distribution are bell shaped with some outliers for both groups.
From: Rich Ulrich <[hidden email]>
Sent: Tuesday, April 23, 2019 12:00 PM
To: [hidden email]; Martin Sherman
Subject: Re: Linear Mixed Model in SPSS Guidance
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
===================== To manage your subscription to SPSSX-L, send a message to
[hidden email] (not to SPSSX-L), with no body text except the command. To leave the list, send the command SIGNOFF SPSSX-L For a list of commands to manage subscriptions, send the command INFO REFCARD
| Free forum by Nabble | Edit this page |