Date: Sun, 19 Nov 2006 17:58:44 -0500
Reply-To: Statisticsdoc <firstname.lastname@example.org>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Statisticsdoc <email@example.com>
Subject: Re: Repeated measures analysis
Content-Type: text/plain; charset="iso-8859-1"
For expository purposes, giving the reader Cohen's d for each contrast would
do very well, I think. I would also suggest including a graph of the means
for each point. Sometimes trends are easier to see and to show than they
are to write about.
You may want to include a footnote stating that the quadratic trend was
significant. The reader can skip the footnote without losing the point, and
the more statistically-minded among us will note that you tested the effect.
For personalized and professional consultation in statistics and research
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU]On Behalf Of
Sent: Sunday, November 19, 2006 2:58 PM
Subject: Re: Repeated measures analysis
Thank you Stephen,
A trend analysis is a very good idea. The publication is aimed at a
nontechnical audience however, and I do find that the simple word
'quadratic' may cause considerable anxiety with some people (or they simply
skip over that part.) I had another idea, on which I thought to ask your
opinion. What if I do a repeated measures ANOVA with repeated contrasts (t1
vs t2 and t2 vs t3) accompanied by Cohen d statistics for EACH contrast? In
fact there is significant change during the waitlist on several measures due
to people's natural improvement and perhaps regression to the mean.
However, it is trivial compared to the change during the treatment period.
The results of the contrasts and the associated d statistics would show the
relative magnitudes of the change during the two periods although there
would be no direct statistical test of the difference.
Thanks again for your input on this,
>From: Statisticsdoc <firstname.lastname@example.org>
>Reply-To: Statisticsdoc <email@example.com>
>Subject: Re: Repeated measures analysis
>Date: Fri, 17 Nov 2006 22:12:05 -0500
>I think that this is a question of carrying out a trend analysis across
>time. The issue is whether the difference between time points is linear,
>whether a curvilinear effect is also needed to fit the data (a particular
>type of curvilinear trend in which the change from T2 to T3 is greater than
>the linear slope between time points). I imagine that you have plotted the
>means of the measures at each time point, and that what you are looking for
>is a formal statistical procedure for estimating the strength and
>significance of the curvilinear trend).
>SPSS provides the tools you need to test the within-subject trends. GLM
>provides the tools to test the significance of the quadratic trend. You
>might also want to consider using the TEST option within the MIXED command
>in SPSS to examine the shape of the within-subject trends over time.
>For personalized and professional consultation in statistics and research
>From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU]On Behalf Of
>Sent: Friday, November 17, 2006 12:52 PM
>Subject: Repeated measures analysis
>My clients have carried out a careful treatment evaluation study. One of
>the groups received a battery of assessment tests at time 1, then were on a
>waiting list for about 16 weeks. At time 2 at the beginning of treatment
>they received the identical battery of tests and then at time 3 after 16
>weeks of treatment they received the battery of tests again.
>I have been asked to provide an analysis which has supporting statistical
>tests which would prove that the change from time 2 to time 3 (pre- to
>treatment) was greater than the change from time 1 to time 2 (pre- to
>post-wait list). The usual analysis of repeated measures would not answer
>this exact question and so with some doubt in my mind I formed change
>by subtracting time 2 scores from time 1 and time 3 scores from time 2.
>results of a repeated measures analysis on these scores were quite
>significant and reasonable and I wrote them up. I might mention that this
>is a sub-analysis rather than the principal analysis.
>A reviewer has criticized, suggesting that I divide the time 1 and 2 scores
>by the time 2 SD and the time 2 and 3 scores by the time 3 SD prior to
>forming the difference scores. This suggestion is not sitting well with
>and I thought to ask your advice as to other possible analyses which would
>answer the question posed.