Re: Repeated measures analysis of fractions summing to a constant

Posted by Ryan on Apr 04, 2013; 1:34pm
URL: http://spssx-discussion.165.s1.nabble.com/Repeated-measures-analysis-of-fractions-summing-to-a-constant-tp5719257p5719261.html

I'd like to chip in to report that since that exchange, I came across real-world data where subjects were asked to rank order items. When faced with these data, I found an elegant solution to this problem:

 

http://christakis.med.harvard.edu/pdf/publications/articles/017.pdf

 

I'm not suggesting that this article presents a solution to the OPs problem, but the article is relevant to the dependency issue. It's a good read for those who must contend with rank-ordered items.

 

Best,

 

Ryan

 

On Thu, Apr 4, 2013 at 6:30 AM, Bruce Weaver <[hidden email]> wrote:

Hello Kirill.  This is not a direct answer to your question.  I'm just
pointing to a thread from a couple years ago that addressed the same
question.  One of my posts in it gives a couple of references that may be of
interest to you.  Both of them suggest that ANOVA generally works quite well
with "ipsative" data (or "allocated observations").  You can see the
relevant messages here:

   http://listserv.uga.edu/cgi-bin/wa?A2=ind1101&L=spssx-l&P=36237

HTH.


Kirill Orlov wrote

> Consider you have a between-within design: several between-subject
> groups and several (3 or more) repeated measures (= within-subject)
> trials. It's all very classic and typical. The nuance, however, is that
> the values for every subject sum across the repeated levels to a
> **constant**. This is because the data are complementary, i.e.
> percentages of fractions, so, in this case they sum to 100 for every
> individual. For example, with 3 RM levels, a respondent's data is like
> 30%, 22%, 48% (sum=100); for another respondent 25%, 33%, 42% (sum=100).
>
> I know that I can analyze between-groups X repeated-measures count data
> via Generalized Estimating Equations procedure. By I doubt in this case
> because the values *sum to a constant*, they are complementary
> fractions; they are not counts of successes in repeated independent
> trials!
>
> Can I analyze such data in SPSS and how? Thanks.

-----
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Bruce Weaver
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