> From:
[hidden email]> Subject: Re: Repeated measures analysis of fractions summing to a constant
> To:
[hidden email]>
> Judging from what I see on the Wikipedia page
> (http://en.wikipedia.org/wiki/Compositional_data), "compositional data" is
> another name for with Shaffer called "allocated observations" and Greer &
> Dunlap called "ipsative data". But it also looks like there are two sets of
> literature that do not overlap all that much.
>
>
>
> Rich Ulrich-2 wrote
> > There is a literature on "compositional data" which probably will be
> > helpful.
> > Years ago, I found Aitchison to be readable.
> >
> > I have no idea whether it will work for your model, but I will mention
> > that you escape the absolute linear dependency if you represent each
> > fraction as its log-odds, like log(25/75) in place of 25%.
> >
> > --
> > Rich Ulrich
> >
> > Date: Thu, 4 Apr 2013 12:05:47 +0400
> > From:
>
> > kior@
>
> > Subject: Repeated measures analysis of fractions summing to a constant
> > To:
>
> > SPSSX-L@.UGA
>
> >
> >
> > 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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