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June 2003

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Subject:
From:
"Brian M. Schott" <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Date:
Tue, 10 Jun 2003 11:15:53 -0400
Content-Type:
TEXT/PLAIN
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On Tue, 10 Jun 2003, Ufuk Yildirim wrote:

+Hi everyone,


+Now my real problem, as I enter the data in SPSS, I use the
+subjects' ratings of the pairwise similarities for the 30
+concepts. I want to know which of these is the appropriate
+statistical analysis for my analysis. I am confused with
+the metric/non-metric distinction. My data is non-metric I
+think. Can I use HCA with non-metric data? If I can, and if
+HCA is appropriate, what is the best method? Ward's?
+Between-groups linkage? or within-groups linkage? etc.
+Since my original data is already a proximity matrix (or at
+least I think it is), what HCA is doing seems to be wrong.
+It tries to create proximity matrix again. Is this ok? When
+I run the analysis as it is, it seem fine, but when I
+change the syntax so that it uses the original data matrix
+in /MATRIX IN ('filename.sav'), a totally different
+clustering is produced. Which one is correct? Is there a
+clearly written book on multivariate analysis using SPSS?

Ufuk,

<disclaimer>
        I am unfamiliar with SPSS, but familiar with
SAS.
</disclaimer>

        It sounds as if your data is almost metric: each
observation is a whole number rating between 1 and 9,
reflecting the preference for one item over another. If a
subjects data were arranged in a square matrix, the matrix
would be antisymmetric unless you recoded either the upper
or lower triangular cells.

        I think you want to supply SPSS with a (lower
or upper) matrix input in which the cells are averages
across subjects, and in the /MATRIX IN manner (remember the
disclaimer, though).

+
+
+Ufuk YILDIRIM

(B=)

Brian Schott/Decision Sciences                          [log in to unmask]
1000 J Mack Robinson College of Business      /      Georgia State Univ
Atlanta, Georgia USA 30303-3083           interests: approx. reasoning,
http://www.Gsu.EDU/~dscbms/         (B=)       decision support systems

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