On 7 Feabh 2007, at 00:06, Adam Seth Litwin wrote:
Hello. I just ran a cluster analysis, not a technique I use
frequently. I have seven binary variables forming, at the moment,
five clusters. I thought a useful exercise would be the following:
For each of the seven variables, examine its mean in all five
clusters. Then, run an F-test to show that the means are not equal
across all five clusters.
So, for example, I type
- tabstat var1, by(CLUSTER) stat(n mean)
But, I'm not sure how to run the F-test.
Careful. An analysis of variance is a hypothesis test. The model is
specified in advance and the anova calculates the values of the model
parameters.
In your case, the model was generated from the data. The usual
interpretation of the F ratio does not apply.
Cluster analysis is an exploratory technique. You need to think about
validating the clustering by showing that the clusters differ on
variables which were not used in the clustering but which are
theoretically related to the cluster process.
For example, if you use clustering to define five clusters of people
based on the type and frequency of their social interactions, then
you would expect that the clusters would differ on things like
loneliness and perceived social support, and you would hope that they
differed in dimensions like mood or (headline from this month's
Archives of General Psychiatry) risk of Alzheimer's disease.
So I'd forget the F-test and start validating the clusters. Your
hypothesis is that the clusters are different from each other in some
respect other than the variables you clustered on.
=========
Ronán Conroy
Royal College of Surgeons in Ireland
rconroy@rcsi.ie
+353 (0) 1 402 2431
+353 (0) 87 799 97 95
http://www.flickr.com/photos/ronanconroy
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