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st: RE: RE: creating combined correlation of dummy (ordered multilevel)


From   Nick Cox <n.j.cox@durham.ac.uk>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: creating combined correlation of dummy (ordered multilevel)
Date   Thu, 9 Jun 2011 17:06:16 +0100

That is, and guessing at what is going on here, something like

gen combined = AAA + 2 * AA + 3 * A + 4 * BBB + 5 * BB + 6 * B 

would map 1-0 dummies to 1-6, but your correlation will be very sensitive to the weights used to produce -combined-. 

Nick 
n.j.cox@durham.ac.uk 


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
Sent: 09 June 2011 17:02
To: 'statalist@hsphsun2.harvard.edu'
Subject: st: RE: creating combined correlation of dummy (ordered multilevel)

A correlation coefficient is only likely to have meaning if the combined variable in question is at least on an ordered scale. Even to calculate a meaningless correlation for a six-category scale you would need to have such a combined variable. 

Nick 
n.j.cox@durham.ac.uk 

Stefan Nijssen

I am struggling to find the correlation of various variables to a dummy variable (6level ordered). The problem is that since the dummies appear in the table as six variables, I cannot read a single correlation between the combined dummy and any other variable (see below). Does anyone know how to do this?

Stefan Nijssen

. correlate AAA AA A BBB BB B wc5 MAR COV
(obs=383)

             |      AAA       AA        A      BBB       BB        B      wc5      MAR      COV
-------------+---------------------------------------------------------------------------------
         AAA |   1.0000
          AA |  -0.0303   1.0000
           A |  -0.0767  -0.1249   1.0000
         BBB |  -0.1187  -0.1932  -0.4892   1.0000
          BB |  -0.0559  -0.0909  -0.2302  -0.3563   1.0000
           B |  -0.0504  -0.0820  -0.2077  -0.3214  -0.1513   1.0000
         wc5 |   0.3377   0.1016   0.1805  -0.1683  -0.0769  -0.1032   1.0000
         MAR |   0.0030   0.0101   0.0146   0.0449  -0.0136  -0.0807   0.0176   1.0000
         COV |   0.3052   0.1377   0.2432  -0.0967  -0.1235  -0.2545   0.2121   0.1220   1.0000


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