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RE: st: KcKelvey & Zavoina's R2 in -fitstat- after -gologit2-


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: KcKelvey & Zavoina's R2 in -fitstat- after -gologit2-
Date   Thu, 6 Mar 2008 16:25:30 -0000

I like the idea of reporting lots of these, especially if it underlines
that the various pseudos are measuring 
various different things and will not agree numerically (although
perhaps they will often rank different models
more or less consistently). 

Still, that won't stop 

People asking, "But which is best?" 

People wondering if there is a way of averaging or combining the
pseudos. 

People cherry-picking what seems "best" for their favourite story. 

Naturally, I do the latter all the time when I choose graphs, but I put
that down to experience and judgment.... 

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

Mike Lacy
>
 >I have fitted a partial proportional odds model in Stata 9.2 using
 >- -gologit2-. In their book, Regression Models for Categorical
Dependent
 >Data using Stata, Long and Freese suggest that McKelvey & Zavoina's R2
 >most closely approximates the R2 from linear regression models

This is not necessarily true. That is a good measure, but 
see:  (self-promotion mode on)

Lacy, M. 2006.  "An Explained Variation Measure for Ordinal  Response 
Models with Comparisons to Other Ordinal R2  Measures."  Sociological 
Methods and Research  34:469-520.

For software (not sufficiently documented to merit submission to 
SSC): http://lamar.colostate.edu/~socmgl/r2o.ado

I did find, however, in parallel to what Richard Williams suggests in 
another post, that while various ordinal pseudo-R^2 fit measures 
might differ in their ability to approximate an underlying linear 
model R^2,  all the various measures have pretty comparable 
performance as aids to model selection.

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