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RE: RE: st: Gologit, ologit and Akaike's Information Criterion


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: RE: st: Gologit, ologit and Akaike's Information Criterion
Date   Tue, 21 Sep 2004 14:50:16 +0100

I'd echo Maarten's incisive comments here. 
In fact, the statement by Long and Freese is temperate. 

Consider also the comment by Richard A. Berk 
in "Regression analysis: a constructive critique" 
Sage, 2004, p.107: 

"The common reliance in regression analysis on 
variance explained and other measures of fit 
is too often a substitute for hard thinking 
about how useful a model really is."  

The context is regression, narrower sense, 
but I'm clear that the author would regard
the comment as more widely applicable. 

The whole is a tough, sustained, well written, 
no punches pulled critique of much that many 
of us do. 

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

Maarten Buis
 
> More generally, I am sceptical about using a single measure 
> of goodness-of-fit (including R^2 and the various pseudo 
> R^2's), and I am not alone in that respect. To quote from the 
> Long and Freese book I recommended earlier (p.88): ``[T]here 
> is no convincing evidence that selecting a model that 
> maximizes the value of a given measure results in a model 
> that is optimal in any sense other than the model having a 
> larger (or, in some instances, smaller) value of that 
> measure.'' Apparently, the authors think this is a very 
> important point since this sentence is printed in italics. 

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