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.
*
* For searches and help try:
* http://www.stata.com/support/faqs/res/findit.html
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/