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Re: st: gologit2
"Christian Bustamante" <firstname.lastname@example.org>
Re: st: gologit2
Tue, 15 Apr 2008 10:58:54 -0500
Thanks for your responses, but I need to understand this right, so I
have more questions....
If the problem is in only a small subset of variables, it could be
reasonably use gologit? (I suppose, that you can find these variables
with the -brant, detail- command.
When I did the Brant test, some chi-squares values of variables
appears as negatives (and p-value equal to 1), how can in be possible?
Which representative author has written about this estimation technique?
Universidad Javeriana Cali
On Mon, Apr 14, 2008 at 5:54 PM, Verkuilen, Jay <JVerkuilen@gc.cuny.edu> wrote:
> Richard Williams wrote:
> >>My experience is that it is rare to have a model where the
> proportional odds assumption isn't violated!<<
> True, my feeling was that it was often capitalization on chance. How
> often do you find it replicated?
> >>Often, though, the
> violation only involves a small subset of the variables, in which
> case gologit2 can be useful.
> >> You might also want to consider more
> stringent alpha levels (e.g. .01, .001) to reduce the possibility of
> capitalizing on chance. You can also try to assess the practical
> significance of violations, e.g. do my conclusions and/or predicted
> probabilities really change that much if I stick with the model whose
> assumptions are violated as opposed to a (possibly much harder to
> understand and interpret) model whose assumptions are not violated.<<<
> Right, this is about what I was thinking---be a more stringent about the
> test. I wonder if anyone's done good simulation studies to see the
> properties of the Brant test?
> >>Finally, while I happen to like gologit2, there are a lot of other
> categorical and ordinal models out there that might be worth a look
> depending on the problem.<<
> I hope no one misunderstands me on this point: The generalized model is
> very useful but I think giving up the proportional odds assumption in
> the face of "small but significant" violations is a bad idea. It's kind
> of like taking the goodness of fit statistics in SEM or confirmatory
> factor analysis too seriously.... If the violations are real, that's a
> totally different question.
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