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Re: st: gologit2


From   "Christian Bustamante" <cdeb77@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: gologit2
Date   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?

Christian Bustamante
Economics Student
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.
>
>  Absolutely.
>
>
>
>  >>  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.
>
>  Jay
>
>
>
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-- 
CdeB
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