Re: st: test for appropriateness of logit models

[Andreas Chouliaras <adhoul@gmail.com>]

Dear Marten & Joseph,

Thank you very much for your precious comments and the material you
suggest. I will study this information and come back to you.

Best regards,
Andreas

On Sun, Jun 16, 2013 at 7:43 AM, Joseph Coveney <stajc2@gmail.com> wrote:
> Maarten Buis wrote:
>
> On Sat, Jun 15, 2013 at 1:21 PM, Andreas Chouliaras wrote:
>> I would like to apply a test to check the appropriateness of a
>> multinomial vs an ordered logit model (or another flavor such as
>> gologit2).
>
> It would make most sense to compare an ordered logit with a full free
> generalized ordered logit. After a gologit a mlogit would make little
> sense; an mlogit would describe the pattern with the exactly same
> number of parameters, the difference is only which outcome categories
> are compared. Richard Wiliams and I discussed at the last German Stata
> Users' meeting the different tests that are available for testing an
> ologit against a gologit. The slides can found here:
> http://www.maartenbuis.nl/presentations/gsug13.pdf
>
> ---------------------------------
>
> I wonder if Andreas is referring to a situation where, say, a three-category
> response variable is said  to be conceptually ambiguous (or is controvertible)
> as to whether the categories are ordered or not.  Something like "A", "B", and
> "some characteristics of A/some characteristics of B".  Some subject-matter
> experts might be inclined to consider the response variable as representing
> B-ness (or A-ness), and the scores run from "none or a little" to "some" to "a
> lot or all".  While others view it as nominal, with scores of A, B and "not
> really either".
>
> It seems that generalized ordered logistic regression could suggest a principled
> approach to these situations.  For example, after considering Richard Williams's
> July 2006 follow-up presentation on -gologit2- (see links at
> http://www3.nd.edu/~rwilliam/gologit2/ ), maybe you can first determine whether
> you end up with unacceptable negative predicted probabilities with relaxed
> constraints, and if not, then determine whether the totally unconstrained model
> is the "best fit" according to one or another criterion, and if so then toss a
> coin to decide whether to just use -mlogit- with a convenience choice of
> baseline score.  (This abbreviated example of course doesn't take into account
> matters of response dimensionality and heterogeneity that Richard Williams
> describes in his presentation.)
>
> Do list members have alternative advice as to how to approach analysis in these
> situations based upon what's worked for them?  Perhaps it boils down to the
> nitty-gritty details of the specific research question being addressed (which
> would be a safe answer), and there's no generally applicable protocol for
> approaching these situations.
>
> Joseph Coveney
>
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-- 
Andreas
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