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RE: st: gologit2
A student and I have about 1300 U.S. state-years in a pooled time
series analysis of a state legal outcome that is measured as an
ordinal scale (I plan to cluster on the state IDs to adjust for the
pooled nature of the data or to use the pooled ordinal estimators in
Limdep if I have to).
I understand, of course, how to use the BIC test to compare models,
but I don't understand how this test can be used to test for the
absence of proportionality in an ordinal logit of probit analysis.
By the way, I can't get slogit to work at all (the Stata rountine
won't give estimates) perhaps (?) because we have too many ranked
outcomes in this dependent variable.
Thanks for your help in advance.
Dave Jacobs
BIC test to check out departures
At 04:15 PM 4/17/2008, you wrote:
--- David Jacobs <[email protected]> wrote:
> > I don't yet find a solution for how to handle referees who often
> > can be mechanical and rigid about the departures they will allow
> > from the most conservative textbook practices.
--- Maarten buis <[email protected]> wrote:
> The easiest way to make everybody happy when the Brant test rejects
> the null, is to show an ologit and a gologit, and (hopefully) show
> that the results are very similar. Then you can refer to the
> distinction between statistical and practical significance, or the
> conclusion does not change, etc.
Alternatives are:
o If your dataset is large you can use (Raftery 1995), he does not deal
with -ologit- specificaly, but more generally with hypothesis
testing/model selection in large datasets. Than you can compare BICs,
these are less likely to reject the proporitonal odds assumption, and
if they do show strong evidence against the proportional odds
assumption you should probably be worried anyhow.
o You can use other methods for dealing with ordinal dependent
variables that are not surrounded by these ingrained practices, for
instance the stereotyped ordered logit (-slogit- in Stata). This is a
cheat, but if it gets you around the referees...
-- Maarten
Raftery, Adrian E. (1995). Bayesian model selection in social research
(with Discussion). Sociological Methodology, 25, 111-196.
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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