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Re: st: orpobit vs ologit
At 03:08 PM 4/11/2007, Nick Winter wrote:
Whatever the theoretical differences (in terms of assumptions about
the distribution of the latent response variable, conditional on
covariates; not the marginal distribution), in practice both will
give you substantively the same results. (The coefficients will be
different because they are normallized differently, but the size of
effects in terms of substantively meaningful things like predicted
probabilities will be indistinguishable.)
I agree. The choice may just boil down to what the common practice is
in your field. Seems to me like I see a lot of logit/ologit work in
Sociology, and also in the occasional medical-related research that I
read. In economics I see probit/oprobit being used some.
The bigger issue may be whether you should use either ologit/oprobit
in the first place. The assumptions of the ologit/oprobit models are
often violated. Some programs try to deal with these violations,
e.g. my own gologit2 and oglm programs available on SSC. Another
possibility is just to forego the ordinal route completely and
estimate an mlogit model. There are also other ordinal
regression-related routines out there, like slogit and ocratio.
As noted before, Long & Freese's book is a good place to learn about
these things. See
http://www.stata.com/bookstore/regmodcdvs.html
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
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