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Re: st: orpobit vs ologit


From   Richard Williams <[email protected]>
To   [email protected]
Subject   Re: st: orpobit vs ologit
Date   Wed, 11 Apr 2007 20:10:43 -0400

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


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Richard Williams, Notre Dame Dept of Sociology
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