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Re: st: logistic versus probit


From   "Hyojoung Kim" <hyojoung@u.washington.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: st: logistic versus probit
Date   Tue, 14 Jun 2005 14:52:38 -0700

Dear Richard,

thanks for your quick reply. The "brief overview of alternatives" is
immensely helpful. although the scobit and cloglog do not seem appropriate
for my data, it does worked to sensitize me to the substantive issues
associated with different distributional assumptions. Thank you again.

hyojoung

(E-mail) hyojoung@u.washington.edu
----- Original Message ----- 
From: "Richard Williams" <Richard.A.Williams.5@ND.edu>
To: <statalist@hsphsun2.harvard.edu>
Sent: Tuesday, June 14, 2005 10:08 AM
Subject: Re: st: logistic versus probit


> At 09:48 AM 6/14/2005 -0700, Hyojoung Kim wrote:
> >Dear Statalisters,
> >
> >I have one basic question. It is my understanding that people
alternatively
> >use logistic and/or probit regression analyses for a categorical
dependent
> >variable, although the two methods are based different distributional
> >assumptions. The rationale, as far as I can tell, is that they rarely
make a
> >substantive difference in practice.
> >
> >But, what if they result in different regression outcomes? Is there any
> >formal test for determining which is more appropriate than the other? If
> >there is, how do you do it in Stata? Or, ff there is no such a formal
test,
> >what is the convention for choosing (if you choose) what to report?
>
> If by "different regression outcomes" you mean different substantive
> conclusions - that is very unusual, and it might make you wonder about
your
> data and models as much as it does about which method is better.
>
> Long (1997 - Regression models for categorical and limited dependent
> variables) has a brief discussion of this on p. 83.  A lot of it may just
> be whatever the convention is in your field.  Some people like being able
> to interpret odds ratios in logistic regression.  Advanced generalizations
> may be a factor, e.g. multiple-equation biprobit models or multinomial
> logistic regression.  Stata 9 has added some advanced probit-related
> routines, which might affect the decision.
>
> Rather than worry about fine-line distinctions between logit and probit
> results, depending on your circumstances you might want to think about
> other alternatives, such as scobit (skewed logit) or cloglog
(complimentary
> log-log) models.  There is a super-brief overview of such things at
>
> http://www.nd.edu/~rwilliam/xsoc694/x09.pdf
>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> FAX:    (574)288-4373
> HOME:   (574)289-5227
> EMAIL:  Richard.A.Williams.5@ND.Edu
> WWW (personal):    http://www.nd.edu/~rwilliam
> WWW (department):    http://www.nd.edu/~soc
>
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