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Re: st: logistic versus probit

From   Richard Williams <[email protected]>
To   [email protected]
Subject   Re: st: logistic versus probit
Date   Tue, 14 Jun 2005 12:08:06 -0500

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

Richard Williams, Notre Dame Dept of Sociology
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