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Re: st: Logistic Regression_Unequal Ns (outcomes)

From   Richard Williams <>
To   "" <>, "" <>
Subject   Re: st: Logistic Regression_Unequal Ns (outcomes)
Date   Sun, 08 Mar 2009 10:43:08 -0500

At 08:34 AM 3/8/2009, Chao Yawo wrote:
Hello, I'm preparing to run a logit model predicting the odds of NOT
testing for an STD.  As you can see from the table below, 4688 (about
98%) of respondents have my outcome of interest (i.e., have not tested
for an STD).  I realized that because of this unequal groupings, all
crosstabulations have higher proportions within the untested category.
 I have a feeling that these could bias my estimates in a way. For
example, given the unequal groupings, I think I am only restricted to
modeling failure to test (the zero outcome), as modeling for ever
tested (1) could lead to unstable estimates.  So my question is  what
possible impact will this have on my model, and what can I do about
it?  Thanks - Chao

Like Martin says, it doesn't matter which is one and which is zero. Also, my experience is that the classification table, which I never use all that much anyway, is especially worthless when you have such an extreme split.

You may wish to check into Gary King's -relogit-.  See

Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu

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