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From | Richard Williams <Richard.A.Williams.5@ND.edu> |
To | "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>, "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |
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 http://gking.harvard.edu/stats.shtml ------------------------------------------- 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 WWW: http://www.nd.edu/~rwilliam * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/
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