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Re: st: Wald Chi-Square in Logistic with Cluster Option
At 05:25 PM 3/11/2006, you wrote:
The good news is that, assuming your logistic model specifications are
correct, then your Wald value is OK. It may be that some of your variables
are highly collinear with each other, and it's that that's pushing it up a
few notches: you can check this with Richard Williams' highly useful
-collin- post-estimation command, downloadable from SSC.
Thanks to Clive for the kind words. Alas, much as I'd like to claim
credit for -collin- (along with xtabond2 and several other programs!)
the actual author is Phil Ender and you need to get it from UCLA, not
SSC. Just use -findit collin- to get a copy.
The bad news is that comparing two logistic regression models, even if
they both have some independent variables in common, is _wrong_. For the
full reasoning, you can check out a neat .pdf file from that man again
Williams at
http://www.nd.edu/%7Erwilliam/xsoc694/x04.pdf
Not quite. The problem comes in comparing coefficients across
models, e.g. you have x1, x2 and x3 in a model, you then add x4, x5
and x6, and you observe that the coefficients for x1, x2 and x3 are
quite a bit different in the two models. This is a fairly common
thing to do with OLS regression models, but, for reasons explained in
the handout, can be highly deceptive when doing things like logistic
regression. But, that doesn't mean that you can't run a series of
models, and see whether adding or deleting variables significantly
affects the fit of the model.
In the case of the original problem, I am not sure what is going
on. The behavior seems bizarre to me; you add a variable, and the
chi-square plummets by 80,000??? You add a different variable, and
it plummets by over 111,000? I suspect it has something to do with
the use of clustering, but I really don't know. Besides -collin-, I
might do some simple descriptive stats, e.g. crosstab Y with some of
the Xs. 23 Xs is a lot; perhaps the data are being spread too
thin. Maybe add variables in small groups and see if there is some
point at which the chi-square goes wild, and then see if there is
something odd about the variable that causes it. I'd also probably
cheat and try running it without the cluster option, and see if that
produces more sensible results; I believe that would suggest that
clustering was somehow part of the problem.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
FAX: (574)288-4373
HOME: (574)289-5227
EMAIL: [email protected]
WWW (personal): http://www.nd.edu/~rwilliam
WWW (department): http://www.nd.edu/~soc
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