Daniel Indro wrote:
> I ran a logistic regression with a cluster option. In one model, the
> results showed a Wald Chi-Square in the order of 100,000. When I ran a
> different model (by adding additional independent variables), I got a
> much smaller Wald Chi-Square (in the order of 30,000 or 2,000 depending
> on the additional independent variable being added). I have seen a
> paper reporting a Wald Chi-Square as high as 30,000 in a logistic
> regression with a robust option, but haven't been able to locate any
> information about why I got such a high Wald Chi-Square. Could someone
> explain if my results are normal or if I have done something wrong?
[...]
Well, there's good news and there's bad news (but mostly good news) to
give you.
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.
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
In essence, it's wrong because the total sum of squares (TSS) in each
-logit- model is not exactly the same. If the TSS statistic is not exactly
the same for both models, they're not comparable.
Hope that helps.
CLIVE NICHOLAS |t: 0(044)7903 397793
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
Whereever you go and whatever you do, just remember this. No matter how
many like you, admire you, love you or adore you, the number of people
turning up to your funeral will be largely determined by local weather
conditions.
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