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RE: st: k-sample tests for differences in proportions


From   Richard Williams <Richard.A.Williams.5@nd.edu>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: k-sample tests for differences in proportions
Date   Wed, 05 Nov 2003 10:26:42 -0500

At 02:49 PM 11/5/2003 +0000, Nick Cox wrote:
The mean of a binary variable is totally interpretable;
in this case the interpretation is not that an individual
has half a degree, but that on average half the individuals
have degrees.

Logit, probit and much else would not make sense otherwise.

Nick
n.j.cox@durham.ac.uk
Exactly. Anova is technically incorrect here, but in the few large sample cases where I've tried it both the "right" and the "wrong" way, Anova got the significance tests almost exactly right. Of course, that may not always be true, and there is no reason not to do it the "right" way, other than maybe convenience. Especially back when computing power was more of an issue, doing things a quick but technically wrong way (e.g. run an OLS regression with a binary dependent variable) was sometimes a way of narrowing down what you would estimate with a more time-consuming correct procedure.

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