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

From   Richard Williams <>
Subject   Re: st: k-sample tests for differences in proportions
Date   Wed, 05 Nov 2003 08:30:20 -0500

I have a suspicion that, while technically wrong, ANOVA would work ok, at least if the Ns are fairly large.

But, if I understand you correctly, the correct way to do it would seem to be to simply crosstab the two variables, and test the model of independence. If there is no relationship between religion and education, i.e. members of each religion are equally likely to have university degrees, the chi-square will be insignificant. If the chi-square is significant, that would mean that members of some religions were more likely to receive university degrees than were members of others.

An alternative approach might be to run a logistic regression, where degree is regressed on 4 dummy vars for religion. If all 4 dummies were insignificant,that would mean that religion had no effect on the probability of having a university degree. I think there would be ways to isolate where any significant differences were but just off the top of my head I am not sure how to approach it in Stata.

At 12:54 PM 11/5/2003 +0000, you wrote:

Thanks for your replies.

What I am trying to calculate is if the mean of a dummy
variable is different across the categories of a separate
categorical variable. So if the mean of a dummy variable
(e.g. let's say 1=has university degree, 0=does not have
university degree) is significantly different across a
nominal variable like religious affiliation which has five
possible values. If I had just two categories in the
religious affiliation variable, I could just prtest
university, by(religion). Since I have multiple categories,
however, this becomes impossible.

If my DV was continuous, I could do an anova and with
post-hoc estimations figure out where the significant
differences between categories were. However, because my DV
is not continuous, I have been told an anova here is not
appropriate, hence my confusion. Perhaps I am just being

I would really appreciate your opinion now that I have
fully explained myself!
Richard Williams, Associate Professor
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