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

From   Karen Robson <>
Subject   Re: st: k-sample tests for differences in proportions
Date   Wed, 5 Nov 2003 12:54:16 +0000 (GMT Standard Time)

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!

On Wed, 05 Nov 2003 07:17:59 -0500 Richard Williams 
<> wrote:

> At 09:18 AM 11/5/2003 +0000, wrote:
> >Is there an established equivalent command to "prtest" for categorical
> >variables with more than two categories?
> >
> >If not, just 'how wrong' is it to use an anova estimation for this purpose?
> >
> >Thanks for any guidance.
> I just tried the csgof command suggested by Ronan Conroy for a single 
> variable, and it works as I would expect it to.  In SPSS, you would use the 
> NPAR test command for this purpose.
> But, are you talking about comparing proportions between 2 variables, e.g. 
> Var1 and Var2, each has 5 categories, and you want to test whether the 
> proportion in each category is the same for each variable?  If so, I don't 
> understand why you would consider Anova, since you'd be computing means of 
> a categorical variable.  If csgof doesn't give you what you want, perhaps 
> you could give a specific example of what it is you want to test.
> Incidentally, I have "cheated" and used Anova to test p1=p2, where p1 is 
> the proportion of successes in the first group and p2 is the proportion of 
> successes in the 2nd group.  That is, both my IV and DV were 
> dichotomies.  At least in the large samples I tried it on, I got almost 
> exactly the same results you get by doing it the "right" way.  But, once 
> you get past 2 categories on your categorical dependent variable, Anova 
> doesn't make any sense to me.
> -------------------------------------------
> Richard Williams, Associate Professor
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Karen Robson (
Institute for Social and Economic Research (ISER)
University of Essex, Colchester, UK CO4 3SQ
tel: +44 (0)1206 873897; fax: +44 (0)1206 873151

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