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

 From "\"Jingky P. Lozano\"" To statalist@hsphsun2.harvard.edu Subject Re: st: k-sample tests for differences in proportions Date Wed, 05 Nov 2003 22:37:35 +0800 (PHT)

```I was thinking that you can probably use Kruskall-Wallis which is the nonpar
equivalent of ANOVA but since you only have two possible answer (with Univ and
without univ degree) per religion, I agree that you can just simply use chi-
square.  There will really be not much use in comparing the means.  A 0.5 mean
in education variable cannot be interpreted as incomplete Univ degree since
your coding system is categorical.  I think it is more appropriate to use chi-
square.

Quoting Richard Williams <Richard.A.Williams.5@nd.edu>:

> At 09:18 AM 11/5/2003 +0000, klrobson@essex.ac.uk 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.
>
>
> -------------------------------------------
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```