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Re: st: k-sample tests for differences in proportions
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
> At 09:18 AM 11/5/2003 +0000, firstname.lastname@example.org 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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