Karen Robson wrote:
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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
pedantic?
I would really appreciate your opinion now that I have
fully explained myself!
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How about -glm-? See the do-file below for how. The example is artificial,
but it shows how to use -glm- followed by -test- to do exactly what you want.
If you want to adjust for multiple comparisons, Stata can do that, too. -help
test- Also, just as an aside, consider performing logistic regression
(examining odds ratios, using link(logit)) in lieu of testing differences in
proportions.
Joseph Coveney
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clear
set obs 300
set seed 20031105
generate byte degreed = uniform() > 0.25
* degreed = 1 for has college degree and 0 for doesn't
generate byte religion = mod(_n, 5) + 1
* religion = 1 for first religion, 2 for second, and so on
*
* Here we go:
*
xi: glm degree i.religion, family(binomial) link(identity) nolog
test _Ireligion_2 = _Ireligion_3
test _Ireligion_2 = _Ireligion_4
* . . . and so on
exit
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