In psychometrics, there are concepts of polychoric and polyserial
correlations. The first one is between two ordinal variables, and the
second one is between an ordinal variable and a continuous variable.
If your variables are truly nominal (like gender or geography), then
the correlations are likely meaningless, although you can meaningfully
ask whether the distributions of the continuous variables differ
between the values of the discrete variable (answered by ANOVA,
Kruskal-Wallis test and such). I wrote -polychoric- package some while
ago that computes these correlations.
On Mon, Sep 21, 2009 at 9:45 AM, Christian Weiß <[email protected]> wrote:
> Dear Statalist,
>
> although it's not a particularly Stata specific question , I am hoping
> to get advise on the following (basic?) question:
>
>
> I am using the following command to get a correlation matrix
>
> quietly estpost correlate `vars', matrix
> esttab using correlations.csv, not unstack compress noobs star(* 0.10
> ** 0.05 *** 0.01) long b(%9.2f) replace
>
> `vars' containts a battery of mostly metric variables. Besides the
> metric variables, there is also three dummy variables.
>
> I am wondering now if the reported (relatively high) correlation
> coefficients among the dummy variables and between some of the metric
> variables and the dummy variables are actually meaningful. How to
> interpret them / which correlation test to use?
>
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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