> > Robert Lundqvist wrote:
> > > I found in one of the textbooks we use that calculating correlation
> > > coefficients is not meaningful when you have categorical data. However,
> > > using dummy variables should be possible, shouldn't it? Either when you
> > > have one ordinary numerica variable and one dummy, or even when you have
> > > two dummy variables...
Robert J. MacG. Dawson wrote:
> > Oh, it's _possible_, all right. It's just not *meaningful*, because
> > there are many ways to assign dummy variables to the levels of the
> > categorical variable
> > and typically each will give a different result. Is "banana" between
> > "apple" and "orange" or not?
> >
> > There is a sort of exception when there are two levels, in which case
> > all ways of labelling are equivalent up to linear transformation; but
> > there are better ways to deal with this special case.
And Jim Clarke responded:
> I have to disagree here. Any ANOVA and contrasts can be analyzed
> by regression/correlation methods. So the regression analysis is
> just as meaningful as any anova would be. For a simple
> illustration, consider a study involving 2 control groups and 2
> treatment groups. Three contrasts could be generated, ideally
> based on a priori expectations (e.g., -1 -1 +1 +1, -1 +1 0 0, 0 0
> -1 +1).
If you're using one dummy variable for each level [but one] of the
categorical variable, that's fine. What we were asked about was using
one dummy
variable, a much more limiting technique.
-Robert
.
.
=================================================================
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at:
. http://jse.stat.ncsu.edu/ .
=================================================================
