- by the way, I have not yet seen this post (on sci.stat.edu)
that dfb has cited.
On 28 Jun 2003 07:40:18 -0700, [EMAIL PROTECTED] (Donald Burrill) wrote:
> On Thu, 26 Jun 2003, jackson_high wrote (edited):
>
> > I have a problem. I set up two correlation-coefficients both
> > correlated to a common feature. How can I now test those two
> > coefficients for significance in the sense of differing
> > significantly?? And how can I do this in SPSS?
> > Thanks for any quick help!!
>
> If I understand you correctly, you have a correlation r_xy between
> two variables X and Y, and a correlation r_xz between variables X and Z,
> for the same sample of instances (persons, cases, whatever); and you
> wish to test whether r_xy differs from r_xz.
> Equivalently, you wish to test whether (r_xy - r_xz) differs from 0.
> Glass & Stanley (1970) report the following procedure, where n is the
> sample size and r_yz is the correlation between Y and Z:
>
There are references in my stats-FAQ, and here is an
important comment concerning the classical test by Hotelling,
in comparison to other tests. (I expect that the test above should
be in the Meng article, but I don't remember that detail.)
* * * from my stats-FAQ. Citation, and other comments.
Meng, Xiao-Li, Rosenthal, Robert; and Rubin, Donald B. (1992).
"Comparing correlated correlation coefficients."
_Psychological Bulletin_ , 111, 172-175.
Hotelling's solution is included in Ferguson's textbook.
[June 17, 2002: vol., above, corrected to '111'.
Comment: Hotelling's is an exact test of a particular hypothesis,
one that tests positive correlations against a *residual*
of error variation in the criterion. The articles
I have read have not made clear that one test is proper when
the other is not.
=== Communication to me, 2002, from Paul von Hippel
" ... if you read the appendix
and related articles you realize that they're confining
themselves to the case where the regressors are random variables.
If the regressors are fixed, as in an experimental design,
then Hotelling's test is appropriate. Hotelling (1940) was
quite explicit about this, so what Meng, Rosenthal, & Rubin
are really criticizing is the mistaken practice of using
Hotelling's test with random regressors.
"Williams (1959) adapted Hotelling's test to the case of
random regressors. In simulation studies Williams' test
has held up quite well against the alternatives described
by Meng, Rosenthal, & Rubin. This is all in the papers cited
in MR&R's bibliography."
=== end of communication ].
* * * end of extract from my stats-FAQ.
p.s. I've been asked to compare correlations when the
two variables were supposed to be 'diagnostic predictors'
and they differed from each other by the addition of an item
or two.... so the two predictors were correlated with each
other at about 0.95 or more. That is a place for looking
directing at the value of the "change" rather than performing
a test on correlations.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
"Taxes are the price we pay for civilization." Justice Holmes.
.
.
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