perhaps the problem here is with the word ... "cause"
say i put in a column some temps in F ... then use the F to C formula ...
and get the corresponding C values ...
then i do an r between the two and find 1.0
now, is the formula the "cause" of the r of 1?
maybe we might see it as a cause but ... then again ... if i had Cs and
changed to Fs ... i get the same thing ...
now, what if we have 3 dosage levels (we manipulate) of 10, 20, and 30 ...
and we find that on the criterion ... we get a graph of
Plot
- *
1.50+
-
res1 -
- *
-
1.00+
-
- *
-
--------+---------+---------+---------+---------+--------dosage
12.0 16.0 20.0 24.0 28.0
and an r = 1
MTB > corr c1 c2
Correlations: dosage, res1
Pearson correlation of dosage and res1 = 1.000
P-Value = *
but, for another set of data we get
MTB > plot c3 c1
Plot
res2 - *
-
-
1.00+
-
- *
-
-
0.80+ *
-
--------+---------+---------+---------+---------+--------dosage
12.0 16.0 20.0 24.0 28.0
MTB > corr c1 c3
Correlations: dosage, res2
Pearson correlation of dosage and res2 = 0.982
P-Value = 0.121
and an r = .982
the fact is that we could find and equation (not linear) where the dots
about would be "hit" perfectly ... ie, find another model where there is no
error
but, the r is not 1 ... so, does that mean that some other factor is
detracting from our "cause" of Y?
the fact that the pattern is NOT linear (to which r is a function) ... is
not a detraction ...
if the model fails to find an r = 1, then that does not mean that there is
lack of perfect "cause", whatever that means ... only that the model does
not detect it
so, in that sense, lower rs do not necessarily mean that there are other
"errors" or "extraneous" factors that enter ... i would not call using the
wrong model an "extraneous" factor
At 02:12 PM 12/5/01 -0500, Wuensch, Karl L wrote:
>Dennis warns "the problem with this is ... does higher correlation mean MORE
>cause? lower r mean LESS cause?
>in what sense can think of cause being more or less? you HAVE to think that
>way IF you want to use the r value AS an INDEX MEASURE of cause ..."
>
>Dennis is not going to like this, since he has already expressed a disdain
>of r-square, omega-square, and eta-square like measures of the strength of
>effect of one variable on another, but here is my brief reply:
>
>R-square tells us to what extent we have been able to eliminate, in our data
>collection procedures, the contribution of other factors which influence the
>dependent variable.
>
>
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_________________________________________________________
dennis roberts, educational psychology, penn state university
208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED]
http://roberts.ed.psu.edu/users/droberts/drober~1.htm
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