Hi

Mike you seem to be claiming that if we let all the students with SATs < 1200 
into university this would NOT produce any increase in the variability in GPAs? 
 Given good reason to think that these students would score on average at a 
lower level than students with SATs > 1200, I fail to see how the standard 
deviation for GPAs would stay the same.  Or do you believe that scores < 1200 
on the SAT would be associated with GPAs lat the same average level as people 
with SAT scores > 1200?  Or perhaps that faculty would modify their standards 
so that everyone fell into the same distribution as at present ... hardly a 
compelling argument for not using SATs.

Take care
Jim

James M. Clark
Professor of Psychology
204-786-9757
204-774-4134 Fax
[EMAIL PROTECTED]

>>> "Mike Palij" <[EMAIL PROTECTED]> 30-May-08 6:14:11 PM >>>
On Fri, 30 May 2008 12:56:41 -0700, Jim Clark wrote:
>I guess I do not understand the concerns about this correction (or 
>the mention of a similar concern about ANCOVA) raised by Mike. 

Consider:
One form of the formula for the Pearson r is following:

r = cova(X,Y)/[SD(X) * SD(Y)]

where
cova(X,Y) is the covaraince of X and Y
SD(X) is the standard deviation of X, and
SD(Y) is the standard deviation of Y

In the classic restriction of range situation both SD(X)
and SD(Y) are underestimates of the the population 
standard deviation.  Using knowledge of the sample SD
and its relationship to the population SD might correct
for the restriction of range of values in the sample.
However, if only SD(X) is restricted (e.g, SAT > 1200)
and SD(Y) is an unrestricted estimate of the population
standard deviation, what happens?  Assume SD(X)=50
in the restricted sample but in the population SD(X)=100.
What would happen to r above if we inserted the population
value for X into the equation?  Counterintuitive, no?

What is really going on if there is restriction of range only for
one variable?

-Mike Palij
New York University
[EMAIL PROTECTED] 






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