Reminds me a bit of "Heywood cases" in structural equation modeling, where (if 
I recall correctly) the estimated variances are negative (but I'm confident 
that one or more sharp-eyed TIPSters will correct me if I'm wrong...). ...Scott



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-----Original Message-----
From: Mike Palij [mailto:[email protected]]
Sent: Wednesday, April 21, 2010 1:20 PM
To: Teaching in the Psychological Sciences (TIPS)
Cc: Mike Palij
Subject: RE: Re:[tips] Biserial r.

On Wed, 21 Apr 2010 05:48:50 -0700, Rick Froman wrote:
>OK, I know that some correlational techniques occasionally produce r greater
>than 1 or less than -1 but I think I am on firm footing when I say that I am
>not going to see a negative r-squared in the set of real numbers used in
>statistical calculations (although it may occur with complex numbers
> http://mathforum.org/library/drmath/view/52613.html  ).

Unfortunately, this is not true.  A simple Google search for either
"negative R squared" or "negative R square" will provide a variety of
hits.  A number of conditions can give rise to a negative R square
but they all tend to be pathological.  If you used the regression tool
in pre-2003 Excel and you forced the regression through the origin
(i.e., the intercept is zero), you could get a negative R-square
as well as negative sum of squares, etc., (it is somewhat unusual to see
a negative F-value in the output).  Microsoft fixed the code that created
these results in Excel 2003 and later versions; see the following website
(scroll down to "Regression" or search for the  word "negative" on the page):
http://support.microsoft.com/default.aspx?scid=kb;en-us;829208

Negative R square can also be obtained in multilevel or HLM analyses.
Consider the following that attempts to identify the variance accounted
for in a heirarchial model:

|Socioeconomic status explains 45% of the explainable between-unit
|variance in this model using the first formula and 59% using the second
|formula. Thus, it appears that socioeconomic status contributes greatly
|to explaining variation between schools, but does not explain much
|variance in math achievement scores.
|
|It should be noted that there are some potential problems with the method
|described above. One possible problem is the possibility that the level-1
|variance is larger in the restricted model than the unrestricted model, which
|would produce negative R-squared values. Kreft and De Leeuw (1998) point
|out that the formula may not apply to situations where there are random
|intercepts. This is especially true for computing the between-unit variance
|explained, as there is not a single level-2 error term in models containing
|random slopes.

from:
http://ssc.utexas.edu/software/faqs/hlm
The Question is "R-squared in a Hierarchical Model" which is lower on the
page. The cited reference for Kreft & De Leeuw is:

Kreft, I., De Leeuw, J. (1998). Introducing Multilevel Modeling.
London: Sage Publications.

So, it is possible to get oddball values for statistics and for a variety of
reasons, ranging from improperly programmed procedures to situations
where key assumptions are violated.  In either case, one has to think through
what is going on.

-Mike Palij
New York University
[email protected]




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