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Hey, Chris --
I could be wrong about this, but most of what I've seen
comparing power of the two sorts of tests has come from Monte Carlos and
bootstrapping methods for generating a distribution based on the data. I
don't know that there's a way to _a priori_ compute the power of a
non-parametric test.
But again, I could be wrong, and welcome correction if I
am.
And it's good that you note that people still
(unjustifiably) underestimate the power of non-parametric tests. They're
actually pretty nifty and will get the right answer almost all the time.
They're often not perceived as being as mathematically "elegant" as
parametric tests, and I think we have a bias against them.
Cheers,
m
-------
"Mauchly's Test of Sphericity:
Tests the null
hypothesis that the error covariance matrix of the
orthonormalized
transformed dependent variables is proportional
to an identity
matrix."
---
SPSS
I cannot tell you how to calculate the power of nonparametric
tests, but I am certain that one can, because there is an extensive scholarly
literature comparing the relative powers of nonparametric tests to their
parametric "cousins" (in which it was initially argued that nonparametrics
were much less powerful -- which is why we haven't traditionally used them as
much as parametrics -- and later finding that even fairly minor deviations
from assumptions lower the power of parametric tests enough to make
nonparametrics highly competitive -- which is why there is now renewed
interest in them).
Regards,
--
Christopher D.
Green
Department of Psychology
York University
Toronto, ON M3J
1P3
Canada
416-736-5115 ex. 66164
[EMAIL PROTECTED]
http://www.yorku.ca/christo
=============================
Marc
Carter wrote:
Seconded.
I'd be interested in hearing about this, too. It
seems to me that the computation of power has to make assumptions about the
shape of the distribution of the dependent variable (power
is essentially a measure of area of the distribution of the variable
-- under the alternative hypothesis -- above the criterion), and so if
we cannot make assumptions about the character of that distribution (that's
why they're called "distribution-free stats"), I'm at a loss to figure how
we'd compute its area.
I'm wondering if there's some way to bootstrap a
distribution based on the data, generate a function to describe it, and then
get about integrating it.
But, as often happens, I could be wrong and would
really like to know.
m
-------
"Mauchly's Test of Sphericity:
Tests the null
hypothesis that the error covariance matrix of the
orthonormalized
transformed dependent variables is proportional
to an identity
matrix."
---
SPSS
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- [tips] RE: Nonparametric Effect Size and Post-Hoc Power Marc Carter
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