On Fri, 23 Jun 2000 16:32:47 -0500, "Jason Osborne, Ph.D."
<[EMAIL PROTECTED]> wrote:
> I am working on a power analysis project- we are reviewing old journal
> articles to calculate observed effect sizes and power. Some of these
> articles, for example reporting t-test results, only give means and
> t-test, no standard deviation. thus, no effect size calculation is
> possible. I was hoping to estimate an effect size by converting a t to
> an r. I seem to remember a formula that relates the two, but am having
> a dickens of a time tracking one down. The one I did track down, for
> calculating t from r, is not that helpful:
>
Oh! This is scary! I hope (against experience) that this is a class
project, and not an intent to publish.
It is my own impression that other statisticians share my general
impression,
- that a majority of meta-analyses that get published are awful; and
- the shortcomings are at least as severe in the statistics as in the
content.
You need to have a statistician who is facile with ANOVA to do one of
those, and your project (apparently) does not. But yours is not a
meta-analysis? or is it?
I am not sure what you are intending with "power" but I suspect that
it is at least as difficult. I would have trusted Jacob Cohen with
any power analysis, since I like his popular book; but I have some
recent doubts about the published surveys that *he* made of power.
I don't doubt his math, but I suspect that the popular questions of
math have blurred some deeper questions. And studies are being
averaged that don't deserve to be averaged.
> t= r * sqrt(n-2)
> -------------
> sqrt(1-r^2)
>
> I want to be able to calculate r from t. I tried algebraically
> manipulating the formula, but never quite got it to where I could do
> this. Any advice?
Yeah. r is a an "effect" size measure, but you should notice that it
is one of poor repute. When you can, you should stick with something
else -- For at comparison of regressions, you want to compare betas,
not r. If you can have a difference in means, that is better than r.
Hope this helps.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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