I remember I read somewhere about different effect size measures
and now I found the spot: A book by Michael Oakes, U. of Sussex,
"Statistical Inference" 1990. The measures were (xbar-ybar)/s,
Proportion misclassified, r squared (biserial corr) and w squared
(which I think means the same as Rsq adj in ordinary linear
regression).
I would rather talk about these things as measures of different
aspects of a relationship between two variables. (A quantitative and a
qualitative with two categories in Oakes' example.):
Statistical effect, explanatory power and strength of relationship. (...
even if one could be derived from another ...)
Other aspects which could be added as pieces of information would
be p-value of test of no relationship, real world effects, causal
mechanisms, consistency, responsiveness ... (these last from a
Mosteller and Tukey reference).
If we teach this, I think it would be more obvious that one single
printout doesnt tell the whole story. And I think it would be a good
thing to acheeve. Anyway I would be happy to read comments about
aspects of relationships, since I have only just started to think about
it in this way.
/Rolf
> Mike Granaas wrote:
> > I think that we might agree: I would say that studies need a clear a
> > priori rational (theoretical or empirical) prior to being conducted. It
> > is only in that context that effect sizes can become meaningful. If a
>
> Even then standardized effect sizes may not be very helpful. We need
> to know much more information about the effect, the sensitivity of
> our measurements and so on.
>
> Thom
>
>
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