Dear all,

I've been delighted to just notice that Cohen's formulas for Effect Size 'w' and the associated power have been implemented in the 'pwr' package (thanks to Stéphane Champely and others)..

There is one aspect, though, that perplexes me. I'm doing some last minute post hoc analyses, meaning that my sample size (N=3404) has been long fixed, and I'm interested in assessing the ES and Power after the fact..

As far as I can deduce from the implementation of the ES.w2 formula or Cohen's (1992) own article, it seems to me that the probabilities p(H0) and p(H1) would simply be the expected and observed absolute frequencies divided by the sample size N, in that the 'true' probablities are the observed proportions and the null probabilities the expected ones. If this is correct, then the effect size and the power statistics can naturally easily be calculated with the 'pwr' package. However, this entails that the noncentrality parameter lambda=N*w^2 is equal to the chi-squared statistic X^2.

observed
    p   h   m    a
X 119  64  36   37
Y 594 323 776 1455

expected
          p         h         m         a
X  53.62162  29.10458  61.06698  112.2068
Y 659.37838 357.89542 750.93302 1379.7932

observed.p
           p          h          m          a
X 0.03495887 0.01880141 0.01057579 0.01086957
Y 0.17450059 0.09488837 0.22796710 0.42743831

expected.p
           p           h          m          a
X 0.01575253 0.008550112 0.01793977 0.03296322
Y 0.19370693 0.105139664 0.22060312 0.40534465

ES.w2(observed.p)
[1] 0.2406104

ES.w1(expected.p,observed.p)
[1] 0.2406104

pwr.chisq.test(w=ES.w1(expected.p,observed.p),N=3404,sig.level=.05,
df=3)
     Chi squared power calculation

              w = 0.2406104
              N = 3404
             df = 3
      sig.level = 0.05
          power = 1

 NOTE: N is the number of observations

lambda <- 3404*ES.w1(observed.p,expected.p)^2

lambda
[1] 240.9289

pchisq(qchisq(p=.05,df=3,lower.tail=F),ncp=lambda,df=3,lower=F)
[1] 1

Have I missed or misunderstood something here altogether? Should the alternative H0 probabilities be estimated by e.g. some sort of fitting? Any pointers, suggestions or assistance would be greatly appreciated.

        -Antti Arppe
--
======================================================================
Antti Arppe - Master of Science (Engineering)
Researcher & doctoral student (Linguistics)
E-mail: [EMAIL PROTECTED]
WWW: http://www.ling.helsinki.fi/~aarppe
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