From: Mike Lawrence <Mike.Lawrence_at_dal.ca>

Date: Tue, 26 Jun 2007 15:38:45 -0300

}

R-help_at_stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 26 Jun 2007 - 18:53:33 GMT

Date: Tue, 26 Jun 2007 15:38:45 -0300

On 26-Jun-07, at 2:36 PM, Mike Lawrence wrote:

> On 26-Jun-07, at 8:12 AM, Mike Lawrence wrote:

*>> Hi all,
**>> Hopefully this will be quick, I'm looking for pointers to packages/
**>> functions that would allow me to calculate the power of a t.test when
**>> the DV has measurement error. That is, I understand that, ceteris
**>> paribus, experiments using measure with more error (lower
**>> reliability) will have lower power.
**>
**> I came across a reference (http://memetic.ca/reliability.pdf) that
**> provides a formula for calculating the noncentrality parameter for
**> tests using imperfect measures (see Eq. 4), as well as a table of
**> some resulting power estimates. However, while I have created a (very
**> slow) monte carlo function that so far as I can tell matches their
**> results, when I attempt to implement their analytic solution it's way
**> off. Can anyone see what I'm doing incorrectly?
**>
**> n=100
**> r=.5 #reliability
**> e=.5 #effect size
**> delta=(sqrt(r*n)/2)*e
**> power.t.test(n,delta,sig.level=.05,alternative='one.sided')
**>
**> Two-sample t test power calculation
**>
**> n = 100
**> delta = 1.767767
**> sd = 1
**> sig.level = 0.05
**> power = 1
**> alternative = one.sided
**>
**> NOTE: n is number in *each* group
**>
**>
**> Meanwhile, their tables and my monte carlo method say that the power
**> in that circumstance should be .7
*

Found it; I was using power.t.test without being thorough in reading its details. Sorry for the spam, and for anyone that's interested, here's the final analytic solution:

#get power for a t.test, incorporating measurement error. #n = total number of participants across your 2 groups #r = estimated reliability of the measure used #e = measured effect size get.power=function(n,r,e,tails=2,alpha=.05){ d=(sqrt(r*n)/2)*e a=1-ifelse(tails==2,alpha/2,alpha) p=1-pt(qt(a,n-2),n-2,d) return(p)

}

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 26 Jun 2007 - 18:53:33 GMT

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