Date: Mon, 28 Jan 2008 11:49:12 -0600
Reply-To: Sara House <email@example.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Sara House <firstname.lastname@example.org>
Subject: Re: CALCULATION FOR SAMPLE SIZE
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Thanks, Scott, for the reference. Looks like an interesting article - I'm looking forward to reading it.
I can definitely understand the authors' arguments about why post-hoc is a bad thing which is why I commend people who do a priori power analysis. To answer a question posed earlier, I have used what the authors of this article (and SPSS) refer to as "observed power". I wouldn't say it is the preferred method of determining power, since you're using the current experiment's values to estimate parameters to determine power - using theoretical values to determine theoretical power. It seems that your observed power would be biased to be low if you found no effect, which could lead researchers to believe they failed to find an effect due to small sample size. Perhaps their observed power was very high and there was actually no effect to be found. But then again, isn't power completely theoretical anyway? Can we ever truly know power? I would argue that there are good ways and bad ways of estimating power, but you can never be absolutely sure.
And I agree that the fact that some journals and textbooks urge researchers to find out how much power they had in an experiment is alarming. This is why in my research methods class, I do teach my students about power and explain to them the logic of determining power ahead of time.
>>> SR Millis <email@example.com> 01/28/08 10:19 AM >>>
Post hoc power analysis is not recommended. See:
Hoenig, J. M., & Heisey, D. M. (2001). The abuse of
power: The pervasive fallacy of power calculations for
data analysis The American Statistician, 55(1), 19-24.
Scott R Millis, PhD, MEd, ABPP (CN,CL,RP), CStat
Professor & Director of Research
Dept of Physical Medicine & Rehabilitation
Wayne State University School of Medicine
261 Mack Blvd
Detroit, MI 48201
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