Dear Gerrit, the most appropriate approach for data of this type would be a proper multivariate meta-analytic model (along the lines of Kalaian & Raudenbush, 1996). Since you do not know the correlations of the reaction time measurements across conditions for the within-subject designs, a simple solution is to "guestimate" those correlations and then conduct sensitivity analyses to make sure your conclusions do not depend on those guestimates.
Best, -- Wolfgang Viechtbauer http://www.wvbauer.com/ Department of Methodology and Statistics Tel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) ----Original Message---- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Gerrit Hirschfeld Sent: Saturday, June 12, 2010 12:45 To: r-help@r-project.org Subject: [R] meta analysis with repeated measure-designs? > Dear all, > > I am trying to run a meta analysis of psycholinguistic reaction-time > experiments with the meta package. The problem is that most of the > studies have a within-subject designs and use repeated measures ANOVAs to > analyze their data. So at present it seems that there are three > non-optimal ways to run the analysis. > > 1. Using metacont() to estimate effect sizes and standard errors. But as > the different sores are dependent this would result in biased estimators > (Dunlap, 1996). Suppose I had the correlations of the measures (which I > do not) would there by an option to use them in metacont() ? > > 2. Use metagen() with an effect size that is based on the reported F for > the contrasts but has other disadvantages (Bakeman, 2005). The problem I > am having with this is that I could not find a formular to compute the > standard error of partial eta squared. Any Ideas? > > 3. Use metagen() with r computed from p-values (Rosenthal, 1994) as > effect size with the problem that sample-size affects p as much as effect > size. > > Is there a fourth way, or data showing that correlations can be neglected > as long as they are assumed to be similar in the studies? > Any ideas are much apprecciated. > > best regards > Gerrit > > ______________________________ > Gerrit Hirschfeld, Dipl.-Psych. > > Psychologisches Institut II > Westfälische Wilhelms-Universität > Fliednerstr. 21 > 48149 Münster > Germany > > psycholinguistics.uni-muenster.de > GerritHirschfeld.de > Fon.: +49 (0) 251 83-31378 > Fon.: +49 (0) 234 7960728 > Fax.: +49 (0) 251 83-34104 > > ______________________________________________ > R-help@r-project.org 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. ______________________________________________ R-help@r-project.org 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.
