meta analysis with repeated measure-designs?
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-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Gerrit Hirschfeld Sent: Saturday, June 12, 2010 12:45 To: r-help at 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 Westf?lische Wilhelms-Universit?t Fliednerstr. 21 48149 M?nster 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 at 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.