Re: [R] meta analysis with repeated measure-designs?

From: Viechtbauer Wolfgang (STAT) <>
Date: Sat, 12 Jun 2010 15:59:09 +0200

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.


Wolfgang Viechtbauer              
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: [] On
Behalf Of Gerrit Hirschfeld Sent: Saturday, June 12, 2010 12:45
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
> Fon.: +49 (0) 251 83-31378
> Fon.: +49 (0) 234 7960728
> Fax.: +49 (0) 251 83-34104
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Received on Sat 12 Jun 2010 - 14:01:12 GMT

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