[R] meta analysis with repeated measure-designs?

[Viechtbauer Wolfgang (STAT)]

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
> 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
>
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