[R] meta analysis with repeated measure-designs?

[Gerrit Hirschfeld]

Hi, 

thanks for the references I will try the sensitivitiy-analysis in R and try out winbugs if that does not work (little afraid of switching programmes). 

I also had an idea for a reasonable estimate of the correlations. Some studies report both results from paired t-tests and means and SDs, and thus allow to calculate two estimates for d one based on M and SD alone the other on t. The difference between the two estimates should be systematically related to the correlations of measures.

I will keep you posted, if I have a solution or hit a wall.

efachristo and dank je wel!

Gerrit


On 12.06.2010, at 15:59, Viechtbauer Wolfgang (STAT) wrote:

> 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)
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> 
> 
> ----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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______________________________
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