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

From: Viechtbauer Wolfgang (STAT) <Wolfgang.Viechtbauer_at_STAT.unimaas.nl>
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.

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
>
> ______________________________________________
> 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.
______________________________________________ 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.
Received on Sat 12 Jun 2010 - 14:01:12 GMT

Archive maintained by Robert King, hosted by the discipline of statistics at the University of Newcastle, Australia.
Archive generated by hypermail 2.2.0, at Mon 14 Jun 2010 - 09:10:29 GMT.

Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.

list of date sections of archive