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 Viechtbauerhttp://www.wvbauer.com/
Department of Methodology and StatisticsTel: +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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Gerrit Hirschfeld Sent: Saturday, June 12, 2010 12:45
To: r-help@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@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.
__
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@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.
