[R-meta] Percent Correct as outcome variable
markus.janczyk at uni-tuebingen.de
Sun Feb 4 13:47:56 CET 2018
Thanks Wolfgang, it is exactly the problem that moste often no
within-group variances were reported, nor were the relevant t-tests to
back-calculate the variance... Is there still a method for a reasonable
imputation of the variance? Something like a best guess? We know the
sample size of course, sometimes the number of items per participants
that was administered and could be right or wrong rememberd.
Am 04.02.2018 um 13:33 schrieb Viechtbauer Wolfgang (SP):
> Dear Markus,
> Sure, you can compute D = M_X - M_Y. Since M_X and M_Y are means, this is a mean difference. This can be easily handled by metafor, meta, etc. In metafor, you can compute mean differences with escalc(measure="MD", ...) and then pass those to rma(). In meta, there is metacont(sm="MD", ...).
> The only problem is that in order to compute the sampling variance of a mean difference, you need the within-group variances, since:
> var(D) = Var(X) / n_X + Var(Y) / n_Y.
> So, unless you impute the missing within-group variances, you cannot include such studies when using standard meta-analytic procedures.
> If you do know the t-test statistic or p-value (from which one could back-calculate the t-statistic), then:
> Var(pooled) = D^2 / (t^2 * (1/n_X + 1/n_Y))
> so then you can back-calculate the pooled within-group variance (assuming the t-test computed was also computed based on the pooled variance) and then we could assume Var(pooled) = Var_X = Var_Y for computing var(D).
>> -----Original Message-----
>> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>> project.org] On Behalf Of Markus Janczyk
>> Sent: Monday, 29 January, 2018 11:05
>> To: r-sig-meta-analysis at r-project.org
>> Subject: [R-meta] Percent Correct as outcome variable
>> Dear everybody,
>> We are interested in a meta-analytic question related to memory research.
>> Consider the case where two conditions X and Y are compared.
>> Unfortunately, in many of the (old) papers only the means M_X and M_Y
>> are reported for the percent correct remembered items (and often not
>> even the interesting t-test). To calcutale a "real" effect size as the
>> outcome measure to use in a meta-analysis, we need to some variability
>> measure though (if I understood the metafor functions right). With the
>> data we have, it feels like a raw (and non-standardized) effect we can
>> caluculate as the outcome variable (i.e., D = M_X - M_Y).
>> Is there any other possible solution or improvement somebody knows of or
>> recommends or just somebody who can let me know a reference where I can
>> Thanks, Markus
>> Jun.-Prof. Dr. phil. habil. Markus Janczyk, Dipl.-Psych.
>> University of Tübingen
>> Department of Psychology
>> Cognition and Action
>> Schleichstraße 4
>> 72076 Tübingen
>> email: markus.janczyk at uni-tuebingen.de
>> phone: +49 (0)7071 2976761
Jun.-Prof. Dr. phil. habil. Markus Janczyk, Dipl.-Psych.
University of Tübingen
Department of Psychology
Cognition and Action
email: markus.janczyk at uni-tuebingen.de
phone: +49 (0)7071 2976761
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