[R-meta] aggregating effect sizes

When you say the 'values', do you mean the estimates themselves or their
sampling variances?

I only talked about the latter, that is, if the sampling variances within
a set of estimates are homoscedastic, then there is no difference between
weighting or not weighting. If the sampling variances are quite different,
then I would typically prefer to use weighting, since that will give the
most efficient estimate of the underlying true effect/outcome for the set.

However, James Pustejovsky (cc-ed) asked me to add the 'weighted' option
to aggregate(), because there can be circumstances where using a simple
(unweighted) average might be preferred.

If I recall, one argument goes along the following lines. Say you want to
aggregate two effect estimates, one for male and one for female subjects.
With weighting, the two estimates are weighted approximately proportional
to the sample sizes within the two subgroups. However, the subgroup sizes
within a study are just a reflection of how many male and female subjects
the researchers were able to recruit for their study (and females tend to
be more likely to volunteer), which doesn't reflect the population to which
you want to make an inference (which consists of approx. equal parts of
male and female subjects). So in that case, a simple average might be
preferred.

Best,
Wolfgang

-----Original Message-----
From: Filippo Gambarota [mailto:filippo.gambarota at gmail.com]
Sent: Monday, 10 January, 2022 13:10
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: [R-meta] aggregating effect sizes

Thank you Wolfgang,
So if I get correctly, the weighted approach should be preferred if

values that I

have to aggregate are quite different? Because using the Borenstein vs

weighted

method gives me quite different results, especially for the mean effect.
Thank you!

On Mon, 10 Jan 2022 at 12:57, Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Dear Filippo,

If you are asking about what is described in Box 24.1, then the answer is

yes, if

you use struct='CS' (which is the default) and 'weighted=FALSE' -- the

default in

aggregate() is to compute a weighted average, but Borenstein et al. only

give the

equations for computing an unweighted average and its sampling variance

(but

since the sampling variances of the two estimates that are being

aggregated in

the book example are the same, whether one uses weighted=TRUE or FALSE

makes no

difference). You can also find the corresponding code here:

https://wviechtb.github.io/meta_analysis_books/borenstein2009.html#24)_Multiple_O

utcomes__Time-Points

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:

r-sig-meta-analysis-bounces at r-project.org] On

Behalf Of Filippo Gambarota
Sent: Monday, 10 January, 2022 12:31
To: R meta
Subject: [R-meta] aggregating effect sizes

Hi,
In order to be sure which function to use I would like to ask if the
aggregation method of multiple effect sizes with dependent sampling
error suggested by Borenstein et al. (2009) is the same as what
performed by the aggregate() function in metafor specifying a single
correlation.
In my case I have calculated pre-post effect size using Morris (2008)
and then I have to combine multiple effect sizes calculated on the
same pool of subjects.
Thank you!

--
Filippo Gambarota
PhD Student - University of Padova
Department of Developmental and Social Psychology
Website: filippogambarota
Research Group: Colab   Psicostat