# [R-meta] Performing a multilevel meta-analysis

Tzlil Shushan tz|||21092 @end|ng |rom gm@||@com
Wed Aug 5 05:44:59 CEST 2020

```Hi R legends!

My name is Tzlil and I'm a PhD candidate in Sport Science - Human
performance science and sports analytics

I'm currently working on a  multilevel meta-analysis using the metafor
package.

My first question is around the methods used to assign weights within rma.mv
models.

I'd like to know if there is a conventional or 'most conservative' approach
to continue with. Since I haven't found a consistent methodology within the
multilevel meta-analyses papers I read, I originally applied a weight which
pertains to variance (vi) and number of effect sizes from the same study. I
found this method in a lecture by Joshua R. Polanin

W = 1/vi, then divided by the number of ES for a study
for example, a study with vi = 0.0402 and 2 different ES will weight as
follow;
1/0.0402 = 24.88, then 24.88/2 = 12.44 (finally, converting into
percentages based on the overall weights in the analysis)

After I've read some of the great posts provided in last threads here such
as;
http://www.metafor-project.org/doku.php/tips:weights_in_rma.mv_models and
https://www.jepusto.com/weighting-in-multivariate-meta-analysis/
I wonder if it is not correct and I need to modify the way I use weights in
my model..

For some reason, I tried to imitate the approach used in the first link
above. However, for some reason I get an error every time I tried to
specify weights(res, type="rowsum") *Error in match.arg(type, c("diagonal",
"matrix")) : 'arg' should be one of “diagonal”, “matrix”*

My second question is related to the way I meta-analyse a specific ES. My
meta-analysis involves the reliability and convergent validity of heart
rate during a specific task, which is measured in relative values (i.e.
percentages). Therefore, my meta-analysis includes four different ESs
parameters (mean difference; MD, interclass correlation; ICC, standard
error of measurement; SEM, and correlation coefficient; r).

I wonder how I need to use SEM before starting the analysis. I've seen some
papers which squared and log transformed the SEM before performing a
meta-analysis, while others converted the SEM into CV%. Due to the original
scale of our ES (which is already in percentages) I'd like to perform the
analysis without converting it into CV% values. Should I use the SEM as the
reported values? only log transformed it? Further, is there a
straightforward way  in metafor to specify the analysis with Chi-square
values (as "ZCOR" in correlations)?

Kind regards,

Tzlil Shushan | Sport Scientist, Physical Preparation Coach

BEd Physical Education and Exercise Science
MSc Exercise Science - High Performance Sports: Strength &
Conditioning, CSCS
PhD Candidate Human Performance Science & Sports Analytics

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