[R-meta] Performing a multilevel meta-analysis

[Viechtbauer, Wolfgang (SP)]

Dear Tzlil,

Unless you have good reasons to do so, do not use custom weights. rma.mv() uses weights and the default ones are usually fine.

weights(res, type="rowsum") will only (currently) work in the 'devel' version of metafor, which you can install as described here:

https://wviechtb.github.io/metafor/#installation

I can't really comment on the second question, because answering this would require knowing all details of what is being computed/reported.

As for the last question ("is there a straightforward way in metafor to specify the analysis with Chi-square values"): No, chi-square values are test statistics, not an effect size / outcome measure, so they cannot be used for a meta-analysis (at least not with metafor).

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of Tzlil Shushan
>Sent: Wednesday, 05 August, 2020 5:45
>To: r-sig-meta-analysis using r-project.org
>Subject: [R-meta] Performing a multilevel meta-analysis
>
>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
>https://www.youtube.com/watch?v=rJjeRRf23L8&t=1719s from 28:00.
>
>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)?
>
>Thanks in advance!
>
>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