[R-meta] Performing a multilevel meta-analysis

[Viechtbauer, Wolfgang (SP)]

Leaving aside that the SEM, as far as I understood your description of it, is not just a 'simple' standard deviation (i.e., it is computed in a different way) - yes, that is how you should specify the arguments for this outcome measure.

Best,
Wolfgang

>-----Original Message-----
>From: Tzlil Shushan [mailto:tzlil21092 using gmail.com]
>Sent: Wednesday, 19 August, 2020 16:21
>To: Fernando Klitzke Borszcz
>Cc: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
>Subject: Re: [R-meta] Performing a multilevel meta-analysis
>
>Dear Wolfgang and Fernando,
>
>Apologise for the multiple emails, but I just figured out that my last
>questions were probably unnecessary..
>After I read this ‘measures for quantitative variables’ section’
>https://wviechtb.github.io/metafor/reference/escalc.html
>I finally understood that I probably need to specify the SEM values as sdi
>and sample size as ni in the model.
>res -> escalc(measure = “SDLN”, sdi = sem, ni, data = dat)
>That’s right?
>
>Thanks and kind regards,
>
>On Wed, 19 Aug 2020 at 21:28, Tzlil Shushan <tzlil21092 using gmail.com> wrote:
>Dear Wolfgang and Fernando,
>
>Woflgang, thanks for letting me know..
>
>Fernando, thanks for your answer, I wanted to have some time working with
>"SDLN" function you suggested before commenting again.
>
>I'm familiar with those papers that investigated SEM, thanks for sending
>them over. Since you already mentioned the "SDLN" function I have two
>questions;
>
>1) If I want to proceed with log transformation of SEM effect sizes, Do I
>need to specify log() for the yi value? res <- escalc(measure = "SDLN", yi =
>log(sem), vi , data = dat)?
>
>2) Because it is hard to obtain the sampling variance for each individual
>study (some reported CI and some not), What function should I use to compute
>the sampling variance? is 1/(n-3) works fine in this case?
>
>If I be able to compute the estimated standard error from individual studies
>based on their confidence intervals: (CI upper - CI lower)/3.92 for 95% CI,
>then specify sei within the escalc function to compute the variance. Does
>this approach serve better estimation for the model?
>
>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