[R-meta] SEM of correlational meta-analytic data?

Mike Cheung m|kew|cheung @end|ng |rom gm@||@com
Mon Jan 18 10:23:56 CET 2021

Dear Gladys,

Added to what Wolfgang said, neither SEM nor MASEM automatically makes your
(meta)-analyses supporting causality claim. If you have a causal model, SEM
and MASEM provide a tool to test whether your model is consistent with your

If you are meta-analyzing indirect effects, you may be interested in the
following preprint. https://psyarxiv.com/df6jp/

 Mike W.L. Cheung               Phone: (65) 6516-3702
 Department of Psychology       Fax:   (65) 6773-1843
 National University of Singapore

On Mon, Jan 18, 2021 at 4:01 PM Gladys Barragan-Jason <gladou86 using gmail.com>

> Dear Wolfgang,
> Thanks for your helpful reply. Actually I am not (randomly) assuming the
> causality. For instance, most of the correlational studies I included in
> the meta-analysis (from which I extracted Pearson correlations) also
> performed a SEM showing that Human-nature connectedness mediates the
> effect. Would reporting how many papers actually report such causation
> and/or making a meta-analysis on the extracted beta would make more sense?
> For the latter possibility, another problem is that the number of
> moderators included in the the SEM would differ between studies...
> What do you think?
> Thanks a lot for your reply.
> Best,
> Gladys
> Le dim. 17 janv. 2021 à 12:11, Viechtbauer, Wolfgang (SP) <
> wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :
>> Dear Gladys,
>> Inferring causality from observational data is tricky business. SEM (with
>> primary data) or meta-analytic structural equation modeling (MASEM) does
>> not magically allow us to do so just by fitting some model.
>> But if you want to do MASEM, then the MetaSEM package is a good choice. I
>> also recently added some functionality to metafor that goes a bit in the
>> same direction. See:
>> https://wviechtb.github.io/metafor/reference/rcalc.html
>> https://wviechtb.github.io/metafor/reference/matreg.html
>> Note that you will need to install the 'devel' version of metafor to make
>> use of these functions:
>> https://wviechtb.github.io/metafor/index.html#installation
>> Best,
>> Wolfgang
>> >-----Original Message-----
>> >From: R-sig-meta-analysis [mailto:
>> r-sig-meta-analysis-bounces using r-project.org]
>> >On Behalf Of Gladys Barragan-Jason
>> >Sent: Sunday, 17 January, 2021 11:23
>> >To: r-sig-meta-analysis using r-project.org
>> >Subject: [R-meta] SEM of correlational meta-analytic data?
>> >
>> >Dear all,
>> >
>> >I am conducting a meta-analysis on the causes and consequences of
>> >human-nature connectedness. As most of the studies were correlational, I
>> >collected zero order Pearson r correlations between HNC and let's say 3
>> >moderators (Exposure to nature, human-welfare and nature conservation). I
>> >was able to obtain positive and moderate estimates in running one model
>> by
>> >moderator with lab and study as random effect thanks to the rma.mv
>> >function which was great.
>> >
>> >My only concern now if whether we could somehow infer causality from
>> those
>> >meta-analytic data in making Structural Equation Modelling (SEM) on those
>> >data. I saw that the MetaSEM package can do so but I have the feeling
>> that
>> >it is not using the same structure/function as metafor (e.g. meta3
>> instead
>> >of rma.mv) leading to some discrepancies.
>> >
>> >I would like to know if someone has developed a package or a function to
>> do
>> >this type of causal analysis from meta-analytic correlation data.
>> >
>> >The aim would be validate (or invalidate) a model where exposure to
>> nature
>> >increases HNC which in turn increases Nature conservation and welfare
>> >(rather than the opposite). I don(t know if it is feasible but would be
>> >great if so.
>> >
>> >Any advice would be more than welcome :-)
>> >
>> >All the best,
>> >
>> >Gladys
> --
> ------------------------------------------
> Gladys Barragan-Jason, PhD.  Website
> <https://sites.google.com/view/gladysbarraganjason/home>
> Station d'Ecologie Théorique et Expérimentale (SETE)
> CNRS de Moulis
> [image: image.png][image: image.png]
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