[R-meta] SEM of correlational meta-analytic data?
Gladys Barragan-Jason
g|@dou86 @end|ng |rom gm@||@com
Mon Jan 18 10:31:48 CET 2021
Dear Mike,
Thanks a lot for your reply. Yes, I just want to see whether my data are
more consistent with a causal vs. bidirectional model. Is metaSEM
appropriate to do this?
Best,
Gladys
Le lun. 18 janv. 2021 à 10:24, Mike Cheung <mikewlcheung using gmail.com> a
écrit :
> 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 data.
>
> 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
> http://mikewlcheung.github.io/
> <http://courses.nus.edu.sg/course/psycwlm/internet/>
> ---------------------------------------------------------------------
>
> On Mon, Jan 18, 2021 at 4:01 PM Gladys Barragan-Jason <gladou86 using gmail.com>
> wrote:
>
>> 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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>
>
>
--
------------------------------------------
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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