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

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On Behalf Of Gladys Barragan-Jason
Sent: Sunday, 17 January, 2021 11:23
To: r-sig-meta-analysis at 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

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 at r-project.org]

On Behalf Of Gladys Barragan-Jason
Sent: Sunday, 17 January, 2021 11:23
To: r-sig-meta-analysis at 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

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/

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On Mon, Jan 18, 2021 at 4:01 PM Gladys Barragan-Jason <gladou86 at 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 ? 12:11, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> a ?crit :

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 at r-project.org]

On Behalf Of Gladys Barragan-Jason
Sent: Sunday, 17 January, 2021 11:23
To: r-sig-meta-analysis at 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 Th?orique et Exp?rimentale (SETE)

CNRS de Moulis

[image: image.png][image: image.png]