Hi Gladys,
When you're talking about causality are you talking about high
heterogeneity or the study design of the effects sizes?
Kyle
W. Kyle Hamilton | Doctoral Candidate
University of California, Merced | Psychological Sciences
5200 N. Lake Rd. Merced Ca. 95343
Tel (559) 325-4166 ? Cell (559) 392-5782
psychology.ucmerced.edu ? kylehamilton.com
On Sun, Jan 17, 2021 at 2:23 AM Gladys Barragan-Jason <gladou86 at gmail.com>
wrote:
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
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