[R-meta] Pseudoreplication

[Gladys Barragan-Jason]

Dear Wolfgang,
Thanks a lot for your helpful response which reinforces my belief that this
was the right thing to do.
All the best,
Gladys

Le jeu. 30 sept. 2021 à 08:38, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :

> Dear Gladys,
>
> Along the lines of what Michael already said -- it seems like the reviewer
> might not be familiar with modern meta-analytic methods (like the
> multilevel/multivariate models we frequently discuss on this mailing list).
> I cannot tell you whether what you have done is fully sufficient to capture
> all potential sources of heterogeneity and dependency in your data, but
> it's a fairly standard (four-level) multilevel random-effects model that
> allows for correlation in multiple effects belonging to the same study and
> multiple studies belonging to the same article. So this does take
> 'pseudoreplication' into consideration.
>
> 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: Tuesday, 28 September, 2021 16:08
> >To: Michael Dewey
> >Cc: R meta
> >Subject: Re: [R-meta] Pseudoreplication
> >
> >Dear Michael,
> >thanks a lot for your reply.
> >I have a structure like this:
> >
> >Article ID.         Study ID.           Effect size.
> >X et al.              S1                       0.5
> >X et al.              S2                       0.8
> >Y et al.              S1                       0.2
> >Y et al.              S2                       0.6
> >Y et al.              S2                       0.2
> >Z et al.              S1                       0.1
> >Z et al.              S1                       0.5
> >
> >and I use: random=~1|articleID/studyID/estID with estid <- 1:nrow(data)
> >Thanks,
> >Gladys
> >
> >Le mar. 28 sept. 2021 à 15:57, Michael Dewey <lists using dewey.myzen.co.uk> a
> écrit :
> >Dear Gladys
> >
> >I think the referee may have misunderstood what you did. We may need
> >more details about your data structure to be absolutely sure you have
> >the right structure but it seems superior to the suggested rather
> >indirect way of going about things.
> >
> >Micael
> >
> >On 28/09/2021 13:06, Gladys Barragan-Jason wrote:
> >> Dear all,
> >>
> >> I am contacting you to have your advice about a reviewer comment on a
> >> meta-analysis. The reviewer said that as we used multiple comparisons
> >> from within studies (e.g., five effect sizes from one particular paper
> >> comparisons), this raises the potential of pseudoreplication issue and
> >> he/she suggests to do the following:  recalculate the overall effect
> >> sizes after sampling one comparison from each separate study and
> >> calculate the estimated mean and 95% CI of effect size by bootstrap
> >> resampling 1,000 times in R.
> >> However, I think I've already taken into account non-independence of the
> >> data by including three random effects using metafor to control for
> >> multiple data from the same article, multiple data from same
> >> participants from the same study and multiple estimates within a study
> >> within a lab. So I coded random effect as follows:
> >> ~1|articleID/studyID/estID. I think this is the right way to take into
> >> account heterogeneity of the data but I would like to know if I am
> >> correct or if the pseudoreplication techniques gave something different
> >> and/or complementary?
> >> Thanks in advance for your help!
> >> Best,
> >> Gladys
>


-- 

------------------------------------------

Gladys Barragan-Jason, PhD.  Website
<https://sites.google.com/view/gladysbarraganjason/home> / Site web
<https://sites.google.com/view/frgladysbarragan-jason/accueil>

Station d'Ecologie Théorique et Expérimentale (SETE)

CNRS de Moulis

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

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