<div dir="ltr"><div dir="ltr">Dear Michael,<div>thanks a lot for your reply.</div><div>I have a structure like this:</div><div><br></div><div>Article ID. Study ID. Effect size. </div><div>X et al. S1 0.5</div><div>X et al. S2 0.8<br></div><div>Y et al. S1 0.2</div><div>Y et al. S2 0.6</div><div>Y et al. S2 0.2<br></div><div>Z et al. S1 0.1</div><div>Z et al. S1 0.5</div><div><br></div><div>and I use: random=~1|articleID/studyID/estID with estid <- 1:nrow(data)</div><div>Thanks,</div><div>Gladys</div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">Le mar. 28 sept. 2021 à 15:57, Michael Dewey <<a href="mailto:lists@dewey.myzen.co.uk" target="_blank">lists@dewey.myzen.co.uk</a>> a écrit :<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Dear Gladys<br>
<br>
I think the referee may have misunderstood what you did. We may need <br>
more details about your data structure to be absolutely sure you have <br>
the right structure but it seems superior to the suggested rather <br>
indirect way of going about things.<br>
<br>
Micael<br>
<br>
On 28/09/2021 13:06, Gladys Barragan-Jason wrote:<br>
> Dear all,<br>
> <br>
> I am contacting you to have your advice about a reviewer comment on a <br>
> meta-analysis. The reviewer said that as we used multiple comparisons <br>
> from within studies (e.g., five effect sizes from one particular paper <br>
> comparisons), this raises the potential of pseudoreplication issue and <br>
> he/she suggests to do the following: recalculate the overall effect <br>
> sizes after sampling one comparison from each separate study and <br>
> calculate the estimated mean and 95% CI of effect size by bootstrap <br>
> resampling 1,000 times in R.<br>
> However, I think I've already taken into account non-independence of the <br>
> data by including three random effects using metafor to control for <br>
> multiple data from the same article, multiple data from same <br>
> participants from the same study and multiple estimates within a study <br>
> within a lab. So I coded random effect as follows: <br>
> ~1|articleID/studyID/estID. I think this is the right way to take into <br>
> account heterogeneity of the data but I would like to know if I am <br>
> correct or if the pseudoreplication techniques gave something different <br>
> and/or complementary?<br>
> Thanks in advance for your help!<br>
> Best,<br>
> Gladys<br>
> <br>
> -- <br>
> <br>
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> <br>
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</blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria,serif;color:black">------------------------------------------</span></p><p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria,serif;color:black">Gladys Barragan-Jason, PhD. <u></u><u></u></span><a href="https://sites.google.com/view/gladysbarraganjason/home" style="font-family:Tahoma,sans-serif" target="_blank"> Website</a> / <a href="https://sites.google.com/view/frgladysbarragan-jason/accueil" target="_blank">Site web</a></p>
<p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria,serif;color:black">Station d'Ecologie Théorique et Expérimentale (SETE)<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria,serif;color:black">CNRS de Moulis<u></u><u></u></span></p>
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