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I am doing a mini-meta-analysis of four correlational studies.

In the estimated model I have three predictors and one DV.

Using the meta-package in R, I use the following code to test the link between one IV and DV.

m1 <- metacor(c(-.18, -.09, -.38, -.03), c(886, 1050, 478, 1043))
m1
forest(m1)

I have three questions.

  1. Should entered correlations be zero-order correlations between IV and DV or partial correlations where the other two predictors from my model are being controlled for?

  2. Should I enter correlations or should I use Fisher's z transformed correlations? My understanding is that they should be regular correlations, as in the details of the meta-analytical method of metacor function says it is using Fisher's z transformation of correlations, which I guess means that entered correlations are being transformed to Fisher’s z by default?

  3. The default estimator is DerSimonian-Laird estimator. Is it ok to use it?

Thanks! Aneta

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    $\begingroup$ I am voting to leave this open as questions 1 and 3 seem to me to demand statistical knowledge although I agree question 2 is about the options of an R package. $\endgroup$
    – mdewey
    Mar 26, 2021 at 16:31

1 Answer 1

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Q1 - that depends on your scientific question. They measure different things, which is relevant for you?

Q2 - You can determine that by finding the default for the sm parameter but I think it transforms them for you which is what you want.

Q3 - it it probably better to use REML although it is hard to give an answer which applies across all situations. In a paper entitled Bias and efficiency of meta--analytic variance estimators in the random--effects model Viechtbauer discusses five different estimates of $\tau^2$ including DL and REML.

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