-----Original Message-----
From: R-sig-meta-analysis <r-sig-meta-analysis-bounces at r-project.org> On Behalf
Of Vainius Bartasevicius via R-sig-meta-analysis
Sent: Thursday, January 25, 2024 16:31
To: r-sig-meta-analysis at r-project.org
Cc: Vainius Bartasevi?ius <vainius.bartasevicius at tspmi.vu.lt>
Subject: [R-meta] Estimating partial correlation coefficients from multi-level
regression models
Dear All,
I am writing to you with an inquiry on the estimation of partial correlation
coefficients (PCCs) using escalc function of the metafor package.
Currently we are conducting a meta-analysis which draws on data from multiple
regression models and uses partial correlation coefficient as an effect size.
Some of the models included in our meta-analysis come from multi-level analyses.
Our predictors of interest are at level 1, so is the outcome measure.
We are a bit unsure about the correct way to estimate PCCs using estimates from
multi-level models, given that the calculation of degrees of freedom in multi-
level models is different from that applied in single-level regression. In
particular, we are wondering what figure we should provide for mi argument
(total number of predictors) when estimating PCCs from multi-level models.
Should we take the sum of level 1 and level 2 predictors? Should we take into
account the number of level 2 units when specifying this argument?
Any help would be greatly appreciated - thank you very much for your time.
Kind regards
Vainius Bartasevi?ius