[R-meta] Estimating partial correlation coefficients from multi-level regression models

[Vainius Bartasevičius]

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

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