I am currently performing a meta-analysis on bank relationship and firm performance with 27 different studies; almost all of them report different cases.

I calculated the partial effect size (I have different dependent and independent variable), its variance and confidence interval using the formula provided by Aloe & Thompson, "The synthesis of partial effect size".

Due to the studies-specifics I have now to face two issues: - Studies have different sample size and different specification, so heteroskedasticity is likely to arise; - Studies present several cases, so some of the observation in my meta-analysis are not independent;

I thought to face the second issue by using what in econometrics would be called an "study-fixed effect regression" that, if I got correctly, in the metafor package should be:

  res <- rma(Yi, Vi, mods = ~ I(study))

That is, a Mixed-Effects Model. I would also like to compare these results with the one of a multilevel\hierarchical model (Gelman & Hill, 2006).

Is the ram.mv package good for that or I have to manually write the function following Gelman & Hill? If it is good, how can I can calculate my Variance-Covariance Matrix considering the fact that I have different independent variable? Last, how can I account for heteroskedasticity?

Thank you for your time

  • $\begingroup$ Asking for code is off-topic here but your thoughts about rma.mv are correct. I would suggest reading some of the examples on the metafor pages metafor-project.org/doku.php for detailed code. $\endgroup$ – mdewey Apr 24 '17 at 12:58

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