Meta-Analysis: Summarizing several effects of a study I am currently conducting a meta-analysis looking at a rather global effect. Therefore, some of the included studies report several effects which can be included. As far as I read in the literature, it is most common to take an average of the reported effects per study to not introduce a bias. In some cases the effects cancel out, but it seems somehow wrong to just report a "null"-effect, as it might be quite interesting to see in which cases the effects are negative and in which they are positive.
Is it somehow possible to include all the effects measured within a single group, or at least display all "subeffects" in a forest plot?
At the moment, I am using the metafor package in R, using a random-effects model.
Thanks a lot to anyone reading and thinking about this!

Thanks to the hints in the comments, I came to the following solution:
I included all extracted effects in the model.
I used the  vcalc(..) function to approximate the var-cov matrix of dependent estimates, and used the rma.mv(..) with random = ~ 1 | study_id/subeffect to account for the random effect of each study and each subeffect.
Finally I used the robust(..) method for cluster-robust estimate of the variance-covariance matrix.
Very helpful for understanding were the Credé example and the general workflow recommendations (both from the metafor website) .
