Hi, thank you for your videos! They have been extremely useful. In a different video you say that the weight function attributes more weight to studies that are less likely to be published (p>0.05) and less weight to those more likely to be published (p
@@dsquintana damn :o thanks! Is it frequent for you to highlight the values given by weightfunction correction in the abstract, for example? In the past I have used TaF, but never gave it too much focus through the paper.
This depends, but I’d be hesitant to treat the bias-corrected values as definitive, as they’re estimates. You *could* say something like, “the summary effect size estimate was statistically significant but a meta-analysis using selection models indicated evidence of publication bias, with a bias-corrected corrected effect estimate that was considerably smaller than the original estimate...”
How come I will get an error saying 'Random effects model: non-finite finite-difference value [1]'? It also showed a warning note that at least one p-value contains no effect sizes. Thanks for your help!