[R-meta] meta-analytic models with overdispersed data
Hi all: I have read numerous websites and searched online forums for help with the following meta-analysis question but still am not clear what the answer is. My study is concerned with the magnitudes of effects (using hedges d as a measure of effect size). So, I have taken the absolute value of the effect size to use as my response variables in random effects models (rma function). However, t*he "absolute" effect sizes are not normally distributed (rather a negative binomial distribution). I presume that the model run via rma() is not appropriate*, given assumptions of normality of the data? I have read discouraging remarks about log-transforming effect sizes for meta-analysis... I understand that bootstrapping can be done to estimate confidence intervals for the data, but what model to use to estimate mean effect sizes is still unclear to me. If you can provide any answers and/or point me to references to clarify how I model overdispersed data for meta-analysis, it would be a great help. Many thanks for your time and assistance.
**************************************************************** *David Daversa, PhD* *Postdoctoral Researcher* *Institute for Integrative Biology, University of Liverpoolddaversa at gmail.com <ddaversa at gmail.com>ddaversa at liv.ac.uk <ddaversa at wustl.edu>* https://eegid.wordpress.com/post-doctoral-researchers/dave-daversa/ <http://www.zoo.cam.ac.uk/zoostaff/manica/drdaversa.htm> [[alternative HTML version deleted]]