Hi List Members,
I am a graduate student who is new to R and meta-analyses, and I have been running into problems getting my code sorted out.
I am conducting a meta-analysis to explore how the structure of psychopathology changes across childhood and adolescence. My effect size of interest is represented by a proportion score that is conceptualized as ratio of variance accounted for by a general factor, called "general_es" (i.e., general / general + specific). These data do not currently have a sampling variance, nor have transformed effect sizes been calculated. I have 3 levels of nested data: Level 1 = "timepoint_id", Level 2 = "sample_id", Level 1 = "study_id" which account for non-independence of data. Here, I will call my data file "dat."
1. How should I structure the escalc command to derive a "yi" and "V" values needed for the rma.mv analysis? Would my measure be "PLO"?
1. Would this structure be acceptable: rma.mv(yi, vi, random = ~ 1 | study_id/sample_id/timepoint_id, data=dat)?
Thanks,
Jordan