[R-meta] 4-Level analysis in metafor
Harris, Jordan L
jord@n-|-h@rr|@ @end|ng |rom u|ow@@edu
Fri Mar 4 17:29:55 CET 2022
Does rma.mv appropriately account for between- and within-cluster variance for 4 level nested data?
rma.mv(yi=ES, V=sampling_variance, slab=authors, data=Data, random = list(~ 1 | datasource_id/wave_id/study), tdist=TRUE, method="REML")
study_id = included study
datasource = the source of data (e.g., large cohort study or independent samples)
wave_id = the wave of the datasource (i.e., age) from which the study was analyzed
Multiple effect sizes can occur at a given wave in a given data source. Multiple effect sizes also exist in a given study at a given wave. Provided this information, it might be important to nest studies within waves within data sources. I ask because I see that the sigma^2.2. estimate of my output is nearly 0 and I was not sure if this is an accurate reflection of my data or metafor's ability to account for differences at this added level? Should I use the 0 estimate at 2.2 to justify a removal of wave_id from the nesting?
Multivariate Meta-Analysis Model (k = 100; method: REML)
estim sqrt nlvls fixed factor
sigma^2.1 0.0069 0.0832 41 no datasource_id
sigma^2.2 0.0000 0.0000 60 no datasource_id/wave_id
sigma^2.3 0.0023 0.0482 82 no datasource_id/wave_id/study_id
I am a graduate student, and I am new to meta-analyses, and I would love any feedback!
[[alternative HTML version deleted]]
More information about the R-sig-meta-analysis