[R-meta] [External] Re: 4-Level analysis in metafor
Hi Michael, I am fitting age as a moderator because my primary research question is to see whether the effect size changes with age, I just did not send along my meta-regression. As I see it, datasource would be a random effect because there were 36 distinct datasources and each had different age ranges (i.e., wave). If you think it would be important to consider datasource as a fixed effect moderator, I will happily give it a chance, I just am not sure about what that would mean theroetically. Jordan
From: Michael Dewey <lists at dewey.myzen.co.uk>
Sent: Saturday, March 5, 2022 7:25 AM
To: Harris, Jordan L <jordan-l-harris at uiowa.edu>; r-sig-meta-analysis at r-project.org <r-sig-meta-analysis at r-project.org>
Subject: [External] Re: [R-meta] 4-Level analysis in metafor
Sent: Saturday, March 5, 2022 7:25 AM
To: Harris, Jordan L <jordan-l-harris at uiowa.edu>; r-sig-meta-analysis at r-project.org <r-sig-meta-analysis at r-project.org>
Subject: [External] Re: [R-meta] 4-Level analysis in metafor
Dear Jordan I know this is not what you asked but are you sure that all of those should be random effects? Do you not want to fit age as a fixed effect as a potential moderator? I also wonder about datasource. Michael On 04/03/2022 16:29, Harris, Jordan L wrote: > Hi all, > > 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) > > Variance Components: > > 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! > Thanks, > Jordan > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-meta-analysis mailing list > R-sig-meta-analysis at r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis > -- Michael http://www.dewey.myzen.co.uk/home.html