[R-meta] [External] Re: 4-Level analysis in metafor

Harris, Jordan L jord@n-|-h@rr|@ @end|ng |rom u|ow@@edu
Sat Mar 5 17:07:54 CET 2022

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.

From: Michael Dewey <lists using dewey.myzen.co.uk>
Sent: Saturday, March 5, 2022 7:25 AM
To: Harris, Jordan L <jordan-l-harris using uiowa.edu>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using 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.


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
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