[R-meta] [External] Re: 4-Level analysis in metafor
Going by the two examples you gave of datasources I assumed something different. I agree that is not a fixed effect.
On 05/03/2022 16:07, Harris, Jordan L wrote:
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 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]]
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