# Why no talk of n-level meta analyses instead of three-level meta-analyses?

I have a data set that I'm going to use for a meta-analysis that has a lot of dependencies between the effect sizes. Because of this, I've started to look into how people normally handle this, resulting in me finding a couple of articles (e.g. Konstantopoulos 2010; Cheung 2014) about three-level meta-analyses.

However, what puzzles me with all of these articles is that they're always talking about three-level meta-analyses rather than starting from the more general n-level meta-analyses. For my data set, I have what probably would count as a four-level structure (effect sizes clustered in testing occasions; testing occasions clustered into samples of participants; and samples of participants clustered into published papers) but I never find any examples of this in the literature. As of now, I'm shifting the unit of analysis by creating grand means on the sample level (meaning that I have several effect sizes for the same sample, corresponding to different factors of the certain moderator I'm interested in for the time being), and then I apply some kind of three-level analysis on that data, but I've never seen any examples of this in the literature either.

Am I missing something here?

References

• Cheung (2014). Modeling dependent effect sizes with three-level meta-analyses: a structural equation modeling approach. Psychological Methods, 2, 211-229.
• Konstantopoulos (2011). Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods, 2, 61-76.
• You can fit multilevel models with as many levels as you deem suitable for your data with rma.mv() in the metafor package in R. – Wolfgang May 18 '16 at 17:58