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Correction for Dependency - Meta-Analysis

Hey Everyone,


I couldn´t find it, and was thus wondering whether JASP allows me to correct for dependency when conducting a meta-analysis.


Best,


Max

Comments

  • Hi Max,

    I'll direct your question to our meta-analysis expert. Just for my own education: what exactly do you mean with "dependency"?

    Cheers, E.J.

  • Hey E.J.,

    Thank you for your help!

    I think I need to give a bit more concrete background:

    A recent paper found that there are many different techniques for data pre-processing (e.g. trimming/log-transformation) used in my subfield. The authors showed that those different pre-processing techniques had profound influence on whether one sample dataset showed "significant" effects, even when using more complex analyses such as Bayesian analyses or LLMs.

    I planned on re-analysing existing studies using each of those preprocessing techniques and then compare the mean effect sizes of each preprocessing pathway. That means, I have one effect size per pre-processing pathway, each of which is build on the same data (e.g. 4 effect sizes per study). So if I were to run a sub-group meta-analysis with the preprocessing pathway as group-factor, then the effect sizes in the study would not be independent of each other. Simply put, dependency here means that (some of) the effect sizes are based on the same studies.

    Thank you for your time!


    Best,


    Max

  • Hi Max,

    The meta-analysis module in the current version of Jasp does not yet allow for this. The next release probably will allow for what is sometimes called 'multivariate meta-analysis'. If you need to get to it right away, I recommend to have a look at the rma.mv() examples in the metafor package for R.

    Hope that helps!

    Best,

    Raoul

  • Dear Raoul,


    thank you for your response! Unfortunately, I did not work with R yet, but I will see if I have time to look into it. When is the next release scheduled?


    Thank you for your time!


    Best,


    Max

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