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[R-meta] Score Normalization for Moderator Analysis in Meta-Analysis

Dear Kiet,

I don't mind cross-posting, but when doing so, please indicate this in posts, so in case answers are provided elsewhere, duplicate efforts can be avoided. For reference, this question was also posted here:

https://stats.stackexchange.com/questions/626306/score-normalization-for-moderator-analysis-in-meta-analysis

What you describe under 2 is the 'proportion/percentage of maximum possible' (POMP) score method, which is nicely discussed in this article:

Cohen, P., Cohen, J., Aiken, L. S., & West, S. G. (1999). The problem of units and the circumstance for POMP. Multivariate Behavioral Research, 34(3), 315-346. https://doi.org/10.1207/S15327906MBR3403_2

This approach assumes that the observed values on one scale are linear transformations of the observed values on other scales. Of course that is never quite true, but can hold as a rough approximation. In fact, this is also the assumption underlying various effect size / outcome measures (e.g., standardized mean differences, correlation coefficients), so it is an implicit assumption in many meta-analyses anyway (except that you are now also applying this assumption to the moderator variable). There was a thread related to this in April:

https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2023-April/004529.html

When this assumption is not correct (with respect to the variables involved in computing the correlations or with respect to the moderator variable), then this becomes one of the sources of (residual) heterogeneity. Of course, we have random/mixed-effects models to account for (residual) heterogeneity, so this is not the end of the world. But if scales are measuring entirely different constructs, then we should be more worried if we lump them together.

If you have enough studies, then you can also code the type of scale used to measure social support (e.g., MSPSS versus other or even more fine-grained if you have enough studies) and include this in your moderator analysis and allow it to interact with the POMP score mean of the social support scale. That way, you can examine if the relationship between social support and the strength of the association between LGBTQ+ discrimination and mental health differs depending on the scale used.

Best,
Wolfgang