[R-meta] Addressing multicollinearity among moderators in a meta-regression (metafor package)

Hamed Qahri-Saremi h@med@q@hr|@@@rem| @end|ng |rom gm@||@com
Mon Feb 4 20:51:34 CET 2019

Greetings everyone;

I hope my message finds you well.

I am conducting a series of moderation tests using meta-regression in the
metafor package. I have six numeric (continuous) moderators, which are
different cultural dimensions. In my data, several of these six moderators
are moderately to very highly correlated (0.40 - 0.80). As a result, to
avoid any biases in meta-regression analyses due to multicollinearity, I am
testing these moderators separately (i.e., six univariate meta-regressions)
rather than including all of them in one multivariate meta-regression with
six moderators. While this solution prevents the multicollinearity from
biasing the results, it introduces a major limitation in my tests because
essentially I am not controlling for the relations among moderators by
testing them separately. Therefore, I was wondering if there is a better
solution to rectify or reduce the effects of multicollinearity among
moderators while running a multivariate meta-regression in metafor.

I appreciate any advice you might have,

Warm regards,

Assistant Professor of Information Systems | College of Computing and
Digital Media, DePaul University | SAP Certified Associate &
Instructor | Chicago,
IL 60604 | Phone: 312-362-5841 | Email: hamed.saremi using depaul.edu | Webpage:

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