[R-meta] multiple models in one study
Viechtbauer, Wolfgang (NP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Thu Mar 23 09:14:01 CET 2023
I would say this depends on the aims. If there is one key predictor of interest, then I would focus on that. If that's not the case, then I would extract all the ones that are of interest. Taking an average of all coefficients (if this is what the "average-set" approach entails) doesn't make much sense to me unless they are all measuring the same construct in the same direction and in the same units (all unlikely).
If you extract multiple coefficients, you of course have to account for the fact that they are not independent.
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of Valeria Ivaniushina via R-sig-meta-analysis
>Sent: Wednesday, 22 March, 2023 17:52
>To: R meta
>Cc: Valeria Ivaniushina
>Subject: [R-meta] multiple models in one study
>I want to perform a meta-analysis of the relation between the outcome and
>key explanatory variables expressed as regression coefficients.
>As a rule, authors report several models with different specifications. I
>wonder which regression coefficients should I collect?
>In the book Meta-regression analysis in economics and business (Stanley &
>several approaches are described:
>- The best-set = ONE estimate from each study, using the KEY regression
>from each paper
>- The average-set = an average of all coefficients reported in the study
>- The all-set = all relevant estimates reported in the study
>Which approach is preferable? Are there additional considerations that I
>have to take into account?
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