[R-meta] Adjusted R^2 for rma.mv?

[Frank Bosco]

Hi all,

I am running a variety of multilevel meta-regressions using rma.mv. I am 
estimating pseudo R^2 using the following: (sum(model0$sigma2) - 
sum(model1$sigma2)) / sum(model0$sigma2) -- where model0 is an 
intercept-only model and model1 contains moderators.

I would like to estimate an adjusted R^2 that will penalize the addition 
of moderators. However, the number of studies I am summarizing is large 
(in the thousands). Thus, using the standard formula for adjusted R^2, 
adding 20 or so predictors to the model reduces the R^2 by only decimal 
dust.

Are there any suggestions on how to arrive at something like an adjusted 
R^2 in this context?

Although other fit indices might be preferred, I would like to stay 
within the familiar R^2 metric for ease of interpretation. (Though, I am 
curious as to which other fit indices would be preferred.)

Thanks,

Frank

*Frank Bosco, Ph.D.*
Director, metaBUS.org
Professor
School of Business
Department of Management & Entrepreneurship

Virginia Commonwealth University
Snead Hall
301 West Main Street, Room B4151
Richmond, Virginia 23284
Fax: 804 828-1602
business.vcu.edu <http://business.vcu.edu>

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