[R-meta] Small-sample adjustment in robust versus coef_test function
roe@ch @end|ng |rom cb@@mpg@de
Mon May 11 12:21:58 CEST 2020
I am currently running a three-level meta-analysis and I aim for cluster robust tests and confidence intervals.
Therefore, I applied the "robust" function to my rma.mv object.
I then wanted to validate my results using the "coef_test" from the ClubSandwich package,
applying it to the rma.mv object just as I did with the robust function.
For my overall model (including NO moderators), I get perfectly identical results.
However, when it comes to moderator analyses, the results are marginally different depending on the use of robust or coef_test.
Specifically, the estimates are still identical, however the standard errors seem to be slightly larger for the coef_test function, leading to smaller t-values.
Specifically, the formulas I used are for instance:
valenceeff <- rma.mv(yi, vi, mods = ~ valencesimple, random = list(~ 1 | esID, ~ 1 | searchID), tdist=TRUE, data=alldata)
summary(robust(valenceeff, cluster=alldata$searchID, adjust=TRUE))
coef_test(valenceeff, cluster=alldata$searchID, vcov="CR2")
I guess that this difference occurs by virtue of different small-sample adjustments?
In coef_test, I applied the bias-reduced linearization adjustment proposed by Bell and McCaffrey (2002) and Pustejovsky and Tipton (2017),
whereas robust uses the factor n/(n−p) as a small-sample adjustment.
Can anyone explain the difference to me and point out, which small-sample adjustment is preferable?
Thank you so much!
More information about the R-sig-meta-analysis