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[R-meta] Moderator analysis test of residual heterogeneity confusion

Oops, let me try that again...

I am using the metafor package to run a multilevel correlated effects model. For moderator analyses, I am running them one at a time, to see how much heterogeneity each accounst for, and then I ran model with all mods to see how much variance is left to be explained they're combined. 

I have an odd a situation where there is no significant residual variance with just an individual moderator in the model, but then for a set of moderators (that includes that moderator) there is significant residual variance. How can this be?


Maybe this output can help...


Single moderator results:

Multivariate Meta-Analysis Model (k = 456; method: REML)

   logLik   Deviance        AIC        BIC       AICc 
 112.1356  -224.2713  -206.2713  -169.3281  -205.8603   

Variance Components:

            estim    sqrt  nlvls  fixed  factor 
sigma^2    0.0136  0.1166     51     no   study 

Test for Residual Heterogeneity:
QE(df = 448) = 409.9810, p-val = 0.9007

Test of Moderators (coefficients 1:8):
F(df1 = 8, df2 = 448) = 6.2947, p-val < .0001

All mods results:

Multivariate Meta-Analysis Model (k = 389; method: REML)

  logLik  Deviance       AIC       BIC      AICc 
-36.0635   72.1270  186.1270  403.1911  210.1707   

Variance Components:

            estim    sqrt  nlvls  fixed  factor 
sigma^2    0.0330  0.1818     43     no   study 

Test for Residual Heterogeneity:
QE(df = 333) = 1028.1159, p-val < .0001

Test of Moderators (coefficients 2:56):
F(df1 = 55, df2 = 333) = 4.0802, p-val < .0001

Thank you for your help!

My best,

Mia