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

[Viechtbauer, Wolfgang (SP)]

The first model has 8 model coefficients with k = 456. The second model has 58 model coefficients with k = 389. So, the datasets used in those two analyses are not the same (probably because of missing values on some of the moderators included in the second model). So, it's a bit difficult to compare the results. This aside, including 58 model coefficients with k = 389 is not a good idea. This is likely to lead to overfitting. 

Also, I notice that the random effect you added is called 'study', while k is much larger than the number of studies. This was actually just discussed on this mailing list, but to repeat: You really should also include an observation-level random effect in the model.

Best,
Wolfgang

-----Original Message-----
From: Mia Daucourt [mailto:miadaucourt using gmail.com] 
Sent: Wednesday, 18 September, 2019 19:35
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis using r-project.org
Subject: Re: [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

On Sep 18, 2019, at 12:50 PM, Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

Dear Mia,

Your screenshots did not come through properly. Note that this a text-only mailing list, so please post output, not screenshots. Also, please post in plain text -- not rich text format or HTML.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Mia Daucourt
Sent: Wednesday, 18 September, 2019 18:24
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] Moderator analysis test of residual heterogeneity confusion

Good afternoon,

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 these screenshots can help...
Single moderator results:
Moderator analysis test of residual heterogeneity confusion

All mods model results:

Thank you for your help!

My best,

Mia