I’m comparing a mediator model (type= general) with a moderator model, which requires numerical integration (type=random; algorithm= integration). Am I right in assuming that the AIC/BIC of the two models are not comparable? And if yes, why and is there any literature about this topic?
Thanks for your quick response. My dependent variables are the same: 1. Mediator-Model: Analysis: type=general Model: X by X1.1. X1.2. X1.3. X1.4. X1.5 Y by Y1.1. Y1.2. Y1.3. Y1.4. Y on Z C X X on Z X on C Model indirect: Y ind Z; Y ind C; 2. Moderator-Model: Analysis: type: random; algorithm= integration X by X1.1. X1.2. X1.3. X1.4. X1.5 Y by Y1.1. Y1.2. Y1.3. Y1.4.
ZxX | Z xwith X; CxX | C xwith X; Y on X Z C ZxX CxX;
The mediator model has significant effects whereas the moderator model has no significant interaction-effects – nevertheless BIC and AIC suggest the first model. I’m aware that the information criterions have nothing to do with significance, even so I’ m still afraid that I’m missing something regarding AIC/BIC.
The model with interactions (moderation) has extra parameters and those are not significant. That means that BIC is worse for this model because the likelihood is not improved enough to compensate for the extra parameters. So the results make sense to me.
Margarita posted on Friday, March 27, 2015 - 7:52 am
Dear Dr. Muthén,
I wanted to clarify something about the AIC. Can it be used to compare models with different parameters? Or do they need to have at least the same number of parameters to be comparable?