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I am testing competing hypotheses where one hypothesis contains a mediation effects that can be modeled using a path model. The other hypothesis does not include a mediation effect and therefore can be modeled using a simple linear regression. Both models will be run using the same dataset and will have the response variable (or exogenous variable in the path model's case). Therefore, I am wondering if it is appropriate to utilize AIC to compare/rank these models? The path model is being built using the piecewiseSEM package. I have read papers where they have used AIC selection for different path models but never a case when comparing a path model to a non-path model.

Any thoughts would be appreciated.

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  • $\begingroup$ Could you not formulate both models in the path-model nomenclature to make their AICs directly comparable? $\endgroup$ May 24, 2021 at 10:16
  • $\begingroup$ Unfortunately, this doesn't work. When I run the non-mediation model with a basic glm the aic is 100+ . When the same glm is run inside the path-model the AIC goes to 6... $\endgroup$
    – Leo Ohyama
    May 24, 2021 at 14:02

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Assuming your mediation model is of the form $A \rightarrow B \rightarrow C$ and $A \rightarrow C$, where $C$ is your dependent variable and $B$ is the mediator, then you can indeed run a "non-path" model. Simply break the mediation path and run the model $A \rightarrow B$ and $A \rightarrow C$. This is accomplished by setting the path coefficient from $B$ to $C$ to zero. This will give you comparable models to use for either $AIC$ comparison (using the same set of variables and the same data set), and it will also allow you to conduct a $\chi^2$ model comparison as well (as these are nested models).

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