Running some path analysis models. I have one model with 6 variables and another model with 5 variables.
The 5 variable model has an AIC = 30 and a BIC = 80, R Squared = .30
The 6 variable model has an AIC = 40 and a BIC = 110, R Squared = .40
All other fit measures are about equal, with the 6 variable model a miniscule bit better. The chi square and p value is also minimally better for the 6 variable model.
That 6th variable we added is what we're most interested in as researchers. It adds more explanatory value, but the AIC and BIC are a little bit worse.
What would be the best route to take regarding model selection?