Our goal was to find the model that reflected the optimal balance between parsimony and explanatory power. This goal can be operationalized, for example, by Akaike’s information criterion (AIC),27,28 which equals χ2–2df where df equals the difference in the degrees of freedom of the two models. We seek to minimize the AIC value. After determining a best-fitting model based on AIC, the Mx estimated factor loadings for this model were extracted and rotated in SAS29 using a Varimax rotation criterion to improve interpretability.
