The fit of nested models was assessed as a function of the change in the value of twice the log likelihood of the data, which is distributed as a χ2 statistic with degrees of freedom (df) equal to the difference in the number of parameters estimated between models. A significant Δχ2 indicates a significant deterioration in model fit, which would result in rejection of the nested model. We also used Akaike’s Information Criterion (AIC; Akaike, 1987) to select models. A lower AIC value indicates a better balance between the explanatory power of a model and parsimony.
