The significance of moderations was tested by first dropping all moderation terms, then testing all possible nested models. The fit of all models was assessed using Akaike’s Information Criterion (AIC; Akaike, 1987). AIC is derived as minus twice the log likelihood (-2ll) minus twice the degrees of freedom (df; AIC = -2ll-2df). The fit of nested models was assessed using both AIC and the chi-square (χ2) comparison test. The χ2 comparison test involves calculating the χ2 of the nested model as the difference between the -2ll’s of the full and the nested model. We also calculate the difference in degrees of freedom (calculated as the change in number of estimated parameters) between the full and nested models. A p < 0.05 indicates a significant deterioration in the fit of the nested model compared with the full model, indicating that the dropped parameters should remain in the model. If p > 0.05, we have evidence that the parameters can be dropped to create a more parsimonious model.
