Two indices were used to evaluate model fit: (1) the likelihood-ratio test (LRT; distributed as χ2, and computed as the difference in the −2 log-likelihood values for the two models); and (2) the Akaike Information Criterion (AIC; Akaike, 1987). The LRT is used as a goodness-of-fit index, representing the degree of fit between model expectations and observed data. Improvements in the model’s fit, from adding or omitting parameters, can be assessed by a statistically significant change in LRT. Akaike’s Information Criterion (AIC; Akaike, 1987) is also conventionally used to compare the fit of alternative models. The AIC statistic considers goodness of fit while penalizing over-parameterization. Because the aim of model fitting is to explain the data as parsimoniously as possible, the model with the lowest AIC value is generally considered best (Markon & Krueger, 2004).
