selection indices that balance model fit and parsimony, but penalize model complexity to different degrees. When comparing models, generally, lowest values of both, or a scree-plot like test (i.e., where AIC and BIC values begin to level off) may be utilized to determine optimal model fit [28, 29]. Each model was run with 50 starting values to avoid problems with local maxima [30].
