A comparison of compensatory and noncompensatory models of judgment: Effects of task predictability and degrees of freedom

We employ a bankruptcy prediction task to compare the validity of linear compensatory models of judgment to a nonlinear, noncompensatory model of judgment (hierarchical partitioning) given two levels each of task predictability and degrees of freedom. We compare these modeling methods on three measures of validity (predictive, diagnostic, and structural). To aid model comparison, information acquisition protocols are used to develop benchmark measures of cue importance. We find that hierarchical partitioning methods perform significantly better than the linear method with respect to predictive validity and structural validity when task predictability is higher, and particularly when degrees of freedom are low. Measures of diagnostic validity are consistently higher for the hierarchical model, bur no pairwise comparison is found to be statistically significant at acceptable levels.