Table 2: Absolute Biases of Guessing and Slipping Parameter Estimates consistently demonstrated the smaller estimation errors in retrieving the true parameters. The improvement of the GDCM over the DCM estimates was more salient in the guessing parameters than in the slipping parameters. The pattern of the improvement became pronounced as there existed more dependent items in the test. In Table 1 some anticipated observations may deserve some comments. The table shows that the larger the sample size, the more precisely the parameters were recovered. The more complex the underlying dimensions, it became more difficult to estimate the true parameters. Presented in Table 2 are absolute biases of the item parameter estimates. The trends in the results were largely consistent with those reported for Table 1. In the null case of no local depen- dency, the two modeling approaches showed marginal differences with respect to estimation bias. As some of the test items were interdependent, the two approaches exhibited increasing biases. The GDCM notwithstanding constantly showed the smaller degree of bias across all conditions evalu- ated. The direction of the bias in the estimated parameter values was by and large in concordance with each other. In both the modeling approaches, the guessing parameter estimates typically showed positive biases, whereas the slipping parameter estimates mostly showed negative biases. These patterns suggest that the positive association between the items (i.e., φ$_{jj}$ ′ = 1) results in increase in guessing and decrease in slipping, and thus, decrease in item discriminating power. Table 3 reports results of retrieving the graphical model parameters. The results suggest that true design matrices were quite well recovered by the GDCM. The observed RMSDs were small; 18 K = 3 K = 4 GDCM DCM GDCM DCM Graph N Guess Slip Guess Slip Guess Slip Guess Slip Null 500 .017 .027 .020 .019 .018 .028 .024 .020 1000 .012 .016 .015 .013 .012 .018 .016 .014 3000 .007 .009 .009 .007 .007 .010 .009 .008 Pair 500 .028 .032 .050 .047 .029 .033 .058 .047 1000 .029 .029 .047 .046 .028 .028 .054 .047 3000 .030 .030 .045 .046 .029 .026 .051 .046 Triplet 500 .054 .043 .132 .075 .053 .048 .144 .076 1000 .051 .037 .131 .075 .050 .038 .143 .076 3000 .051 .034 .131 .075 .049 .032 .143 .076