more precisely recovered as data structure becomes less complicated and as more observations are available. Table 4 also indicates that the complexity of the design matrix had an appreciable impact on identifying the prior distributions. Compared to the null and pair cases, the design matrix with the triplets of locally dependent items resulted in substantially larger distances between the true and estimated π s. This implies that, while the existence of a small subset of dependent items does not significantly degrade the prediction of π , the larger number of interrelated items or the greater complexity in item network can have a severe impact on π recovery, and in turn, examinee classification using the prior information. In brief, the simulation study supports several conclusions. First, the GDCM and the pseudo- likelihood estimator can recover the model parameters with reasonable accuracy in the presence of local item dependence. The suggested methods are found particularly effective in regulating the biasedness of item parameter estimates. Second, the estimator presents a reliable projection of the design matrix, thereby allowing for valid information in regards to the existence and degree of item interactions. The following section shows how this information can be used for drawing meaningful inference about test items through an empirical data analysis. Third, the algorithm provides an accurate approximation of the prior distribution despite the unknown latent class relationship. The proposed procedure can therefore be used when the prior distribution needs to be empirically determined during the parameter estimation. This section reports an application of the GDCM to real data collected from a well-known per- sonality questionnaire. The data set consists of responses of 824 females to Eysenck Personality Table 4: Recovery of Prior Distribution 6 Real Data Example 6.1 Data K = 3 K = 4 Graph N = 500 N = 1000 N = 3000 N = 500 N = 1000 N = 3000 Null .027 .017 .009 .040 .027 .016 Pair .024 .016 .008 .040 .028 .017 Triplet .043 .038 .034 .064 .056 .051 20