Hierarchical Bayes procedures for the two-parameter logistic item response model were compared for estimating item parameters. Simulated data sets were analyzed using two different Bayes estimation procedures, the two-stage hierarchical Bayes estimation (HB2) and the marginal Bayesian with known hyperparameters (MB), and marginal maximum likelihood estimation (ML). Three different prior distributions were employed in the two Bayes estimation procedures. HB2 and MB yielded consistently smaller root mean square differences and mean euclidean distances than ML. The HB2 and MB estimates of item discrimination parameters yielded relatively larger biases than the ML estimates. As the sample size increased, the three estimation procedures yielded essentially the same bias pattern for item discrimination. Bias results of item difficulty show no differences among the estimation procedures. Tight prior conditions yielded smaller root mean square differences and mean euclidean distances. An appendix discusses the estimate of the unknown item parameters in detail. (Contains 2 figures, 4 tables, and 45 references.) (Author/SLD)