This article was concerned with Bayesian estimation of parameters in three parameter logistic models. Although a considerable number of estimation procedures based on likelihood(e.g.joint maximum likelihood estimation, marginal likelihood estimation, conditional maximum likelihood estimation)have been developed. only one research(Swaminathan and Giffort.1980)used Bayesian approach for an estimation of parameters. The proposed method in this study differed from Swaminathan et al's method:(1)our method was a full realization of the general Bayesian hierarchical model(Lindley and Smith, 1972), that is, our method employed exchangeable prior for all parameters; and(2)our method made use of an efficient quasi-Newton method to obtain the mode of the posterior distribution. We used the mode of joint posterior distribution of the parameters as Baysian estimates. Prior distributions employed at the first stage were as follows; 1)standard normal distribution N(0, 1)for ability parameters θ, 2)chi distribution or truncated normal distribution N(μ_a, σ^2_a)for discrimination parameters a, 3)normal distribution N(μ_b, σ_b^2)for difficulty parameters b, 4)truncated normal distribution N(μ_c, σ_c^2)for pseudo chance-level parameters c. At the second stage, aporopriate distribution were assumed for hyper parameters(e.g.inverse chi-square distribution for σ_a^2, σ_b^2 or σ_c^2). Two programmes by the name of SIMDATA and BAYIRT were made. SIMDATA provided artificial data based on the random numbers generated according to three parameter logistic models. BAYIRT was to obtain model estimates of parameters by making use of the result that the model estimate of θ i.e.θ-value of the mode of the joint distribution corresponded to the mode of the conditional distribution of θ evaluated at the modal estimates of a, b and c, and vice versa. BAYIRT also provided MLE's if desired. We obtained Bayesian estimates and MLE's by applying BAYIRT to two kinds of artificial data, and another set of MLE's by applying a well-reputed LOGIST programme, whose algorithm was somewhat different from ours. In order to evaluate accuracy of the estimates, we calculated mean squared errors(MSE)of the estimates, and correlation coefficients between estimates and true values. In terms of MSE, Bayesian estimates were superior to MLE's. In terms of correlation coefficients. Bayesian estimates of parameters θ were superior to MLE's again, and Bayesian estimates of parameters a, b and c proved generally superior to MLE's.(An exception was that LOGIST produces slightly better estimates for parameters a and b. But the estimaters of parameters c were very poor and LOGIST failed to produce good estimates of parameters θ. Our overall judgment was that Bayesian estimation procedure could yield better estimates.)Among Bayesian procedures, the estimates with normal distribution as prior of parameters a were found to be generally better than those with chi distribution as prior. Added accuracy of the Bayesian estimates seemed to be due to the collateral information gathered through the use of exchangeable priors.