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R. D. Bock and M. Aitkin, “Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM Algorithm,” Psychometrika, Vol. 46, No. 4, 1981, pp. 443-459. doi:10.1007/BF02293801

has been cited by the following article:

• TITLE: Parallel Computing with a Bayesian Item Response Model

  AUTHORS: Kyriakos Patsias, Mona Rahimi, Yanyan Sheng, Shahram Rahimi

  KEYWORDS: Gibbs Sampling; High Performance Computing; Message Passing Interface; Two-Parameter IRT Model

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.2 No.2, June 21, 2012

  ABSTRACT: Item response theory (IRT) is a modern test theory that has been used in various aspects of educational and psychological measurement. The fully Bayesian approach shows promise for estimating IRT models. Given that it is computation- ally expensive, the procedure is limited in practical applications. It is hence important to seek ways to reduce the execution time. A suitable solution is the use of high performance computing. This study focuses on the fully Bayesian algorithm for a conventional IRT model so that it can be implemented on a high performance parallel machine. Empirical results suggest that this parallel version of the algorithm achieves a considerable speedup and thus reduces the execution time considerably.