C. H. Chang and B. B. Reeve, “Item Response Theory and Its Applications to Patient-Reported Outcomes Measurement,” Evaluation & the Health Professions, Vol. 28, No. 3, 2005, pp. 264-282. doi:10.1177/0163278705278275

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.