Dongho Shin

Date of Award

Spring 2019

Document Type

Open Access Thesis



First Advisor

Brian Habing


Two widely used algorithms for estimating item response theory (IRT) parameters are Markov chain Monte Carlo (MCMC) and the EM algorithm. In general, the MCMC algorithm has advantages over the EM algorithm - for example, the MCMC algorithm allows one to estimate the desired posterior distribution and also works more straightforwardly with complex IRT models. This ease of use, allows one to implement the MCMC algorithm without carefully consideration. Previous studies, Hendrix (2011) and Lee (2016), noted that the estimated standard errors from the MCMC algorithm are larger than those from the EM algorithm. Therefore, this study investigate the reason behind the larger standard error problem more in depth. In addition, it explores IRT parameter estimation in R including, including coding the MCMC method and using the mirt package for implementing the EM algorithm.