|Title||A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Response Theory Models.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Liu, Yang, Guanyu Hu, Lei Cao, Xiaojing Wang, and Ming-Hui Chen|
|Journal||J Korean Stat Soc|
|Date Published||2019 Dec|
Nowadays, Bayesian methods are routinely used for estimating parameters of item response theory (IRT) models. However, the marginal likelihoods are still rarely used for comparing IRT models due to their complexity and a relatively high dimension of the model parameters. In this paper, we review Monte Carlo (MC) methods developed in the literature in recent years and provide a detailed development of how these methods are applied to the IRT models. In particular, we focus on the "best possible" implementation of these MC methods for the IRT models. These MC methods are used to compute the marginal likelihoods under the one-parameter IRT model with the logistic link (1PL model) and the two-parameter logistic IRT model (2PL model) for a real English Examination dataset. We further use the widely applicable information criterion (WAIC) and deviance information criterion (DIC) to compare the 1PL model and the 2PL model. The 2PL model is favored by all of these three Bayesian model comparison criteria for the English Examination data.
|Alternate Journal||J Korean Stat Soc|
|Original Publication||A comparison of Monte Carlo methods for computing marginal likelihoods of item response theory models.|
|PubMed Central ID||PMC6953617|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA074015 / CA / NCI NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
A Comparison of Monte Carlo Methods for Computing Marginal Likelihoods of Item Response Theory Models.