A Comparison of Two MCMC Algorithms for Estimating the 2PL IRT Models

<p>The fully Bayesian estimation via the use of Markov chain Monte Carlo (MCMC) techniques has become popular for estimating item response theory (IRT) models. The current development of MCMC includes two major algorithms: Gibbs sampling and the No-U-Turn sampler (NUTS). While the former has been used with fitting various IRT models, the latter is relatively new, calling for the research to compare it with other algorithms. The purpose of the present study is to evaluate the performances of these two emerging MCMC algorithms in estimating two two-parameter logistic (2PL) IRT models, namely, the 2PL unidimensional model and the 2PL multi-unidimensional model under various test situations. Through investigating the accuracy and bias in estimating the model parameters given different test lengths, sample sizes, prior specifications, and/or correlations for these models, the key motivation is to provide researchers and practitioners with general guidelines when it comes to estimating a UIRT model and a multi-unidimensional IRT model. The results from the present study suggest that NUTS is equally effective as Gibbs sampling at parameter estimation under most conditions for the 2PL IRT models. Findings also shed light on the use of the two MCMC algorithms with more complex IRT models.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Open Access Dissertation

Discipline thesis:degree_discipline

Quantitative Methods

Year dc:date.available

2017

Author dc:creator

• Chang, Meng-I

Contributors dc:contributor

• Sheng, Yanyan

• 2PL IRT models
• Fully Bayesian estimation
• Gibbs sampling
• MCMC
• NUTS

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/1446

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-2450