Estimating Item Response Theory Models Using Markov Chain Monte Carlo Methods

2007 ◽  
Vol 26 (4) ◽  
pp. 38-51 ◽  
Author(s):  
Jee-Seon Kim ◽  
Daniel M. Bolt
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Item Response Theory ◽  
Item Response ◽  
Monte Carlo Methods ◽  
Response Theory ◽  
Item Response Theory Models

scholarly journals The Impact of Markov Chain Convergence on Estimation of Mixture IRT Model Parameters

2020 ◽  
Vol 80 (5) ◽  
pp. 975-994
Author(s):  
Yoonsun Jang ◽  
Allan S. Cohen
Keyword(s):  
Markov Chain ◽  
Item Response Theory ◽  
Item Response ◽  
Monte Carlo Algorithm ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Response Theory ◽  
Irt Model ◽  
Mixture Irt ◽  
Item Response Theory Models

A nonconverged Markov chain can potentially lead to invalid inferences about model parameters. The purpose of this study was to assess the effect of a nonconverged Markov chain on the estimation of parameters for mixture item response theory models using a Markov chain Monte Carlo algorithm. A simulation study was conducted to investigate the accuracy of model parameters estimated with different degree of convergence. Results indicated the accuracy of the estimated model parameters for the mixture item response theory models decreased as the number of iterations of the Markov chain decreased. In particular, increasing the number of burn-in iterations resulted in more accurate estimation of mixture IRT model parameters. In addition, the different methods for monitoring convergence of a Markov chain resulted in different degrees of convergence despite almost identical accuracy of estimation.

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