The Effects of Rater Severity and Rater Distribution on Examinees’ Ability Estimation for Constructed-Response Items MCMC IRT
- Author(s):
- Wang, Zhen; Yao, Lihua
- Publication Year:
- 2013
- Report Number:
- RR-13-23
- Source:
- ETS Research Report
- Document Type:
- Report
- Page Count:
- 22
- Subject/Key Words:
- Item Response Theory (IRT), Rater Severity, IRT-Based Rater Model, Markov Chain Monte Carlo (MCMC), Rater Distribution
Abstract
We also compared Yao’s rater model with Muraki’s rater effect model (1993) in terms of ability estimation accuracy and rater parameter recovery. The estimation results from Yao’s rater model using Markov chain Monte Carlo (MCMC) were better than those from Muraki’s rater effect model using marginal maximum likelihood.
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- http://dx.doi.org/10.1002/j.2333-8504.2013.tb02330.x