Markov Chain Monte Carlo Estimation of Item Parameters for the Generalized Graded Unfolding Model
Document Type
Article
Publication Date
5-2006
Keywords
ideal point models, IRT, MCMC, MML, estimation, personality
Digital Object Identifier (DOI)
https://doi.org/10.1177%2F0146621605282772
Abstract
The authors present a Markov Chain Monte Carlo (MCMC) parameter estimation procedure for the generalized graded unfolding model (GGUM) and compare it to the marginal maximum likelihood (MML) approach implemented in the GGUM2000 computer program, using simulated and real personality data. In the simulation study, test length, number of response options, and sample size were manipulated. Results indicate that the two methods are comparable in terms of item parameter estimation accuracy. Although the MML estimates exhibit slightly smaller bias than MCMC estimates, they also show greater variability, which results in larger root mean squared errors. Of the two methods, only MCMC provides reasonable standard error estimates for all items.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Applied Psychological Measurement, v. 30, issue 3, p. 1 – 17
Scholar Commons Citation
de la Torre, Jimmy; Stark, Steve; and Chernyshenko, Oleksandr S., "Markov Chain Monte Carlo Estimation of Item Parameters for the Generalized Graded Unfolding Model" (2006). Psychology Faculty Publications. 1967.
https://digitalcommons.usf.edu/psy_facpub/1967
