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The generalized partial credit model (GPCM) is a popular polytomous IRT modelthat has been widely used in large-scale educational surveys and health careservices. Same as other IRT models, GPCM can be estimated via marginal maximumlikelihood estimation (MMLE) and Markov chain Monte Carlo (MCMC) methods. Whilestudies comparing MMLE and MCMC as estimation methods for other polytomous IRTmodels such as the nominal response model and the graded response model existin literature, no studies have compared how well MMLE and MCMC recover themodel parameters of GPCM. In the current study, a comprehensive simulationstudy was conducted to compare parameter recovery of GPCM via MMLE and MCMC.The manipulated factors included latent distribution, sample size, and testlength, and parameter recovery was evaluated with bias and root mean squareerror. It was found that there were no statistically significant differences inrecovery of the item location and ability parameters between MMLE and MCMC; forthe item discrimination parameter, MCMC had less bias in parameter recoverythan MMLE under both normal and uniform latent distributions, and MMLEoutperformed MCMC with less bias in parameter recovery under skewed latentdistributions. A real dataset from a high-stakes test was used to demonstratethe estimation of GPCM with MMLE and MCMC.  Keywords: polytomous IRT, GPCM, MCMC, MMLE, parameter recovery.

Parameter Recovery with Marginal Maximum Likelihood and Markov Chain Monte Carlo Estimation for the Generalized Partial Credit Model