  The generalized partial credit model (GPCM) is a popular polytomous IRT model
that has been widely used in large-scale educational surveys and health care
services. Same as other IRT models, GPCM can be estimated via marginal maximum
likelihood estimation (MMLE) and Markov chain Monte Carlo (MCMC) methods. While
studies comparing MMLE and MCMC as estimation methods for other polytomous IRT
models such as the nominal response model and the graded response model exist
in literature, no studies have compared how well MMLE and MCMC recover the
model parameters of GPCM. In the current study, a comprehensive simulation
study was conducted to compare parameter recovery of GPCM via MMLE and MCMC.
The manipulated factors included latent distribution, sample size, and test
length, and parameter recovery was evaluated with bias and root mean square
error. It was found that there were no statistically significant differences in
recovery of the item location and ability parameters between MMLE and MCMC; for
the item discrimination parameter, MCMC had less bias in parameter recovery
than MMLE under both normal and uniform latent distributions, and MMLE
outperformed MCMC with less bias in parameter recovery under skewed latent
distributions. A real dataset from a high-stakes test was used to demonstrate
the estimation of GPCM with MMLE and MCMC.
  Keywords: polytomous IRT, GPCM, MCMC, MMLE, parameter recovery.
