Characteristics of MML/EAP parameter estimates in the generalized graded unfolding model

The generalized graded unfolding model (GGUM) is a very general parametric, unidimen-sional item response theory model for unfolding either binary or polytomous responses to test items. Roberts, Donoghue, and Laughlin have described a marginal maximum likelihood (MML) approach to estimate item parameters in the GGUM along with an expected a posteriori (EAP) method to estimate person parameters. This article examines the data demands required to produce accurate parameter estimates using these techniques under ideal condi-tions. It also examines the robustness of parameter estimates under nonideal conditions, in which there are inconsistencies between the prior distribution of person parameters that must be specified whe