A Comparison of Maximum Likelihood Estimation and Expected a Posteriori Estimation in CAT Using the Partial Credit Model

Abstract

A simulation study was conducted to investigate the application of expected a posteriori (EAP) trait estimation in computerized adaptive tests (CAT) based on the partial credit model and compare it with maximum likelihood trait estimation (MLE). The performance of EAP was evaluated under different conditions: the number of quadrature points (10, 20,40, and 80) and the type of prior distribution (normal and uniform). The relative performance of MLE and the EAP estimation methods was assessed under two distributional forms of the latent trait (normal and negatively skewed). Results showed that, regardless of the latent trait distribution, MLE and EAP with a normal prior or a uniform prior using either 20, 40, or 80 quadrature points provided relatively accurate estimation in CAT based on the partial credit model. Also, increasing the number of quadrature points from 20 to 80 did not increase the accuracy of EAP estimation.