A Note on Latent Traits Estimates under IRT Models with Missingness

Abstract

Missingness due to not-reached items and omitted items has received much attention in the recent psychometric literature. Such missingness, if not handled properly, would lead to biased parameter estimation, as well as inaccurate inference of examinees, and further erode the validity of the test. This paper reviews some commonly used IRT based models allowing missingness, followed by three popular examinee scoring methods, including maximum likelihood estimation, maximum a posteriori, and expected a posteriori. Simulation studies were conducted to compare these examinee scoring methods across these commonly used models in the presence of missingness. Results showed that all the methods could infer examinees' ability accurately when the missingness is ignorable. If the missingness is nonignorable, incorporating those missing responses would improve the precision in estimating abilities for examinees with missingness, especially when the test length is short. In terms of examinee scoring methods, expected a posteriori method performed better for evaluating latent traits under models allowing missingness. An empirical study based on the PISA 2015 Science Test was further performed.