The Rasch model is one of the most prominent item response models.
In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.
Original languageEnglish
Title of host publicationBook of short papers : SIS 2021
EditorsCira Perna, Nicola Salvati, Francesco Schirripa Spagnolo
Publication date06.2021
ISBN (Electronic)9788891927361
Publication statusPublished - 06.2021
No renderer: handleNetPortal,dk.atira.pure.api.shared.model.researchoutput.ContributionToBookAnthology

ID: 1672728