title: On the asymptotic standard error of a class of robust estimators of ability in dichotomous item response models.
abstract: In item response theory, the classical estimators of ability are highly sensitive to response disturbances and can return strongly biased estimates of the true underlying ability level. Robust methods were introduced to lessen the impact of such aberrant responses on the estimation process. The computation of asymptotic (i.e., large-sample) standard errors (ASE) for these robust estimators, however, has not yet been fully considered. This paper focuses on a broad class of robust ability estimators, defined by an appropriate selection of the weight function and the residual measure, for which the ASE is derived from the theory of estimating equations. The maximum likelihood (ML) and the robust estimators, together with their estimated ASEs, are then compared in a simulation study by generating random guessing disturbances. It is concluded that both the estimators and their ASE perform similarly in the absence of random guessing, while the robust estimator and its estimated ASE are less biased and outperform their ML counterparts in the presence of random guessing with large impact on the item response process.
authors.affiliation: Department of Education, University of Liège, Belgium; Department of Psychology, Katholieke Universiteit Leuven, Belgium.
authors.forename: David
authors.initials: D
authors.lastname: Magis
mesh_terms: D001076:Aptitude; D006801:Humans; D016013:Likelihood Functions; D008960:Models, Psychological; D015233:Models, Statistical; D011581:Psychological Tests / Q000706:statistics & numerical data*; D011589:Psychology, Experimental / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D012044:Regression Analysis; D015203:Reproducibility of Results; D013223:Statistics as Topic
keywords: ability estimation; estimating equations; item response theory; robust estimation; standard error