Abstract
A novel approach to item-fit analysis based on an asymptotic test is proposed. The new test statistic, , compares pseudo-observed and expected item mean scores over a set of ability bins. The item mean scores are computed as weighted means with weights based on test-takers' a posteriori density of ability within the bin. This article explores the properties of in case of dichotomously scored items for unidimensional IRT models. Monte Carlo experiments were conducted to analyze the performance of . Type I error of was acceptably close to the nominal level and it had greater power than Orlando and Thissen's . Under some conditions, power of also exceeded the one reported for the computationally more demanding Stone's .
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