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Performance of the S-chi(2) Statistic for the Multidimensional Graded Response Model

机译：多维评分响应模型的S-CHI（2）统计的性能

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摘要

S-chi(2) is a popular item fit index that is available in commercial software packages such asflexMIRT. However, no research has systematically examined the performance of S-chi(2) for detecting item misfit within the context of the multidimensional graded response model (MGRM). The primary goal of this study was to evaluate the performance of S-chi(2) under two practical misfit scenarios: first, all items are misfitting due to model misspecification, and second, a small subset of items violate the underlying assumptions of the MGRM. Simulation studies showed that caution should be exercised when reporting item fit results of polytomous items using S-chi(2) within the context of the MGRM, because of its inflated false positive rates (FPRs), especially with a small sample size and a long test. S-chi(2) performed well when detecting overall model misfit as well as item misfit for a small subset of items when the ordinality assumption was violated. However, under a number of conditions of model misspecification or items violating the homogeneous discrimination assumption, even though true positive rates (TPRs) of S-chi(2) were high when a small sample size was coupled with a long test, the inflated FPRs were generally directly related to increasing TPRs. There was also a suggestion that performance of S-chi(2) was affected by the magnitude of misfit within an item. There was no evidence that FPRs for fitting items were exacerbated by the presence of a small percentage of misfitting items among them.
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