多元多维两两比较项目中差异陈述功能的评估。

Journal of applied measurement Pub Date : 2020-01-01
Xue-Lan Qiu
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引用次数: 0

摘要

多维两两比较(MPC)项目被广泛应用于职业兴趣、价值和个性的评估,以避免教育部门的反应偏差。实际上,MPC项目中的语句对于不同的组可能具有不同的实用程序,这被称为差分语句功能(DSF)。关于DSF评价的研究很少。本研究基于MPC题项的Rasch模型,采用三种方法检测多同构MPC题项的DSF:等平均效用法(EMU)、全其他陈述法(AOS)和不变陈述法(CS)。通过仿真研究,对所提方法的参数恢复效果和性能进行了评价。结果表明,当测试包含DSF语句时,选择一个或多个无DSF语句作为锚点的CS方法将产生准确的估计,并在DSF评估中表现良好。最后给出了职业兴趣评价的实证实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Differential Statement Functioning in Polytomous Multidimensional Pairwise Comparison Items.

Multidimensional pairwise comparison (MPC) items have been widely used to assess career interest, value and personality to avoid response bias in educational sectors. In reality, a statement in an MPC item may have different utilities for different groups, which is referred to as differential statement functioning (DSF). Few studies have been investigated DSF assessment. Based on a Rasch model for MPC items, this study adapts three methods to detect DSF for polytomous MPC items: the equal-mean-utility (EMU) method, the all-other-statement (AOS) method and the constant-statement (CS) method. Simulation study was conducted to evaluate the recovery of parameters as well as the performance of the proposed methods. Results showed that when the test contains DSF statement(s), the CS method where one or more DSF-free statements are chosen as an anchor will yield accurate estimates and perform well for DSF assessment. An empirical example of career interest assessment was provided. .

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