A model-based standardization approach that separates true bias/DIF from group ability differences and detects test bias/DTF as well as item bias/DIF
A model-based standardization approach that separates true bias/DIF from group ability differences and detects test bias/DTF as well as item bias/DIF
复制标题
基于模型的标准化方法，将真实偏差/DIF 与群体能力差异区分开来，并检测测试偏差/DTF 以及项目偏差/DIF
DOI：
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
W. Stout
中科院分区：
文献类型：
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作者：
R. Shealy;W. Stout
提出了基于多维 IRT 偏差建模方法的标准化指数的基于模型的修改 (SIBTEST)，该方法可同时检测和估计多个项目的 DIF 或项目偏差。提出了 DIF 和偏差之间的区别。 SIBTEST 可检测偏差/DIF，而不会因群体目标能力差异而导致通常的 1 类错误膨胀。在模拟中，对于单项案例，SIBTEST 的表现与 Mantel-Haenszel 相当。 SIBTEST 在测试分数水平上调查多个项目的偏差/DIF（称为差异测试功能的多项目 DIF：DTF），从而允许研究测试偏差/DIF，特别是偏差/DIF 放大或取消以及偏差/DIF 的认知基础。
A model-based modification (SIBTEST) of the standardization index based upon a multidimensional IRT bias modeling approach is presented that detects and estimates DIF or item bias simultaneously for several items. A distinction between DIF and bias is proposed. SIBTEST detects bias/DIF without the usual Type 1 error inflation due to group target ability differences. In simulations, SIBTEST performs comparably to Mantel-Haenszel for the one item case. SIBTEST investigates bias/DIF for several items at the test score level (multiple item DIF called differential test functioning: DTF), thereby allowing the study of test bias/DIF, in particular bias/DIF amplification or cancellation and the cognitive bases for bias/DIF.