University of Liège, Belgium.
Br J Math Stat Psychol. 2012 May;65(2):302-21. doi: 10.1111/j.2044-8317.2011.02025.x. Epub 2011 Aug 19.
Most methods for detecting differential item functioning (DIF) are suitable when the sample sizes are sufficiently large to validate the null statistical distributions. There is no guarantee, however, that they will still perform adequately when there are few respondents in the focal group or in both the reference and the focal group. Angoff's delta plot is a potentially useful alternative for small-sample DIF investigation, but it suffers from an improper DIF flagging criterion. The purpose of this paper is to improve this classification rule under mild statistical assumptions. This improvement yields a modified delta plot with an adjusted DIF flagging criterion for small samples. A simulation study was conducted to compare the modified delta plot with both the classical delta plot approach and the Mantel-Haenszel method. It is concluded that the modified delta plot is consistently less conservative and more powerful than the usual delta plot, and is also less conservative and more powerful than the Mantel-Haenszel method as long as at least one group of respondents is small.
大多数用于检测差异项目功能（DIF）的方法在样本量足够大以验证零统计分布时是适用的。然而，当焦点组或参考组和焦点组中的应答者很少时，不能保证它们仍然能够充分发挥作用。Angoff 的德尔塔图是一种用于小样本 DIF 调查的潜在有用替代方法，但它存在不当的 DIF 标记标准。本文的目的是在温和的统计假设下改进这种分类规则。这种改进产生了一种带有调整后的 DIF 标记标准的改进的德尔塔图，适用于小样本。进行了一项模拟研究，以比较改进后的德尔塔图与经典的德尔塔图方法和 Mantel-Haenszel 方法。结论是，改进后的德尔塔图比通常的德尔塔图更不保守且更有效，并且只要至少有一组应答者较小，它就比 Mantel-Haenszel 方法更不保守且更有效。
