In recent decades, multidimensional forced-choice (MFC) tests have gained widespread popularity in organizational settings due to their effectiveness in reducing response biases. Detecting differential item functioning (DIF) is crucial in developing MFC tests, as it relates to test fairness and validity. However, existing methods appear insufficient for detecting DIF induced by the interaction between multiple covariates. Furthermore, for multi-category, ordered or continuous covariates, existing approaches often dichotomize them using a-priori cutoffs, commonly using the median of the covariates. This may lead to information loss and reduced power in detecting MFC DIF. To address these limitations, we propose a method to identify both main effect DIF and interactive DIF. This method can automatically search for the optimal cutoffs for ordered or continuous covariates without pre-defined cutoffs. We introduce the rationale behind the proposed method and evaluate its performance through three Monte Carlo simulation studies. Results demonstrate that the proposed method effectively identifies various DIF forms in MFC tests, thereby increasing detection power. Finally, we provide an empirical application to illustrate the practical applicability of the proposed method.

中文翻译：

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多维强制选择评估中检测主效应和交互式 DIF 的框架

近几十年来，多维强制选择（MFC）测试因其在减少反应偏差方面的有效性而在组织环境中广泛流行。检测差异项目功能 (DIF) 对于开发 MFC 测试至关重要，因为它关系到测试的公平性和有效性。然而，现有方法似乎不足以检测多个协变量之间相互作用引起的 DIF。此外，对于多类别、有序或连续协变量，现有方法通常使用先验截止值（通常使用协变量的中位数）将它们二分。这可能会导致信息丢失并降低检测 MFC DIF 的能力。为了解决这些限制，我们提出了一种识别主效应 DIF 和交互 DIF 的方法。该方法可以自动搜索有序或连续协变量的最佳截止值，而无需预先定义截止值。我们介绍了所提出方法背后的基本原理，并通过三个蒙特卡罗模拟研究评估其性能。结果表明，该方法可以有效识别 MFC 测试中的各种 DIF 形式，从而提高检测能力。最后，我们提供了一个实证应用来说明所提出方法的实际适用性。