The sources of differential item functioning (DIF) items are usually identified through a qualitative content review by a panel of experts. However, the differential functioning for some DIF items might have been caused by reasons outside of the experts’ experiences, leading to the sources for these DIF items possibly being misidentified. Quantitative methods can help to provide useful information, such as the DIF status and the number of sources of the DIF, which in turn help the item review and revision process to be more efficient and precise. However, the current quantitative methods assume all possible sources should be known in advance and collected to accompany the item response data, which is not always the case in reality. To this end, an exploratory strategy, combined with the MIMIC (multiple-indicator multiple-cause) method, that can be used to identify and name new sources of DIF is proposed in this study. The performance of this strategy was investigated through simulation. The results showed that when a set of DIF-free items can be correctly identified to define the main dimension, the proposed exploratory MIMIC method can accurately recover a number of possible sources of DIF and the items that belong to each. A real data analysis was also implemented to demonstrate how this strategy can be used in reality. The results and findings of this study are further discussed.

差异项目功能 (DIF) 项目的来源通常通过专家小组的定性内容审查来确定。但是，某些 DIF 项目的功能差异可能是由专家经验之外的原因造成的，导致这些 DIF 项目的来源可能被错误识别。定量方法可以帮助提供有用的信息，例如 DIF 状态和 DIF 的来源数量，这反过来又有助于项目审查和修订过程更加高效和精确。然而，当前的定量方法假设所有可能的来源都应该提前知道并收集以伴随项目响应数据，这在现实中并不总是如此。为此，一种探索性的策略，结合MIMIC（多指标多因）方法，本研究提出了可用于识别和命名 DIF 的新来源。通过仿真研究了该策略的性能。结果表明，当可以正确识别一组无 DIF 项以定义主要维度时，所提出的探索性 MIMIC 方法可以准确地恢复多个可能的 DIF 源以及属于每个项的项。还实施了真实的数据分析，以展示如何在现实中使用该策略。进一步讨论了这项研究的结果和发现。提出的探索性 MIMIC 方法可以准确地恢复 DIF 的多个可能来源以及属于每个来源的项目。还实施了真实的数据分析，以展示如何在现实中使用该策略。进一步讨论了这项研究的结果和发现。提出的探索性 MIMIC 方法可以准确地恢复 DIF 的多个可能来源以及属于每个来源的项目。还实施了真实的数据分析，以展示如何在现实中使用该策略。进一步讨论了这项研究的结果和发现。