Combining Item Purification and Multiple Comparison Adjustment Methods in Detection of Differential Item Functioning

0572469 - ÚI 2025 eng J - Journal Article
Hladká, Adéla - Martinková, Patrícia - Magis, D.
Combining Item Purification and Multiple Comparison Adjustment Methods in Detection of Differential Item Functioning.
Multivariate Behavioral Research. Roč. 59, č. 1 (2024), s. 46-61. ISSN 0027-3171. E-ISSN 1532-7906
R&D Projects: GA ČR(CZ) GA21-03658S
Institutional support: RVO:67985807
Keywords : differential item functioning * item purification * multiple comparison adjustments
OECD category: Statistics and probability
Impact factor: 3.8, year: 2022
Method of publishing: Limited access
https://dx.doi.org/10.1080/00273171.2023.2205393

Many of the differential item functioning (DIF) detection methods rely on a principle of testing for DIF item by item, while considering the rest of the items or at least some of them being DIF-free. Computational algorithms of these DIF detection methods involve the selection of DIF-free items in an iterative procedure called item purification. Another aspect is the need to correct for multiple comparisons, which can be done with a number of existing multiple comparison adjustment methods. In this article, we demonstrate that implementation of these two controlling procedures together may have an impact on which items are detected as DIF items. We propose an iterative algorithm combining item purification and adjustment for multiple comparisons. Pleasant properties of the newly proposed algorithm are shown with a simulation study. The method is demonstrated on a real data example.
Permanent Link: https://hdl.handle.net/11104/0343130