A Residual‐Based Differential Item Functioning Detection Framework in Item Response Theory
A Residual‐Based Differential Item Functioning Detection Framework in Item Response Theory
复制标题
项目响应理论中基于残差的差异项目功能检测框架
DOI：
10.1111/jedm.12313
复制
发表时间：
2022
影响因子：
1.3
通讯作者：
K. T. Han
中科院分区：
文献类型：
--
作者：
Hwanggyu Lim;Edison M. Choe;K. T. Han
Differential item functioning (DIF) of test items should be evaluated using practical methods that can produce accurate and useful results. Among a plethora of DIF detection techniques, we introduce the newResidual DIF(RDIF) framework, which stands out for its accessibility without sacrificing efficacy. This framework consists of three item response theory (IRT) residual statistics: RDIFR$RDI{F_R}$, RDIFS$RDI{F_S}$, and RDIFRS$RDI{F_{RS}}$. We conducted a simulation study with a 40‐item test to assess the performance of RDIF in comparison with the Mantel‐Haenszel, logistic regression, and IRT‐based likelihood ratio test methods. Even when analyzing small sample sizes, the results revealed RDIFRS$RDI{F_{RS}}$ to be the most robust DIF detection statistic with strict control of Type I error across all simulated conditions when paired with the purification procedure. Also, RDIFR$RDI{F_R}$ and RDIFS$RDI{F_S}$ proved to be powerful indicators of uniform and nonuniform DIF, respectively. Therefore, RDIFRS$RDI{F_{RS}}$ should serve as the primary flagging criterion, whereas RDIFR$RDI{F_R}$ and RDIFS$RDI{F_S}$ best serve as indicators of DIF type. An empirical DIF study also showed that the RDIF framework could perform satisfactorily with real data from a large‐scale assessment. Overall, the RDIF framework demonstrated its potential as a new standard for IRT‐based DIF detection methodology.
Differential item functioning (DIF) of test items should be evaluated using practical methods that can produce accurate and useful results. Among a plethora of DIF detection techniques, we introduce the newResidual DIF(RDIF) framework, which stands out for its accessibility without sacrificing efficacy. This framework consists of three item response theory (IRT) residual statistics: RDIFR$RDI{F_R}$, RDIFS$RDI{F_S}$, and RDIFRS$RDI{F_{RS}}$. We conducted a simulation study with a 40‐item test to assess the performance of RDIF in comparison with the Mantel‐Haenszel, logistic regression, and IRT‐based likelihood ratio test methods. Even when analyzing small sample sizes, the results revealed RDIFRS$RDI{F_{RS}}$ to be the most robust DIF detection statistic with strict control of Type I error across all simulated conditions when paired with the purification procedure. Also, RDIFR$RDI{F_R}$ and RDIFS$RDI{F_S}$ proved to be powerful indicators of uniform and nonuniform DIF, respectively. Therefore, RDIFRS$RDI{F_{RS}}$ should serve as the primary flagging criterion, whereas RDIFR$RDI{F_R}$ and RDIFS$RDI{F_S}$ best serve as indicators of DIF type. An empirical DIF study also showed that the RDIF framework could perform satisfactorily with real data from a large‐scale assessment. Overall, the RDIF framework demonstrated its potential as a new standard for IRT‐based DIF detection methodology.