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A Residual‐Based Differential Item Functioning Detection Framework in Item Response Theory.

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Title: A Residual‐Based Differential Item Functioning Detection Framework in Item Response Theory.
Authors: Lim, Hwanggyu1 (AUTHOR), Choe, Edison M.1 (AUTHOR), Han, Kyung T.1 (AUTHOR)
Source: Journal of Educational Measurement. Mar2022, Vol. 59 Issue 1, p80-104. 25p.
Subject Terms: *Item response theory, False positive error, Likelihood ratio tests, Logistic regression analysis
Abstract: 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 new Residual 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: A Residual‐Based Differential Item Functioning Detection Framework in Item Response Theory.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Mar2022, Vol. 59 Issue 1, p80-104. 25p.
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  Data: *<searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br /><searchLink fieldCode="DE" term="%22False+positive+error%22">False positive error</searchLink><br /><searchLink fieldCode="DE" term="%22Likelihood+ratio+tests%22">Likelihood ratio tests</searchLink><br /><searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink>
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  Data: 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 new Residual 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Educational Measurement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/jedm.12313
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        Text: English
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      – SubjectFull: Likelihood ratio tests
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      – SubjectFull: Logistic regression analysis
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              Text: Mar2022
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