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Detecting differential item functioning with multiple causes: A comparison of three methods.

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Title: Detecting differential item functioning with multiple causes: A comparison of three methods.
Authors: Liu, Xiaowen (AUTHOR)
Source: International Journal of Testing. Jan-Mar2024, Vol. 24 Issue 1, p53-79. 27p.
Subjects: Multiple comparisons (Statistics), Logistic regression analysis, Multidimensional databases
Abstract: Differential item functioning (DIF) often arises from multiple sources. Within the context of multidimensional item response theory, this study examined DIF items with varying secondary dimensions using the three DIF methods: SIBTEST, Mantel-Haenszel, and logistic regression. The effect of the number of secondary dimensions on DIF detection rates was investigated with several test conditions such as the percentage of DIF items, item loadings, the correlation between primary and secondary dimensions, and DIF magnitude. Results indicate that both the Mantel–Haenszel and logistic regression procedures performed effectively in detecting DIF when multiple secondary dimensions were present. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Testing is the property of Taylor & Francis Ltd 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: Psychology and Behavioral Sciences Collection
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  Data: Detecting differential item functioning with multiple causes: A comparison of three methods.
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  Data: Differential item functioning (DIF) often arises from multiple sources. Within the context of multidimensional item response theory, this study examined DIF items with varying secondary dimensions using the three DIF methods: SIBTEST, Mantel-Haenszel, and logistic regression. The effect of the number of secondary dimensions on DIF detection rates was investigated with several test conditions such as the percentage of DIF items, item loadings, the correlation between primary and secondary dimensions, and DIF magnitude. Results indicate that both the Mantel–Haenszel and logistic regression procedures performed effectively in detecting DIF when multiple secondary dimensions were present. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Testing is the property of Taylor & Francis Ltd 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: 10.1080/15305058.2023.2286381
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        Text: English
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        Type: general
      – SubjectFull: Logistic regression analysis
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      – SubjectFull: Multidimensional databases
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              Text: Jan-Mar2024
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              Y: 2024
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