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Angoff's delta method revisited: Improving DIF detection under small samples.

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Title: Angoff's delta method revisited: Improving DIF detection under small samples.
Authors: Magis, David (AUTHOR), Facon, Bruno (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. May2012, Vol. 65 Issue 2, p302-321. 20p. 3 Graphs.
Subjects: Logistic regression analysis, Sample size (Statistics), Distribution (Probability theory), Comparative studies, Qualitative research, Simulation methods & models
Abstract: Most methods for detecting differential item functioning (DIF) are suitable when the sample sizes are sufficiently large to validate the null statistical distributions. There is no guarantee, however, that they will still perform adequately when there are few respondents in the focal group or in both the reference and the focal group. Angoff's delta plot is a potentially useful alternative for small-sample DIF investigation, but it suffers from an improper DIF flagging criterion. The purpose of this paper is to improve this classification rule under mild statistical assumptions. This improvement yields a modified delta plot with an adjusted DIF flagging criterion for small samples. A simulation study was conducted to compare the modified delta plot with both the classical delta plot approach and the Mantel-Haenszel method. It is concluded that the modified delta plot is consistently less conservative and more powerful than the usual delta plot, and is also less conservative and more powerful than the Mantel-Haenszel method as long as at least one group of respondents is small. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Mathematical & Statistical Psychology 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: Psychology and Behavioral Sciences Collection
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  Data: Angoff's delta method revisited: Improving DIF detection under small samples.
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  Data: <searchLink fieldCode="AR" term="%22Magis%2C+David%22">Magis, David</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Facon%2C+Bruno%22">Facon, Bruno</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Mathematical+%26+Statistical+Psychology%22">British Journal of Mathematical & Statistical Psychology</searchLink>. May2012, Vol. 65 Issue 2, p302-321. 20p. 3 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Sample+size+%28Statistics%29%22">Sample size (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Qualitative+research%22">Qualitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink>
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  Data: Most methods for detecting differential item functioning (DIF) are suitable when the sample sizes are sufficiently large to validate the null statistical distributions. There is no guarantee, however, that they will still perform adequately when there are few respondents in the focal group or in both the reference and the focal group. Angoff's delta plot is a potentially useful alternative for small-sample DIF investigation, but it suffers from an improper DIF flagging criterion. The purpose of this paper is to improve this classification rule under mild statistical assumptions. This improvement yields a modified delta plot with an adjusted DIF flagging criterion for small samples. A simulation study was conducted to compare the modified delta plot with both the classical delta plot approach and the Mantel-Haenszel method. It is concluded that the modified delta plot is consistently less conservative and more powerful than the usual delta plot, and is also less conservative and more powerful than the Mantel-Haenszel method as long as at least one group of respondents is small. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of British Journal of Mathematical & Statistical Psychology 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/j.2044-8317.2011.02025.x
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        Text: English
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      – SubjectFull: Logistic regression analysis
        Type: general
      – SubjectFull: Sample size (Statistics)
        Type: general
      – SubjectFull: Distribution (Probability theory)
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      – SubjectFull: Comparative studies
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      – SubjectFull: Qualitative research
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      – SubjectFull: Simulation methods & models
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