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Are the Nonparametric Person-Fit Statistics More Powerful Than Their Parametric Counterparts? Revisiting the Simulations in Karabatsos (2003).

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Title: Are the Nonparametric Person-Fit Statistics More Powerful Than Their Parametric Counterparts? Revisiting the Simulations in Karabatsos (2003).
Authors: Sinharay, Sandip (AUTHOR)
Source: Applied Measurement in Education. Oct-Dec2017, Vol. 30 Issue 4, p314-328. 15p.
Subjects: Nonparametric estimation, Educational tests & measurements, Nonparametric statistics, Simulation methods & models, Educational testing services, Examinations, Receiver operating characteristic curves, Education research
Abstract: Karabatsos compared the power of 36 person-fit statistics using receiver operating characteristics curves and found theHTstatistic to be the most powerful in identifying aberrant examinees. He found three statistics,C, MCI, andU3, to be the next most powerful. These four statistics, all of which are nonparametric, were found to perform considerably better than each of 25 parametric person-fit statistics. Dimitrov and Smith replicated part of this finding in a similar study. The present article raises some issues with the comparisons performed in Karabatsos and Dimitrov and Smith and points to literature that suggests that the comparisons could have been performed in a more traditional and more fair manner. The present article then replicates the simulations of Karabatsos and demonstrates in several ways that the parametric person-fit statisticslzandECI4z(that were also considered by Karabatsos) are as powerful as areHTandU3 in identifying aberrant examinees in more traditional and fair comparisons. Two parametric person-fit statistics are shown to lead to similar results asHTandU3 in a real data example. [ABSTRACT FROM AUTHOR]
Copyright of Applied Measurement in Education 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.)
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  Data: Are the Nonparametric Person-Fit Statistics More Powerful Than Their Parametric Counterparts? Revisiting the Simulations in Karabatsos (2003).
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  Data: Karabatsos compared the power of 36 person-fit statistics using receiver operating characteristics curves and found theHTstatistic to be the most powerful in identifying aberrant examinees. He found three statistics,C, MCI, andU3, to be the next most powerful. These four statistics, all of which are nonparametric, were found to perform considerably better than each of 25 parametric person-fit statistics. Dimitrov and Smith replicated part of this finding in a similar study. The present article raises some issues with the comparisons performed in Karabatsos and Dimitrov and Smith and points to literature that suggests that the comparisons could have been performed in a more traditional and more fair manner. The present article then replicates the simulations of Karabatsos and demonstrates in several ways that the parametric person-fit statisticslzandECI4z(that were also considered by Karabatsos) are as powerful as areHTandU3 in identifying aberrant examinees in more traditional and fair comparisons. Two parametric person-fit statistics are shown to lead to similar results asHTandU3 in a real data example. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Measurement in Education 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: 10.1080/08957347.2017.1353990
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 314
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      – SubjectFull: Nonparametric estimation
        Type: general
      – SubjectFull: Educational tests & measurements
        Type: general
      – SubjectFull: Nonparametric statistics
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      – SubjectFull: Simulation methods & models
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      – SubjectFull: Educational testing services
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      – SubjectFull: Examinations
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      – SubjectFull: Receiver operating characteristic curves
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      – SubjectFull: Education research
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              Text: Oct-Dec2017
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