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Fusion of score‐differencing and response similarity statistics for detecting examinees with item preknowledge.

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Title: Fusion of score‐differencing and response similarity statistics for detecting examinees with item preknowledge.
Authors: Xu, Yongze (AUTHOR), He, Ruihang (AUTHOR), Huang, Meiwei (AUTHOR), Luo, Fang (AUTHOR)
Source: British Journal of Mathematical & Statistical Psychology. Nov2025, Vol. 78 Issue 3, p911-938. 28p.
Subjects: Student cheating, Data fusion (Statistics), Statistical reliability, Mathematical statistics, Assessment of education, Statistical measurement
Abstract: Item preknowledge (IP) is a prevalent form of test fraud in educational assessment that can compromise test validity. Two common methods for detecting examinees with IP are score‐differencing statistics and response similarity index (RSI). These statistics have different applications and respective advantages. In this paper, we propose a new method (Joint Survival Function Method, JSFM) to combine these two types of statistics to calculate a fusion statistic that tries to address the issue of distribution differences between the original indicators. By combining the advantages of the original indicators, the fusion statistic can more effectively detect examinees with IP. We fused two typical RSI and four typical score‐differencing statistics using different methods and compared their performance. The results demonstrate that the proposed JSFM exhibits strong cross‐scenario stability and performs better than other fusion methods. [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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  Label: Title
  Group: Ti
  Data: Fusion of score‐differencing and response similarity statistics for detecting examinees with item preknowledge.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Xu%2C+Yongze%22">Xu, Yongze</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Ruihang%22">He, Ruihang</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Meiwei%22">Huang, Meiwei</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Fang%22">Luo, Fang</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>. Nov2025, Vol. 78 Issue 3, p911-938. 28p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Student+cheating%22">Student cheating</searchLink><br /><searchLink fieldCode="DE" term="%22Data+fusion+%28Statistics%29%22">Data fusion (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Assessment+of+education%22">Assessment of education</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+measurement%22">Statistical measurement</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Item preknowledge (IP) is a prevalent form of test fraud in educational assessment that can compromise test validity. Two common methods for detecting examinees with IP are score‐differencing statistics and response similarity index (RSI). These statistics have different applications and respective advantages. In this paper, we propose a new method (Joint Survival Function Method, JSFM) to combine these two types of statistics to calculate a fusion statistic that tries to address the issue of distribution differences between the original indicators. By combining the advantages of the original indicators, the fusion statistic can more effectively detect examinees with IP. We fused two typical RSI and four typical score‐differencing statistics using different methods and compared their performance. The results demonstrate that the proposed JSFM exhibits strong cross‐scenario stability and performs better than other fusion methods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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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RecordInfo BibRecord:
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        Value: 10.1111/bmsp.12388
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        Text: English
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        PageCount: 28
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        Type: general
      – SubjectFull: Data fusion (Statistics)
        Type: general
      – SubjectFull: Statistical reliability
        Type: general
      – SubjectFull: Mathematical statistics
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      – SubjectFull: Assessment of education
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      – SubjectFull: Statistical measurement
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            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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