View in EDS HTML Full Text PDF Full Text

Detecting Differential Item Functioning among Multiple Groups Using IRT Residual DIF Framework.

Saved in:
Bibliographic Details
Title: Detecting Differential Item Functioning among Multiple Groups Using IRT Residual DIF Framework.
Authors: Lim, Hwanggyu1 (AUTHOR), Zhu, Danqi2 (AUTHOR), Choe, Edison M.3 (AUTHOR), Han, KyungT.4 (AUTHOR)
Source: Journal of Educational Measurement. Dec2024, Vol. 61 Issue 4, p656-681. 26p.
Subject Terms: *Item response theory, False positive error, Error rates, Empirical research
Abstract: This study presents a generalized version of the residual differential item functioning (RDIF) detection framework in item response theory, named GRDIF, to analyze differential item functioning (DIF) in multiple groups. The GRDIF framework retains the advantages of the original RDIF framework, such as computational efficiency and ease of implementation. The performance of GRDIF was assessed through a simulation study and compared with existing DIF detection methods, including the generalized Mantel‐Haenszel, Lasso‐DIF, and alignment methods. Results showed that the GRDIF framework demonstrated well‐controlled Type I error rates close to the nominal level of.05 and satisfactory power in detecting uniform, nonuniform, and mixed DIF across different simulated conditions. Each of the three GRDIF statistics, GRDIFR$GRDI{{F}_R}$, GRDIFS$GRDI{{F}_S}$, and GRDIFRS$GRDI{{F}_{RS}}$, effectively detected the specific type of DIF for which it was designed, with GRDIFRS$GRDI{{F}_{RS}}$ exhibiting the most robust performance across all types of DIF. The GRDIF framework outperformed other DIF detection methods under various conditions, suggesting its potential for practical applications, particularly in large‐scale assessments involving multiple groups. Additionally, an empirical study demonstrated the efficacy and utility of the GRDIF framework in conducting DIF analysis with a high‐stakes assessment data set. [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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ehh
DbLabel: Education Research Complete
An: 181921599
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Detecting Differential Item Functioning among Multiple Groups Using IRT Residual DIF Framework.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lim%2C+Hwanggyu%22">Lim, Hwanggyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Danqi%22">Zhu, Danqi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Choe%2C+Edison+M%2E%22">Choe, Edison M.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+KyungT%2E%22">Han, KyungT.</searchLink><relatesTo>4</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Dec2024, Vol. 61 Issue 4, p656-681. 26p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  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="%22Error+rates%22">Error rates</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study presents a generalized version of the residual differential item functioning (RDIF) detection framework in item response theory, named GRDIF, to analyze differential item functioning (DIF) in multiple groups. The GRDIF framework retains the advantages of the original RDIF framework, such as computational efficiency and ease of implementation. The performance of GRDIF was assessed through a simulation study and compared with existing DIF detection methods, including the generalized Mantel‐Haenszel, Lasso‐DIF, and alignment methods. Results showed that the GRDIF framework demonstrated well‐controlled Type I error rates close to the nominal level of.05 and satisfactory power in detecting uniform, nonuniform, and mixed DIF across different simulated conditions. Each of the three GRDIF statistics, GRDIFR$GRDI{{F}_R}$, GRDIFS$GRDI{{F}_S}$, and GRDIFRS$GRDI{{F}_{RS}}$, effectively detected the specific type of DIF for which it was designed, with GRDIFRS$GRDI{{F}_{RS}}$ exhibiting the most robust performance across all types of DIF. The GRDIF framework outperformed other DIF detection methods under various conditions, suggesting its potential for practical applications, particularly in large‐scale assessments involving multiple groups. Additionally, an empirical study demonstrated the efficacy and utility of the GRDIF framework in conducting DIF analysis with a high‐stakes assessment data set. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=181921599
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jedm.12415
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 26
        StartPage: 656
    Subjects:
      – SubjectFull: Item response theory
        Type: general
      – SubjectFull: False positive error
        Type: general
      – SubjectFull: Error rates
        Type: general
      – SubjectFull: Empirical research
        Type: general
    Titles:
      – TitleFull: Detecting Differential Item Functioning among Multiple Groups Using IRT Residual DIF Framework.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Lim, Hwanggyu
      – PersonEntity:
          Name:
            NameFull: Zhu, Danqi
      – PersonEntity:
          Name:
            NameFull: Choe, Edison M.
      – PersonEntity:
          Name:
            NameFull: Han, KyungT.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 00220655
          Numbering:
            – Type: volume
              Value: 61
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Journal of Educational Measurement
              Type: main
ResultId 1