A Comparison of the Efficacies of Differential Item Functioning Detection Methods

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Title: A Comparison of the Efficacies of Differential Item Functioning Detection Methods
Language: English
Authors: Basman, Munevver (ORCID 0000-0003-3572-7982)
Source: International Journal of Assessment Tools in Education. 2023 10(1):145-159.
Availability: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://ijate.net/index.php/ijate
Peer Reviewed: Y
Page Count: 15
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Test Bias, Test Items, Test Validity, Item Response Theory, Test Theory, Test Length, Sample Size, Monte Carlo Methods, Error Patterns
ISSN: 2148-7456
Abstract: To ensure the validity of the tests is to check that all items have similar results across different groups of individuals. However, differential item functioning (DIF) occurs when the results of individuals with equal ability levels from different groups differ from each other on the same test item. Based on Item Response Theory and Classic Test Theory, there are some methods, with different advantages and limitations to identify items that show DIF. This study aims to compare the performances of five methods for detecting DIF. The efficacies of Mantel-Haenszel (MH), Logistic Regression (LR), Crossing simultaneous item bias test (CSIBTEST), Lord's chi-square (LORD), and Raju's area measure (RAJU) methods are examined considering conditions of the sample size, DIF ratio, and test length. In this study, to compare the detection methods, power and Type I error rates are evaluated using a simulation study with 100 replications conducted for each condition. Results show that LR and MH have the lowest Type I error and the highest power rate in detecting uniform DIF. In addition, CSIBTEST has a similar power rate to MH and LR. Under DIF conditions, sample size, DIF ratio, test length and their interactions affect Type I error and power rates.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1393256
Database: ERIC
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  Data: A Comparison of the Efficacies of Differential Item Functioning Detection Methods
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  Data: <searchLink fieldCode="AR" term="%22Basman%2C+Munevver%22">Basman, Munevver</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3572-7982">0000-0003-3572-7982</externalLink>)
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  Data: International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://ijate.net/index.php/ijate
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  Data: <searchLink fieldCode="DE" term="%22Test+Bias%22">Test Bias</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Validity%22">Test Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Theory%22">Test Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Length%22">Test Length</searchLink><br /><searchLink fieldCode="DE" term="%22Sample+Size%22">Sample Size</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Error+Patterns%22">Error Patterns</searchLink>
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  Data: To ensure the validity of the tests is to check that all items have similar results across different groups of individuals. However, differential item functioning (DIF) occurs when the results of individuals with equal ability levels from different groups differ from each other on the same test item. Based on Item Response Theory and Classic Test Theory, there are some methods, with different advantages and limitations to identify items that show DIF. This study aims to compare the performances of five methods for detecting DIF. The efficacies of Mantel-Haenszel (MH), Logistic Regression (LR), Crossing simultaneous item bias test (CSIBTEST), Lord's chi-square (LORD), and Raju's area measure (RAJU) methods are examined considering conditions of the sample size, DIF ratio, and test length. In this study, to compare the detection methods, power and Type I error rates are evaluated using a simulation study with 100 replications conducted for each condition. Results show that LR and MH have the lowest Type I error and the highest power rate in detecting uniform DIF. In addition, CSIBTEST has a similar power rate to MH and LR. Under DIF conditions, sample size, DIF ratio, test length and their interactions affect Type I error and power rates.
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  Data: 2023
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      – Text: English
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      Pagination:
        PageCount: 15
        StartPage: 145
    Subjects:
      – SubjectFull: Test Bias
        Type: general
      – SubjectFull: Test Items
        Type: general
      – SubjectFull: Test Validity
        Type: general
      – SubjectFull: Item Response Theory
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      – SubjectFull: Test Theory
        Type: general
      – SubjectFull: Test Length
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      – SubjectFull: Sample Size
        Type: general
      – SubjectFull: Monte Carlo Methods
        Type: general
      – SubjectFull: Error Patterns
        Type: general
    Titles:
      – TitleFull: A Comparison of the Efficacies of Differential Item Functioning Detection Methods
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              Y: 2023
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