RIM: A Random Item Mixture Model to Detect Differential Item Functioning.
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| Title: | RIM: A Random Item Mixture Model to Detect Differential Item Functioning. |
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| Authors: | Frederickx, Sofie1 sofie.frederickx@psy.kuleuven.be, Tuerlinckx, Francis1 francis.tuerlinckx@psy.kuleuven.be, De Boeck, Paul2 paul.deboeck@uva.nl, Magis, David3 david.magis@ulg.ac.be |
| Source: | Journal of Educational Measurement. Dec2010, Vol. 47 Issue 4, p432-457. 26p. 5 Charts, 2 Graphs. |
| Subject Terms: | *Educational tests & measurements, *Item response theory, *Bayesian analysis, Rasch models, Statistics |
| Abstract: | In this paper we present a new methodology for detecting differential item functioning (DIF). We introduce a DIF model, called the random item mixture (RIM), that is based on a Rasch model with random item difficulties (besides the common random person abilities). In addition, a mixture model is assumed for the item difficulties such that the items may belong to one of two classes: a DIF or a non-DIF class. The crucial difference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may differ according to the observed person groups while they are equal across the person groups for the items from the non-DIF class. Statistical inference for the RIM is carried out in a Bayesian framework. The performance of the RIM is evaluated using a simulation study in which it is compared with traditional procedures, like the likelihood ratio test, the Mantel-Haenszel procedure and the standardized -DIF procedure. In this comparison, the RIM performs better than the other methods. Finally, the usefulness of the model is also demonstrated on a real life 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 60026048 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: RIM: A Random Item Mixture Model to Detect Differential Item Functioning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Frederickx%2C+Sofie%22">Frederickx, Sofie</searchLink><relatesTo>1</relatesTo><i> sofie.frederickx@psy.kuleuven.be</i><br /><searchLink fieldCode="AR" term="%22Tuerlinckx%2C+Francis%22">Tuerlinckx, Francis</searchLink><relatesTo>1</relatesTo><i> francis.tuerlinckx@psy.kuleuven.be</i><br /><searchLink fieldCode="AR" term="%22De+Boeck%2C+Paul%22">De Boeck, Paul</searchLink><relatesTo>2</relatesTo><i> paul.deboeck@uva.nl</i><br /><searchLink fieldCode="AR" term="%22Magis%2C+David%22">Magis, David</searchLink><relatesTo>3</relatesTo><i> david.magis@ulg.ac.be</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Dec2010, Vol. 47 Issue 4, p432-457. 26p. 5 Charts, 2 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Educational+tests+%26+measurements%22">Educational tests & measurements</searchLink><br />*<searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Rasch+models%22">Rasch models</searchLink><br /><searchLink fieldCode="DE" term="%22Statistics%22">Statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper we present a new methodology for detecting differential item functioning (DIF). We introduce a DIF model, called the random item mixture (RIM), that is based on a Rasch model with random item difficulties (besides the common random person abilities). In addition, a mixture model is assumed for the item difficulties such that the items may belong to one of two classes: a DIF or a non-DIF class. The crucial difference between the DIF class and the non-DIF class is that the item difficulties in the DIF class may differ according to the observed person groups while they are equal across the person groups for the items from the non-DIF class. Statistical inference for the RIM is carried out in a Bayesian framework. The performance of the RIM is evaluated using a simulation study in which it is compared with traditional procedures, like the likelihood ratio test, the Mantel-Haenszel procedure and the standardized -DIF procedure. In this comparison, the RIM performs better than the other methods. Finally, the usefulness of the model is also demonstrated on a real life 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/j.1745-3984.2010.00122.x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 432 Subjects: – SubjectFull: Educational tests & measurements Type: general – SubjectFull: Item response theory Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Rasch models Type: general – SubjectFull: Statistics Type: general Titles: – TitleFull: RIM: A Random Item Mixture Model to Detect Differential Item Functioning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Frederickx, Sofie – PersonEntity: Name: NameFull: Tuerlinckx, Francis – PersonEntity: Name: NameFull: De Boeck, Paul – PersonEntity: Name: NameFull: Magis, David IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 00220655 Numbering: – Type: volume Value: 47 – Type: issue Value: 4 Titles: – TitleFull: Journal of Educational Measurement Type: main |
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