A Gibbs Sampler for the (Extended) Marginal Rasch Model.
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| Title: | A Gibbs Sampler for the (Extended) Marginal Rasch Model. |
|---|---|
| Authors: | Maris, Gunter1 Gunter.Maris@cito.nl, Bechger, Timo2, Martin, Ernesto3 |
| Source: | Psychometrika. Dec2015, Vol. 80 Issue 4, p859-879. 21p. |
| Subject Terms: | *Educational tests & measurements, Rasch models, Gibbs sampling, Markov chain Monte Carlo, Mathematical statistics |
| Abstract: | In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie and Holland give a theoretically important characterization of the marginal Rasch model. Because their representation of the marginal Rasch model does not involve any latent trait, nor any specific distribution of a latent trait, it opens up the possibility for constructing a Markov chain - Monte Carlo method for Bayesian inference for the marginal Rasch model that does not rely on data augmentation. Such an approach would be highly efficient as its computational cost does not depend on the number of respondents, which makes it suitable for large-scale educational measurement. In this paper, such an approach will be developed and its operating characteristics illustrated with simulated data. [ABSTRACT FROM AUTHOR] |
| Copyright of Psychometrika is the property of Cambridge University Press 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 | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 110931650 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Gibbs Sampler for the (Extended) Marginal Rasch Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Maris%2C+Gunter%22">Maris, Gunter</searchLink><relatesTo>1</relatesTo><i> Gunter.Maris@cito.nl</i><br /><searchLink fieldCode="AR" term="%22Bechger%2C+Timo%22">Bechger, Timo</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Martin%2C+Ernesto%22">Martin, Ernesto</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Psychometrika%22">Psychometrika</searchLink>. Dec2015, Vol. 80 Issue 4, p859-879. 21p. – 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="%22Rasch+models%22">Rasch models</searchLink><br /><searchLink fieldCode="DE" term="%22Gibbs+sampling%22">Gibbs sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+statistics%22">Mathematical statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In their seminal work on characterizing the manifest probabilities of latent trait models, Cressie and Holland give a theoretically important characterization of the marginal Rasch model. Because their representation of the marginal Rasch model does not involve any latent trait, nor any specific distribution of a latent trait, it opens up the possibility for constructing a Markov chain - Monte Carlo method for Bayesian inference for the marginal Rasch model that does not rely on data augmentation. Such an approach would be highly efficient as its computational cost does not depend on the number of respondents, which makes it suitable for large-scale educational measurement. In this paper, such an approach will be developed and its operating characteristics illustrated with simulated data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Psychometrika is the property of Cambridge University Press 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: 10.1007/s11336-015-9479-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 859 Subjects: – SubjectFull: Educational tests & measurements Type: general – SubjectFull: Rasch models Type: general – SubjectFull: Gibbs sampling Type: general – SubjectFull: Markov chain Monte Carlo Type: general – SubjectFull: Mathematical statistics Type: general Titles: – TitleFull: A Gibbs Sampler for the (Extended) Marginal Rasch Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Maris, Gunter – PersonEntity: Name: NameFull: Bechger, Timo – PersonEntity: Name: NameFull: Martin, Ernesto IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2015 Type: published Y: 2015 Identifiers: – Type: issn-print Value: 00333123 Numbering: – Type: volume Value: 80 – Type: issue Value: 4 Titles: – TitleFull: Psychometrika Type: main |
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