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Comparison of Item Response Theory Ability and Item Parameters According to Classical and Bayesian Estimation Methods

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Title: Comparison of Item Response Theory Ability and Item Parameters According to Classical and Bayesian Estimation Methods
Language: English
Authors: Eray Selçuk (ORCID 0000-0003-4033-4219), Ergül Demir (ORCID 0000-0002-3708-8013)
Source: International Journal of Assessment Tools in Education. 2024 11(2):213-248.
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://dergipark.org.tr/en/pub/ijate
Peer Reviewed: Y
Page Count: 36
Publication Date: 2024
Document Type: Journal Articles
Information Analyses
Reports - Evaluative
Descriptors: Item Response Theory, Item Analysis, Test Items, Simulation, Monte Carlo Methods, Comparative Analysis, Test Construction, Test Theory, Bayesian Statistics
ISSN: 2148-7456
Abstract: This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution (normal, left-skewed, right-skewed, leptokurtic, and platykurtic), test length (10, 20, 40), sample size (100, 500, 1000), logistics model (2PL, 3PL). The simulation conditions were performed with 100 replications. Mixed model ANOVA was performed to determine RMSE differentiations. The prior distribution type, test length, and estimation method in the differentiation of ability parameter and RMSE were estimated in 2PL models; the priori distribution type and test length were significant in the differences in the ability parameter and RMSE estimated in the 3PL model. While prior distribution type, sample size, and estimation method created a significant difference in the RMSE of the item discrimination parameter estimated in the 2PL model, none of the conditions created a significant difference in the RMSE of the item difficulty parameter. The priori distribution type, sample size, and estimation method in the item discrimination RMSE were estimated in the 3PL model; the a priori distribution and estimation method created significant differentiation in the RMSE of the lower asymptote parameter. However, none of the conditions significantly changed the RMSE of item difficulty parameters.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1440215
Database: ERIC
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  Data: Comparison of Item Response Theory Ability and Item Parameters According to Classical and Bayesian Estimation Methods
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  Data: <searchLink fieldCode="AR" term="%22Eray+Selçuk%22">Eray Selçuk</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4033-4219">0000-0003-4033-4219</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ergül+Demir%22">Ergül Demir</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3708-8013">0000-0002-3708-8013</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Assessment+Tools+in+Education%22"><i>International Journal of Assessment Tools in Education</i></searchLink>. 2024 11(2):213-248.
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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://dergipark.org.tr/en/pub/ijate
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  Data: 36
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  Data: Journal Articles<br />Information Analyses<br />Reports - Evaluative
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  Data: <searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Analysis%22">Item Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+Methods%22">Monte Carlo Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Construction%22">Test Construction</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Theory%22">Test Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+Statistics%22">Bayesian Statistics</searchLink>
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  Data: This research aims to compare the ability and item parameter estimations of Item Response Theory according to Maximum likelihood and Bayesian approaches in different Monte Carlo simulation conditions. For this purpose, depending on the changes in the priori distribution type, sample size, test length, and logistics model, the ability and item parameters estimated according to the maximum likelihood and Bayesian method and the differences in the RMSE of these parameters were examined. The priori distribution (normal, left-skewed, right-skewed, leptokurtic, and platykurtic), test length (10, 20, 40), sample size (100, 500, 1000), logistics model (2PL, 3PL). The simulation conditions were performed with 100 replications. Mixed model ANOVA was performed to determine RMSE differentiations. The prior distribution type, test length, and estimation method in the differentiation of ability parameter and RMSE were estimated in 2PL models; the priori distribution type and test length were significant in the differences in the ability parameter and RMSE estimated in the 3PL model. While prior distribution type, sample size, and estimation method created a significant difference in the RMSE of the item discrimination parameter estimated in the 2PL model, none of the conditions created a significant difference in the RMSE of the item difficulty parameter. The priori distribution type, sample size, and estimation method in the item discrimination RMSE were estimated in the 3PL model; the a priori distribution and estimation method created significant differentiation in the RMSE of the lower asymptote parameter. However, none of the conditions significantly changed the RMSE of item difficulty parameters.
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  Data: 2024
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 36
        StartPage: 213
    Subjects:
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Item Analysis
        Type: general
      – SubjectFull: Test Items
        Type: general
      – SubjectFull: Simulation
        Type: general
      – SubjectFull: Monte Carlo Methods
        Type: general
      – SubjectFull: Comparative Analysis
        Type: general
      – SubjectFull: Test Construction
        Type: general
      – SubjectFull: Test Theory
        Type: general
      – SubjectFull: Bayesian Statistics
        Type: general
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      – TitleFull: Comparison of Item Response Theory Ability and Item Parameters According to Classical and Bayesian Estimation Methods
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            NameFull: Eray Selçuk
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            NameFull: Ergül Demir
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              Y: 2024
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