A Mixture IRTree Model for Performance Decline and Nonignorable Missing Data
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| Title: | A Mixture IRTree Model for Performance Decline and Nonignorable Missing Data |
|---|---|
| Language: | English |
| Authors: | Huang, Hung-Yu (ORCID |
| Source: | Educational and Psychological Measurement. Dec 2020 80(6):1168-1195. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2020 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Secondary Education |
| Descriptors: | Item Response Theory, Response Style (Tests), Test Items, Statistical Analysis, Bayesian Statistics, Achievement Tests, Foreign Countries, International Assessment, Secondary School Students |
| Assessment and Survey Identifiers: | Program for International Student Assessment |
| DOI: | 10.1177/0013164420914711 |
| ISSN: | 0013-1644 |
| Abstract: | In educational assessments and achievement tests, test developers and administrators commonly assume that test-takers attempt all test items with full effort and leave no blank responses with unplanned missing values. However, aberrant response behavior--such as performance decline, dropping out beyond a certain point, and skipping certain items over the course of the test--is inevitable, especially for low-stakes assessments and speeded tests due to low motivation and time limits, respectively. In this study, test-takers are classified as normal or aberrant using a mixture item response theory (IRT) modeling approach, and aberrant response behavior is described and modeled using item response trees (IRTrees). Simulations are conducted to evaluate the efficiency and quality of the new class of mixture IRTree model using WinBUGS with Bayesian estimation. The results show that the parameter recovery is satisfactory for the proposed mixture IRTree model and that treating missing values as ignorable or incorrect and ignoring possible performance decline results in biased estimation. Finally, the applicability of the new model is illustrated by means of an empirical example based on the Program for International Student Assessment. |
| Abstractor: | As Provided |
| Entry Date: | 2020 |
| Accession Number: | EJ1269525 |
| Database: | ERIC |
| FullText | Text: Availability: 0 |
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| Header | DbId: eric DbLabel: ERIC An: EJ1269525 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Mixture IRTree Model for Performance Decline and Nonignorable Missing Data – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Hung-Yu%22">Huang, Hung-Yu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6244-1950">0000-0001-6244-1950</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Educational+and+Psychological+Measurement%22"><i>Educational and Psychological Measurement</i></searchLink>. Dec 2020 80(6):1168-1195. – Name: Avail Label: Availability Group: Avail Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 28 – Name: DatePubCY Label: Publication Date Group: Date Data: 2020 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Response+Style+%28Tests%29%22">Response Style (Tests)</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Items%22">Test Items</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Analysis%22">Statistical Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+Statistics%22">Bayesian Statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+Tests%22">Achievement Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22International+Assessment%22">International Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink> – Name: SubjectThesaurus Label: Assessment and Survey Identifiers Group: Su Data: <searchLink fieldCode="SU" term="%22Program+for+International+Student+Assessment%22">Program for International Student Assessment</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1177/0013164420914711 – Name: ISSN Label: ISSN Group: ISSN Data: 0013-1644 – Name: Abstract Label: Abstract Group: Ab Data: In educational assessments and achievement tests, test developers and administrators commonly assume that test-takers attempt all test items with full effort and leave no blank responses with unplanned missing values. However, aberrant response behavior--such as performance decline, dropping out beyond a certain point, and skipping certain items over the course of the test--is inevitable, especially for low-stakes assessments and speeded tests due to low motivation and time limits, respectively. In this study, test-takers are classified as normal or aberrant using a mixture item response theory (IRT) modeling approach, and aberrant response behavior is described and modeled using item response trees (IRTrees). Simulations are conducted to evaluate the efficiency and quality of the new class of mixture IRTree model using WinBUGS with Bayesian estimation. The results show that the parameter recovery is satisfactory for the proposed mixture IRTree model and that treating missing values as ignorable or incorrect and ignoring possible performance decline results in biased estimation. Finally, the applicability of the new model is illustrated by means of an empirical example based on the Program for International Student Assessment. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2020 – Name: AN Label: Accession Number Group: ID Data: EJ1269525 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1269525 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/0013164420914711 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 1168 Subjects: – SubjectFull: Item Response Theory Type: general – SubjectFull: Response Style (Tests) Type: general – SubjectFull: Test Items Type: general – SubjectFull: Statistical Analysis Type: general – SubjectFull: Bayesian Statistics Type: general – SubjectFull: Achievement Tests Type: general – SubjectFull: Foreign Countries Type: general – SubjectFull: International Assessment Type: general – SubjectFull: Secondary School Students Type: general – SubjectFull: Program for International Student Assessment Type: general Titles: – TitleFull: A Mixture IRTree Model for Performance Decline and Nonignorable Missing Data Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Hung-Yu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 0013-1644 Numbering: – Type: volume Value: 80 – Type: issue Value: 6 Titles: – TitleFull: Educational and Psychological Measurement Type: main |
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