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Efficient Standard Errors in Item Response Theory Models for Short Tests

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Title: Efficient Standard Errors in Item Response Theory Models for Short Tests
Language: English
Authors: Ippel, Lianne (ORCID 0000-0001-8314-0305), Magis, David
Source: Educational and Psychological Measurement. Jun 2020 80(3):461-475.
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: 15
Publication Date: 2020
Document Type: Journal Articles
Reports - Research
Descriptors: Item Response Theory, Error of Measurement, Accuracy, Standards, Guidelines, Models, Test Format, Item Analysis, Maximum Likelihood Statistics, Mathematical Formulas
DOI: 10.1177/0013164419882072
ISSN: 0013-1644
Abstract: In dichotomous item response theory (IRT) framework, the asymptotic standard error (ASE) is the most common statistic to evaluate the precision of various ability estimators. Easy-to-use ASE formulas are readily available; however, the accuracy of some of these formulas was recently questioned and new ASE formulas were derived from a general asymptotic theory framework. Furthermore, exact standard errors were suggested to better evaluate the precision of ability estimators, especially with short tests for which the asymptotic framework is invalid. Unfortunately, the accuracy of exact standard errors was assessed so far only in a very limiting setting. The purpose of this article is to perform a global comparison of exact versus (classical and new formulations of) asymptotic standard errors, for a wide range of usual IRT ability estimators, IRT models, and with short tests. Results indicate that exact standard errors globally outperform the ASE versions in terms of reduced bias and root mean square error, while the new ASE formulas are also globally less biased than their classical counterparts. Further discussion about the usefulness and practical computation of exact standard errors are outlined.
Abstractor: As Provided
Entry Date: 2020
Accession Number: EJ1253240
Database: ERIC
FullText Text:
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  Data: Efficient Standard Errors in Item Response Theory Models for Short Tests
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  Data: <searchLink fieldCode="AR" term="%22Ippel%2C+Lianne%22">Ippel, Lianne</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8314-0305">0000-0001-8314-0305</externalLink>)<br /><searchLink fieldCode="AR" term="%22Magis%2C+David%22">Magis, David</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Educational+and+Psychological+Measurement%22"><i>Educational and Psychological Measurement</i></searchLink>. Jun 2020 80(3):461-475.
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  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
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  Data: 10.1177/0013164419882072
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  Data: 0013-1644
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  Data: In dichotomous item response theory (IRT) framework, the asymptotic standard error (ASE) is the most common statistic to evaluate the precision of various ability estimators. Easy-to-use ASE formulas are readily available; however, the accuracy of some of these formulas was recently questioned and new ASE formulas were derived from a general asymptotic theory framework. Furthermore, exact standard errors were suggested to better evaluate the precision of ability estimators, especially with short tests for which the asymptotic framework is invalid. Unfortunately, the accuracy of exact standard errors was assessed so far only in a very limiting setting. The purpose of this article is to perform a global comparison of exact versus (classical and new formulations of) asymptotic standard errors, for a wide range of usual IRT ability estimators, IRT models, and with short tests. Results indicate that exact standard errors globally outperform the ASE versions in terms of reduced bias and root mean square error, while the new ASE formulas are also globally less biased than their classical counterparts. Further discussion about the usefulness and practical computation of exact standard errors are outlined.
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        Value: 10.1177/0013164419882072
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 461
    Subjects:
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Error of Measurement
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Standards
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      – SubjectFull: Guidelines
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      – SubjectFull: Models
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      – SubjectFull: Test Format
        Type: general
      – SubjectFull: Item Analysis
        Type: general
      – SubjectFull: Maximum Likelihood Statistics
        Type: general
      – SubjectFull: Mathematical Formulas
        Type: general
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      – TitleFull: Efficient Standard Errors in Item Response Theory Models for Short Tests
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            NameFull: Ippel, Lianne
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            NameFull: Magis, David
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