View in EDS HTML Full Text PDF Full Text

Constructing a Robust Score Scale from IRT Scores with Informed Boundaries.

Saved in:
Bibliographic Details
Title: Constructing a Robust Score Scale from IRT Scores with Informed Boundaries.
Authors: Choe, Edison M. (AUTHOR) edison.choe@renaissance.com, Han, Kyung T. (AUTHOR) khan@gmac.com
Source: Journal of Educational Measurement. Mar2022, Vol. 59 Issue 1, p4-21. 18p.
Subject Terms: *Cognitive ability, *Item response theory
Abstract: In operational testing, item response theory (IRT) models for dichotomous responses are popular for measuring a single latent construct θ$\theta$, such as cognitive ability in a content domain. Estimates of θ$\theta$, also called IRT scores or θ̂$\hat{\theta }$, can be computed using estimators based on the likelihood function, such as maximum likelihood (ML), weighted likelihood (WL), maximum a posteriori (MAP), and expected a posteriori (EAP). Although the parameter space of θ$\theta$ is theoretically unrestricted, the range of finite θ̂$\hat{\theta }$ is constrained by the estimator and test form properties, which is important to consider but often overlooked when developing a score scale for reporting purposes. Irrespective of the estimator or test forms at hand, a common practice is to fix arbitrary points symmetric about zero (e.g., −4 and 4) as anchors for deriving a score transformation, possibly resulting in unintended gaps or truncations at the extremes. Therefore, a systematic framework is proposed for using IRT scores to construct a robust score scale with informed boundaries that are logical and consistent across test forms. [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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: ehh
DbLabel: Education Research Complete
An: 156397601
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Constructing a Robust Score Scale from IRT Scores with Informed Boundaries.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Choe%2C+Edison+M%2E%22">Choe, Edison M.</searchLink> (AUTHOR)<i> edison.choe@renaissance.com</i><br /><searchLink fieldCode="AR" term="%22Han%2C+Kyung+T%2E%22">Han, Kyung T.</searchLink> (AUTHOR)<i> khan@gmac.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Educational+Measurement%22">Journal of Educational Measurement</searchLink>. Mar2022, Vol. 59 Issue 1, p4-21. 18p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Cognitive+ability%22">Cognitive ability</searchLink><br />*<searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In operational testing, item response theory (IRT) models for dichotomous responses are popular for measuring a single latent construct θ$\theta$, such as cognitive ability in a content domain. Estimates of θ$\theta$, also called IRT scores or θ̂$\hat{\theta }$, can be computed using estimators based on the likelihood function, such as maximum likelihood (ML), weighted likelihood (WL), maximum a posteriori (MAP), and expected a posteriori (EAP). Although the parameter space of θ$\theta$ is theoretically unrestricted, the range of finite θ̂$\hat{\theta }$ is constrained by the estimator and test form properties, which is important to consider but often overlooked when developing a score scale for reporting purposes. Irrespective of the estimator or test forms at hand, a common practice is to fix arbitrary points symmetric about zero (e.g., −4 and 4) as anchors for deriving a score transformation, possibly resulting in unintended gaps or truncations at the extremes. Therefore, a systematic framework is proposed for using IRT scores to construct a robust score scale with informed boundaries that are logical and consistent across test forms. [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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=156397601
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/jedm.12307
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 4
    Subjects:
      – SubjectFull: Cognitive ability
        Type: general
      – SubjectFull: Item response theory
        Type: general
    Titles:
      – TitleFull: Constructing a Robust Score Scale from IRT Scores with Informed Boundaries.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Choe, Edison M.
      – PersonEntity:
          Name:
            NameFull: Han, Kyung T.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 03
              Text: Mar2022
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 00220655
          Numbering:
            – Type: volume
              Value: 59
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Journal of Educational Measurement
              Type: main
ResultId 1