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Exploring Boundary Conditions on the Invariance Property of Item Parameter Estimates of Item Response Models.

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Title: Exploring Boundary Conditions on the Invariance Property of Item Parameter Estimates of Item Response Models.
Language: English
Authors: Sadesky, Greg S.
Peer Reviewed: N
Page Count: 16
Publication Date: 2003
Document Type: Reports - Research
Descriptors: Ability, Estimation (Mathematics), Item Response Theory, Robustness (Statistics), Simulation
Abstract: In this study, the robustness of item parameter estimates with respect to the underlying distribution of abilities was explored. Using simulated datasets, item parameters were derived from a large sample representing the population versus samples representing a subset of ability and subsequently compared for possible mismatch. Comparisons were made under these conditions: (1) increasing differences between ability of examinees in the pilot sample and those of the population; and (2) the size of the pilot sample. All datasets were generated using DIFSUM, a computer program that permits the creation of dichotomous test data according to prespecified characteristics. In general, the study shows that item parameter estimates derived from a sample contain more errors when ability differences exist between the sample and its population. Fewer errors result when the size of the sample is increased. This finding highlights some limitations of the item invariance property claimed by item response theorists. Some of the limitations of the study are also discussed. (SLD)
Entry Date: 2004
Accession Number: ED479923
Database: ERIC
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  Availability: 0
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  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED479923
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    Text: Full Text from ERIC
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PubType: Report
PubTypeId: report
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IllustrationInfo
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  Data: Exploring Boundary Conditions on the Invariance Property of Item Parameter Estimates of Item Response Models.
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  Data: N
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  Data: 16
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  Data: Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Ability%22">Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+%28Mathematics%29%22">Estimation (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Robustness+%28Statistics%29%22">Robustness (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation%22">Simulation</searchLink>
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  Data: In this study, the robustness of item parameter estimates with respect to the underlying distribution of abilities was explored. Using simulated datasets, item parameters were derived from a large sample representing the population versus samples representing a subset of ability and subsequently compared for possible mismatch. Comparisons were made under these conditions: (1) increasing differences between ability of examinees in the pilot sample and those of the population; and (2) the size of the pilot sample. All datasets were generated using DIFSUM, a computer program that permits the creation of dichotomous test data according to prespecified characteristics. In general, the study shows that item parameter estimates derived from a sample contain more errors when ability differences exist between the sample and its population. Fewer errors result when the size of the sample is increased. This finding highlights some limitations of the item invariance property claimed by item response theorists. Some of the limitations of the study are also discussed. (SLD)
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
    Subjects:
      – SubjectFull: Ability
        Type: general
      – SubjectFull: Estimation (Mathematics)
        Type: general
      – SubjectFull: Item Response Theory
        Type: general
      – SubjectFull: Robustness (Statistics)
        Type: general
      – SubjectFull: Simulation
        Type: general
    Titles:
      – TitleFull: Exploring Boundary Conditions on the Invariance Property of Item Parameter Estimates of Item Response Models.
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            NameFull: Sadesky, Greg S.
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              M: 01
              Type: published
              Y: 2003
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