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The Item Response Warehouse: What It Is, How to Use It, and Targets for Potential Improvements

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Title: The Item Response Warehouse: What It Is, How to Use It, and Targets for Potential Improvements
Language: English
Authors: Savira D. Nadela, Hansol Lee, Nishka Jain, Ayaan Gupta, Benjamin W. Domingue (ORCID 0000-0002-3894-9049)
Source: Grantee Submission. 2026.
Peer Reviewed: Y
Page Count: 14
Publication Date: 2026
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305D240025
Document Type: Reports - Descriptive
Descriptors: Item Response Theory, Educational Assessment, Psychometrics, Psychological Testing, Metadata, Programming Languages, Natural Language Processing, Information Storage, Databases, Access to Information
Abstract: The Item Response Warehouse (IRW) is a repository of harmonized item response datasets designed to support secondary analysis and methodological research in psychological and educational measurement. This paper serves as a practical guide for researchers interested in using the IRW. We describe the structure of IRW datasets and the quantitative and qualitative metadata available for dataset selection, and we demonstrate how researchers can navigate the IRW website to explore and compare available tables. We further show how the IRW R and Python packages can be used to filter datasets programmatically, download response-level data, and generate standardized citations for reproducible research workflows. Finally, we describe additional IRW features, including access to item text and ongoing development efforts, that are designed to both support immediate use of the IRW and allow for community input related to its continued expansion. [This paper will be published in the "Chinese/English Journal of Educational Measurement and Evaluation."]
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2026
Access URL: https://www.ce-jeme.org/journal/
Accession Number: ED679751
Database: ERIC
FullText Text:
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  Data: <searchLink fieldCode="AR" term="%22Savira+D%2E+Nadela%22">Savira D. Nadela</searchLink><br /><searchLink fieldCode="AR" term="%22Hansol+Lee%22">Hansol Lee</searchLink><br /><searchLink fieldCode="AR" term="%22Nishka+Jain%22">Nishka Jain</searchLink><br /><searchLink fieldCode="AR" term="%22Ayaan+Gupta%22">Ayaan Gupta</searchLink><br /><searchLink fieldCode="AR" term="%22Benjamin+W%2E+Domingue%22">Benjamin W. Domingue</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3894-9049">0000-0002-3894-9049</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Grantee+Submission%22"><i>Grantee Submission</i></searchLink>. 2026.
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  Data: 14
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  Data: 2026
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  Data: Institute of Education Sciences (ED)
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  Data: <searchLink fieldCode="DE" term="%22Item+Response+Theory%22">Item Response Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Assessment%22">Educational Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Testing%22">Psychological Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Metadata%22">Metadata</searchLink><br /><searchLink fieldCode="DE" term="%22Programming+Languages%22">Programming Languages</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Storage%22">Information Storage</searchLink><br /><searchLink fieldCode="DE" term="%22Databases%22">Databases</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Information%22">Access to Information</searchLink>
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  Data: The Item Response Warehouse (IRW) is a repository of harmonized item response datasets designed to support secondary analysis and methodological research in psychological and educational measurement. This paper serves as a practical guide for researchers interested in using the IRW. We describe the structure of IRW datasets and the quantitative and qualitative metadata available for dataset selection, and we demonstrate how researchers can navigate the IRW website to explore and compare available tables. We further show how the IRW R and Python packages can be used to filter datasets programmatically, download response-level data, and generate standardized citations for reproducible research workflows. Finally, we describe additional IRW features, including access to item text and ongoing development efforts, that are designed to both support immediate use of the IRW and allow for community input related to its continued expansion. [This paper will be published in the "Chinese/English Journal of Educational Measurement and Evaluation."]
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      – SubjectFull: Educational Assessment
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      – SubjectFull: Psychometrics
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      – SubjectFull: Psychological Testing
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              Y: 2026
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