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Cited by 869

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Item Response Theory and Health Outcomes Measurement in the 21st Century

Hays, Ron D., Morales, Leo S., Reise, Steve P.·2000·Medical Care

Abstract

Item response theory (IRT) has a number of potential advantages over classical test theory in assessing self-reported health outcomes. IRT models yield invariant item and latent trait estimates (within a linear transformation), standard errors conditional on trait level, and trait estimates anchored to item content. IRT also facilitates evaluation of differential item functioning, inclusion of items with different response formats in the same scale, and assessment of person fit and is ideally suited for implementing computer adaptive testing. Finally, IRT methods can be helpful in developing better health outcome measures and in assessing change over time. These issues are reviewed, along with a discussion of some of the methodological and practical challenges in applying IRT methods.

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Metadata

Authors
Hays, R. D., Morales, L. S., & Reise, S. P.
Venue
Medical Care
Published
2000-09
Volume
38
Issue
9 Suppl
Pages
II-28-II-42
ISSN
0025-7079 (ISSN-L)
OpenAlex
W1969055757
Cited by
869
References
53
FWCI
31.03
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Item Response Theory and Health Outcomes Measurement in the 21st Century. https://app.cochat.ai/papers/10.1097/00005650-200009002-00007
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Hays, R. D., Morales, L. S., & Reise, S. P. (2000). Item Response Theory and Health Outcomes Measurement in the 21st Century. Medical Care. https://doi.org/10.1097/00005650-200009002-00007

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.