TY - JOUR T1 - Item response theory and health outcomes measurement in the 21st century JF - Medical Care Y1 - 2000 A1 - Hays, R. D. A1 - Morales, L. S. A1 - Reise, S. P. KW - *Models, Statistical KW - Activities of Daily Living KW - Data Interpretation, Statistical KW - Health Services Research/*methods KW - Health Surveys KW - Human KW - Mathematical Computing KW - Outcome Assessment (Health Care)/*methods KW - Research Design KW - Support, Non-U.S. Gov't KW - Support, U.S. Gov't, P.H.S. KW - United States AB - 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. VL - 38 N1 - 204349670025-7079Journal Article ER -