|Title||Item response theory and health outcomes measurement in the 21st century|
|Publication Type||Journal Article|
|Year of Publication||2000|
|Authors||Hays, RD, Morales, LS, Reise, SP|
|Number||9 Suppl II|
|Keywords||*Models, Statistical, Activities of Daily Living, Data Interpretation, Statistical, Health Services Research/*methods, Health Surveys, Human, Mathematical Computing, Outcome Assessment (Health Care)/*methods, Research Design, Support, Non-U.S. Gov't, Support, U.S. Gov't, P.H.S., United States|
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.