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

TLDR

Item response theory offers advantages over classical test theory for self‑reported health outcomes, providing invariant item and latent trait estimates, standard errors tied to trait level, and the ability to evaluate differential item functioning, combine items of different formats, assess person fit, and support computer‑adaptive testing, thereby improving measurement and change detection. The paper reviews these IRT advantages and discusses methodological and practical challenges in applying IRT to health outcome measurement. The authors conduct a comprehensive review of IRT issues and outline key methodological and practical obstacles to its implementation.

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.