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Delicate balance between theory and practice: health status assessment and item response theory.

Devotion to the psychometric trinity of reliability, validity, and responsiveness has substantially improved measurement fidelity in health outcomes assessment. We are glad that researchers feel an onus to gather psychometric support before using outcome instruments. However, we are eager for a newer trend to emerge—one in which psychometrics is used for illumination rather than support. In a number of respects, the work by Fortinsky et al2 in this issue of Medical Care exemplifies that hoped-for trend. The study evaluates the functional status portion of the Outcomes and Assessment Information Set (OASIS) using item response theory (IRT). For this analysis, they employed the 1-parameter logistic (1-pL) model, better known as the Rasch model. The Rasch model estimates a single-item parameter— item difficulty; other IRT models estimate additional item parameters. The 2-parameter (2-pL) model estimates item difficulty and item discrimination. The 3-parameter (3-pL) model, used with items scored right or wrong (eg, in educational testing), estimates difficulty, discrimination, and a pseudoguessing parameter. Item response theory was introduced 40 years ago in education and psychology but has been applied only more recently in outcomes research. Unfortunately, when IRT models migrated from education and psychologic testing to health outcomes research, much of the sophisticated theory and methodological precision developed in IRT’s home fields was left behind. In this regard, the article by Fortinsky et al2 is superior to most IRT-based evaluations of health assessments. We wish to acknowledge the authors’ efforts in a number of areas and challenge them in others. Item response theory is a powerful tool. However, what often is not acknowledged is that IRT models rest on strong assumptions. Unidimensional IRT models assume that (1) all the items of a scale tap a single latent trait (unidimensionality), (2) item responses are independent for persons with the same level of the trait being measured (local item independence), and (3) the data fit the mathematical function defined by the IRT model. As Brennan3 stated, “the assumption of local independence in item response theory (IRT) is almost certainly false in virtually all circumstances. . . [S]trong assumptions lead to strong results... Indeed, if the assumptions of IRT hold, many of our most intractable measurement problems evaporate.”