The use of item response theory (IRT) to measure self‐reported outcomes has burgeoned in recent years. Perhaps the most important application of IRT is computer‐adaptive testing (CAT), a measurement approach in which the selection of items is tailored for each respondent.
To provide an introduction to the use of CAT in the measurement of health outcomes, describe several IRT models that can be used as the basis of CAT, and discuss practical issues associated with the use of adaptive scaling in research settings.
The development of a CAT requires several steps that are not required in the development of a traditional measure including identification of “starting” and “stopping” rules. CAT's most attractive advantage is its efficiency. Greater measurement precision can be achieved with fewer items. Disadvantages of CAT include the high cost and level of technical expertise required to develop a CAT.
Researchers, clinicians, and patients benefit from the availability of psychometrically rigorous measures that are not burdensome. CAT outcome measures hold substantial promise in this regard, but their development is not without challenges.
