LAUSR

Item response theory (IRT) is moving to the forefront of methodologies used to develop, evaluate, and score clinical measures. Funding agencies and test developers are routinely supporting IRT work, and the theory has become closely tied to technological advances within the field. As a result, familiarity with IRT has grown increasingly relevant to mental health research and practice. But to what end? This article reviews advances in applications of IRT to clinical measurement in an effort to identify tangible improvements that can be attributed to the methodology. Although IRT shares similarities with classical test theory and factor analysis, the approach has certain practical benefits, but also limitations, when applied to measurement challenges. Major opportunities include the use of computerized adaptive tests to prevent conditional measurement error, multidimensional models to prevent misinterpretation of scores, and analyses of differential item functioning to prevent bias. Whereas these methods and technologies were once only discussed as future possibilities, they are now accessible because of recent support of IRT-focused clinical research. Despite this, much work still remains in widely disseminating methods and technologies from IRT into mental health research and practice. Clinicians have been reluctant to fully embrace the approach, especially in terms or prospective test development and adaptive item administration. Widespread use of IRT technologies will require continued cooperation among psychometricians, clinicians, and other stakeholders. There are also many opportunities to expand the methodology, especially with respect to integrating modern measurement theory with models from personality and cognitive psychology as well as neuroscience. (PsycINFO Database Record (c) 2019 APA, all rights reserved).