Advances in applications of item response theory to clinical assessment.

Abstract

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. Public Significance StatementItem response theory (IRT) is a methodology used to develop, evaluate, and score psychological tests. This article reviews the literature for recent advancements in the use of IRT as applied to clinical research and practice. IRT has contributed to major advancements in computerized adaptive testing, multidimensional modeling, and the identification of bias, but there are still many opportunities to further develop applications of the theory.

A signal detection–item response theory model for evaluating neuropsychological measures

“…As noted, the approach combines SDT scoring with IRT methods for evaluating tests and estimating scores. Thus, the full array of IRT applications (see Thomas, 2017) can be considered in future work. We envision several extensions of the approach.…”

“…Applications of IRT are becoming increasingly common in neuropsychology, with papers now appearing in the Journal of Clinical and Experimental Neuropsychology (e.g., Jahn, Dressel, Gavett, & O’Bryant, 2015), Neuropsychology (e.g., Kenzik et al, 2015), and the Archives of Clinical Neuropsychology (e.g., Li, Root, Atkinson, & Ahles, 2016) among other current periodicals. Nonetheless, the methodology is still somewhat rare in comparison to other domains of clinical assessment (Thomas, 2017). This could reflect a disconnect between models that are common in IRT and models that are common in neuropsychology, such as the equal variance SDT model.…”

Measuring Depression in Autistic Adults: Psychometric Validation of the Beck Depression Inventory–II

“…Thus, a secondary aim of the current study was to provide a free online scoring tool that allows non-experts to easily calculate latent trait scores from the BDI-II using the item parameters derived from our IRT model. The IRT approach also provides an elegant framework in which to assess DIF, testing whether item slope and/or intercept parameters differ between specific subgroups of interest (Thomas, 2019). Using this framework, the we aimed to determine whether the items of the BDI-II function differentially between adults with ASD and TD controls, empirically testing the claim that individuals with ASD answer depression questionnaires in a qualitatively different manner from the general population (Cassidy, Bradley, Bowen, et al, 2018;Gotham et al, 2015;Pezzimenti et al, 2019;Uljarević et al, 2018).…”

Optimal Examination Sites for Periodontal Disease Evaluation: Applying the Item Response Theory Graded Response Model

“…Item response theory (IRT) modeling is an important methodology commonly used for the development of tests to measure the ability by total score of test consisted of items with weighted scores [ 13 ]. Through the application of IRT, we can examine each item’s reliability and whether it contributes to an overall construct [ 14 , 15 ]. Total score correspond to the sum of the values of periodontal examinations.…”