Applying Modern Psychometrics and Technology to Neuropsychological Assessments: Item Response Theory & Computerized Adaptive Testing

Neuropsychological assessments, including their clinical applications, have evolved significantly from their inception, and this is thanks to the improvements in psychometrics and technology (Thomas & Duffy, 2023), which made it possible to better diagnose and measure treatment outcomes for neurological and psychiatric

disorders. Despite these advancements, it has not been as successful in catching up with the advancement in these two fields that it heavily relies on (Bilder & Reise, 2019). It is vital for neuropsychological assessment, especially in clinical settings, to transfer the advancement in psychometrics & technology to its domain. The National Neuropsychology Network, which is a "multicenter

clinical research initiative funded by the National Institute of Mental Health", its whole aim is to "facilitate neuropsychology's transition to contemporary psychometric assessment methods with resultant improvement in test validation and assessment efficiency." (Loring et al., 2022), and it aims to create an international

netwoek focused on that. The importance of psychometric tools and need for utilizing modern psychometrics in neuropsychological assessment is documented in recent research (Bilder & Reise, 2019; Hessen et al., 2019; Loring et al., 2022; Thomas & Duffy, 2023). One of the biggest, if not the biggest, issues with neuropsychological batteries is construct measurement, it is

very difficult to develop a valid and reliable measurement of what the test is meant to measure (i.e., measuring the latent variable), this has led to a problem of tests not accurately reflecting an intended construct, or high overlaps (in an undesired manner) among items within a test, or between different tests

trying to measure the same construct. This array of diverse tests and tasks is a hurdle in the way of combining scales and items into more (and better) standardized tests that allow for better comparability and generalizability (Bilder et al., 2023).