Alzheimer’s disease (AD) affects neurological, cognitive, and behavioral processes. Thus, to accurately assess this disease, researchers and clinicians need to combine and incorporate data across these domains. This presents not only distinct methodological and statistical challenges but also unique opportunities for the development and advancement of psychometric techniques. In this article, we describe relatively recent research using item response theory (IRT) that has been used to make progress in assessing the disease across its various symptomatic and pathological manifestations. We focus on applications of IRT to improve scoring, test development (including cross-validation and adaptation), and linking and calibration. We conclude by describing potential future multidimensional applications of IRT techniques that may improve the precision with which AD is measured.

When asked to predict how they will perform on an upcoming exam, students are often poorly calibrated, typically in the direction of overpredicting their performance. Research shows that low-performing students’ calibration tends to remain poor across multiple tests over the course of a semester. We tested whether these students remain confident in these erroneously high grade predictions across the semester or whether their confidence wanes, suggesting some degree of metacognitive awareness. In two studies, students made grade predictions prior to taking four in-class exams and then rated their level of confidence in their predictions. Results from both studies showed that miscalibration and confidence remained stable across tests, suggesting that low-performing students continued to believe that they would perform well on upcoming exams despite prior evidence to the contrary.