Multidimensional Computerized Adaptive Testing for Rapidly Identifying Patients' Rehabilitative Care Needs

The contributions that technology and psychometrics can make to advance testing and assessment are becoming increasingly compelling and powerful. Multidimensional computerized adaptive testing (MCAT) as a new form of assessment method has gained popularity in many applications, such as health measurement. For example, the MCAT could improve patient report outcome measures because it can handle multiple disease symptoms. More importantly, the MCAT allows items to be tailored to the individual with great content validity, enhanced sensitivity, and reduced response burden. In this talk, I will introduce an ongoing project of MCAT that aims to rapidly identify patients’ rehabilitative care needs. I will elaborate on the psychometric sophistication behind the scenes, focusing on the item selection algorithm and stopping criteria. Finally, I will share the future use of the MCAT for monitoring clinically meaningful intra-individual change.

Chun Wang is currently an associate professor of quantitative psychology at the University of Minnesota. Her research is broadly situated in psychological and educational measurement, with specific devotion to methodology advancement that leads to better assessment with higher reliability/fidelity, fairness, and security. She is particularly interested in latent variable modeling and computerized adaptive testing/adaptive learning. She received the 2017 early career award from the Psychometric Society and the 2015 early career award from AERA Division D.

Tuesday, March 13, 2018 - 2:00pm
PDF icon Wang presentation Pt 11.79 MB
PDF icon Wang presentation Pt 21.46 MB