A new remote guided method for supervised web-based cognitive testing to ensure high-quality data: development and usability study

• Main Authors: Leong, Victoria, Raheel, Kausar, Sim, Jia Yi, Kacker, Kriti, Karlaftis, Vasilis M., Vassiliu, Chrysoula, Kalaivanan, Kastoori, Chen, Annabel Shen-Hsing, Robbins, Trevor W., Sahakian, Barbara J., Kourtzi, Zoe
• Other Authors: School of Social Sciences
• Format: Article
• Language: English
• Published: 2022
• Subjects: Social sciences::General; Web-Based Testing; Neurocognitive Assessment
• Online Access: https://hdl.handle.net/10356/160157
• Institution: Nanyang Technological University

The global COVID-19 pandemic has triggered a fundamental reexamination of how human psychological research can be conducted safely and robustly in a new era of digital working and physical distancing. Online web-based testing has risen to the forefront as a promising solution for the rapid mass collection of cognitive data without requiring human contact. However, a long-standing debate exists over the data quality and validity of web-based studies. This study examines the opportunities and challenges afforded by the societal shift toward web-based testing and highlights an urgent need to establish a standard data quality assurance framework for online studies.