Unifying Inconsistencies in Children Empathy Assessment Using a Multimethod Assessment Approach

• In Event: AERA Virtual Poster Session 4
  In Poster Session: Benefits of Technology in Facilitating Mixed and Multi-Method Research

Abstract

The measurement of empathy in children lacks a standardized framework, which has contributed to inconsistent findings amongst research studies in the subject matter. This study evaluated the conventional methods of measuring empathy, including both parent-reporting and self-reporting by children. A machine learning-driven method, known as automated Facial Action Coding System (FACS), was also evaluated in the same study involving 78 children. The study has demonstrated FACS provides a statistically significant predictive value to measuring empathy in children, as verified in comparisons with parent-reported and children self-reported results. This is the first study to verify the accuracy of FACS in assessing children’s empathy by comparing it with other conventional methods.

Authors

• Ying Yao, University of Toronto

• Sarah Zanette, University of Regina

• Kang Lee

• Earl Woodruff, OISE/University of Toronto

• Qianqing Liu, University of Toronto

• Minghui Wang, University of Southern California

• Xingyu Li, Stanford University