Eye tracker data quality

Abstract

Data quality is essential to the validity of research results and to the quality of gaze interaction. We argue that the lack of standard measures for eye data quality makes several aspects of manufacturing and using eye trackers, as well as researching eye movements and vision, more difficult than necessary. Uncertainty regarding the comparability of research results is a considerable impediment to progress in the field. In this paper, we illustrate why data quality matters and review previous work on how eye data quality has been measured and reported. The goal is to achieve a common understanding of what data quality is and how it can be defined, measured, evaluated, and reported.