The fundamentals of eye tracking, Part 7: Determining data quality
Diederick C Niehorster1,2, Marcus Nyström3, Roy S Hessels4
1Lund University Humanities Lab, Lund University, Lund, Sweden. diederick_c.niehorster@humlab.lu.se.
Behavior Research Methods
|June 2, 2026
Summary
This study explains how to assess eye-tracking data quality using accuracy, precision, and data loss metrics. It introduces ETDQualitizer, a tool for easy, privacy-preserving quality evaluation in eye tracking research.
Area of Science:
- Cognitive Science
- Human-Computer Interaction
- Psychology
Background:
- Eye-tracking data quality is vital for accurate research interpretation.
- Metrics like accuracy, precision, and data loss define recording quality.
- Reporting data quality is often required for publication.
Purpose of the Study:
- To provide an overview of operationalizing eye-tracking data quality metrics.
- To offer practical guidance for assessing eye-tracking data quality.
- To introduce ETDQualitizer for accessible data quality evaluation.
Main Methods:
- Review of operational definitions for accuracy, precision, and data loss.
- Provision of MATLAB, Python, and R code for calculating quality metrics.
- Development of a browser-based and library version of ETDQualitizer.
Main Results:
- ETDQualitizer offers a user-friendly, privacy-focused solution for assessing eye-tracking data quality.
- The tool is available as a standalone web application and as code libraries.
- Researchers can now easily determine, evaluate, and report on their eye-tracking data quality.
Conclusions:
- Empowering researchers to critically evaluate and report eye-tracking data quality is essential.
- Adopting a data quality perspective enhances the reliability of eye-tracking studies.
- ETDQualitizer facilitates widespread adoption of rigorous data quality assessment practices.
