@article{e221f032-b649-458a-925b-45d2ffd6fb5d,
  abstract     = {{Understanding the quality of eye-tracking recordings, often characterized using accuracy, precision, and data loss, is crucial for the interpretation of eye tracking data. Eye-tracking data quality can furthermore place fundamental limits on what studies can be conducted with an eye tracker, and one may be required to report eye-tracking data quality when publishing a study. However, how does one determine the quality of eye-tracking data? This article provides an overview of operationalizations of accuracy, precision, and data loss and practical advice for determining eye-tracking data quality. Furthermore, the programming code for calculating various quality metrics for a segment of eye-tracking data is provided in MATLAB, Python, and R. Also provided is ETDQualitizer, a tool designed to enable anyone to easily determine the data quality of their recordings. We provide a version that is browser-based (https://dcnieho.github.io/ETDQualitizer) and enables determining eye-tracking data quality without installation or programming, while ensuring data privacy by running entirely locally. ETDQualitizer is further provided as a MATLAB, Python, and R library (https://github.com/dcnieho/ETDQualitizer) that can be integrated in one’s analysis scripts. We hope that this article enables any researcher to determine, critically evaluate, and report on eye-tracking data quality, and that it spurs researchers to adopt a data quality perspective in all their future eye-tracking studies.}},
  author       = {{Niehorster, Diederick C. and Nyström, Marcus and Hessels, Roy S. and Benjamins, Jeroen S. and Andersson, Richard and Hooge, Ignace T. C.}},
  issn         = {{1554-3528}},
  keywords     = {{Eye tracking; Data quality; Accuracy; Precision; Data loss; Validation; Tools; Software}},
  language     = {{eng}},
  number       = {{183}},
  publisher    = {{Springer}},
  series       = {{Behavior Research Methods}},
  title        = {{The fundamentals of eye tracking, Part 7 : Determining data quality}},
  url          = {{http://dx.doi.org/10.3758/s13428-026-03039-4}},
  doi          = {{10.3758/s13428-026-03039-4}},
  volume       = {{58}},
  year         = {{2026}},
}