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The fundamentals of eye tracking, Part 7 : Determining data quality

Niehorster, Diederick C. LU orcid ; Nyström, Marcus LU orcid ; Hessels, Roy S. ; Benjamins, Jeroen S. ; Andersson, Richard LU and Hooge, Ignace T. C. LU (2026) In Behavior Research Methods 58(183).
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.... (More)
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. (Less)
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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Eye tracking, Data quality, Accuracy, Precision, Data loss, Validation, Tools, Software
in
Behavior Research Methods
volume
58
issue
183
pages
25 pages
publisher
Springer
ISSN
1554-3528
DOI
10.3758/s13428-026-03039-4
language
English
LU publication?
yes
id
e221f032-b649-458a-925b-45d2ffd6fb5d
date added to LUP
2026-06-02 23:08:23
date last changed
2026-06-04 16:12:44
@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}},
}