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Cited by 353

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Eye tracker data quality: what it is and how to measure it

Holmqvist, Kenneth, Nyström, Marcus, Mulvey, Fiona·2012·Proceedings of the Symposium on Eye Tracking Research and Applications
DOI 10.1145/2168556.2168563
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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.

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Metadata

Authors
Holmqvist, K., Nyström, M., & Mulvey, F.
Venue
Proceedings of the Symposium on Eye Tracking Research and Applications
Publisher
ACM
Published
2012-03-28
Pages
45-52
DOI
10.1145/2168556.2168563
OpenAlex
W2082490348
Cited by
353
References
14
FWCI
7.65
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Eye tracker data quality: what it is and how to measure it. https://app.cochat.ai/papers/10.1145/2168556.2168563
The paper
Holmqvist, K., Nyström, M., & Mulvey, F. (2012). Eye tracker data quality: what it is and how to measure it. Proceedings of the Symposium on Eye Tracking Research and Applications. https://doi.org/10.1145/2168556.2168563

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.