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Testing for statistically significant differences between groups of scan patterns
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Eye Tracking Research & Application archive
Proceedings of the 2008 symposium on Eye tracking research & applications table of contents
Savannah, Georgia
SESSION: Late breaking results: oral presentations table of contents
Pages 43-46  
Year of Publication: 2008
ISBN:978-1-59593-982-1
Authors
Matt Feusner  University of California, San Francisco
Brian Lukoff  Stanford University
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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ABSTRACT

Pairwise sequence alignment methods are now often used when analyzing eyetracking data [Hacisalihzade et al. 1992; Brandt and Stark 1997; Josephson and Holmes 2002, 2006; Pan et al. 2004; Heminghous and Duchowski 2006]. While optimal sequence alignment scores provide a valuation of similarity and difference, they do not readily provide a statistical test of similarity or difference. Furthermore, pairwise alignment scores cannot be used to compare groups of scan patterns directly. Using a statistic that compiles these pairwise alignment scores, a statistical evaluation of similarity can be made by repeatedly computing scores from different permutations of scan pattern groupings. This test produces a p-value as a level of statistical significance.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Matt Feusner: colleagues
Brian Lukoff: colleagues