Testing for statistically significant differences between groups of scan patterns

  • Authors:

  • Matt Feusner;Brian Lukoff

  • Affiliations:

  • University of California, San Francisco;Stanford University

  • Venue:
  • Proceedings of the 2008 symposium on Eye tracking research & applications
  • Year:
  • 2008

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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.