Reporting and Visualizing Fitts's Law: Dataset, Tools and Methodologies
Late-Breaking Works: Novel Interactions
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Jude, Alvin
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Guinness, Darren
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Poor, G. Michael
Extended Abstracts of the ACM CHI'16 Conference on Human Factors in
Computing Systems
2016-05-07
v.2
p.2519-2525
© Copyright 2016 ACM
Summary: In this paper we compare methods of reporting and visualizing Fitts
regressions. We show that reporting this metric using mean movement time per
user over accuracy-adjusted Index of Difficulty (IDe) produces more descriptive
visualization. This method displays variance, which is more useful in
understanding the interfaces, than an aggregated means-of-means approach using
Index of Difficulty. We demonstrate that there is little difference in slope
and intercept between the two methods, but has the potential to uncover wider
goodness-of-fit coefficients which could allow for better comparison across
experiments. We propose the use of quantile regression to report central
tendencies as a trend, rather than box plots. The tools released with this
paper can be used with any pointing device evaluation done with the FittsStudy
program. The dataset released with this paper contains almost 25,000 samples,
which can be used in future research for reporting or visualizing Fitts
regressions.