Reporting and Visualizing Fitts's Law

Abstract

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.