Publications
論文誌 (国際) Relative Merits of Nominal and Effective Indexes of Difficulty of Fitts' Law: Effects of Sample Size and the Number of Repetitions on Model Fit
International Journal of Human–Computer Interaction (IJHCI)
2024.1.14
Two formulations of Fitts' index of difficulty ID are empirically compared under different subjective speed-accuracy biases: the nominal form ID_n and the effective form ID_e using endpoint variability. The effective forms have typically been considered beneficial for capturing the actual accuracy of users’ performance, while the nominal form is better for single-biased data. In our analysis of the data from 210 crowdworkers, the best model tended to switch. At times, this switch was statistically significant, especially when limited portions of the entire workers and trials were used, such as the first eight clicks (out of 16) performed by 20 workers who were randomly sampled from a comprehensive group of 210 participants. Our findings caution against assuming a model's capability based on only a few experiments using a limited number of participants or just a few trials. They also emphasize the need for performing replications on even well-investigated models.
Paper :
Relative Merits of Nominal and Effective Indexes of Difficulty of Fitts' Law: Effects of Sample Size and the Number of Repetitions on Model Fit
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