The Perils of Confounding Factors: How Fitts’ Law Experiments can Lead to False Conclusions

User Research Methods (Interviews, Surveys, Observation)Computational Methods in HCI

The design of Fitts' historical reciprocal tapping experiment gravely confounds index of difficulty ID with target distance D: Summary statistics for the candidate Fitts model and a competing model may appear identical, and the validity of Fitts' model for some tasks can be legitimately questioned. We show that the contamination of ID by either target distance D or width W is due to the common practices of pooling and averaging data belonging to different distance-width (D,W) pairs for the same ID, and taking a geometric progression for values of D and W. We analyze a case study of the validation of Fitts' law in eye-gaze movements, where an unfortunate experimental design has misled researchers into believing that eye-gaze movements are not ballistic. We then provide simple guidelines to prevent confounds: Practitioners should carefully design the experimental conditions of (D,W), fully distinguish data acquired for different conditions, and put less emphasis on r² scores. We also recommend investigating the use of stochastic sampling for D and W.

Quick Actions

Share

Share this page

ios_share

https://hci.top/en/papers/chi/6597/2018

AdRecommended

Learn AI Coding at CodeNow

At a Glance

Paper Snapshot

fact_check
dataset
Source
CHI
calendar_month
Year
2018
emoji_events
Award
No award tagged
group
Authors
4 authors
sell
Subtopics
User Research Methods (Interviews, Surveys, Observation), Computational Methods in HCI
work
Professions
—
article
Content Status
Abstract only
hub
Related Papers
3 related papers