TY - GEN
T1 - The perils of confounding factors
T2 - 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018
AU - Gori, Julien
AU - Rioul, Olivier
AU - Guiard, Yves
AU - Beaudouin-Lafon, Michel
N1 - Publisher Copyright:
© 2018 ACM.
PY - 2018/4/20
Y1 - 2018/4/20
N2 - 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.
AB - 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.
KW - Factor confounds
KW - Fitts' law
KW - Models
U2 - 10.1145/3173574.3173770
DO - 10.1145/3173574.3173770
M3 - Conference contribution
AN - SCOPUS:85046970038
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 21 April 2018 through 26 April 2018
ER -