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Cited by 664

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An Aligned Rank Transform Procedure for Multifactor Contrast Tests

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Elkin, Lisa A., Kay, Matthew, Higgins, James J., Wobbrock, Jacob O.·2021·The 34th Annual ACM Symposium on User Interface Software and Technology
DOI 10.1145/3472749.3474784
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Abstract

Data from multifactor HCI experiments often violates the assumptions of parametric tests (i.e., nonconforming data). The Aligned Rank Transform (ART) has become a popular nonparametric analysis in HCI that can find main and interaction effects in nonconforming data, but leads to incorrect results when used to conduct post hoc contrast tests. We created a new algorithm called ART-C for conducting contrast tests within the ART paradigm and validated it on 72,000 synthetic data sets. Our results indicate that ART-C does not inflate Type I error rates, unlike contrasts based on ART, and that ART-C has more statistical power than a t-test, Mann-Whitney U test, Wilcoxon signed-rank test, and ART. We also extended an open-source tool called ARTool with our ART-C algorithm for both Windows and R. Our validation had some limitations (e.g., only six distribution types, no mixed factorial designs, no random slopes), and data drawn from Cauchy distributions should not be analyzed with ART-C.

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Cited by

Metadata

Authors
Elkin, L. A., Kay, M., Higgins, J. J., & Wobbrock, J. O.
Venue
The 34th Annual ACM Symposium on User Interface Software and Technology
Publisher
ACM
Published
2021-10-10
Pages
754-768
DOI
10.1145/3472749.3474784
OpenAlex
W3133381051
Cited by
664
References
6
FWCI
176.01
Type
conference paper
Language
en
Open access
green
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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