Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2011
DOI: 10.1145/1978942.1978963
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The aligned rank transform for nonparametric factorial analyses using only anova procedures

Abstract: Nonparametric data from multi-factor experiments arise often in human-computer interaction (HCI). Examples may include error counts, Likert responses, and preference tallies. But because multiple factors are involved, common nonparametric tests (e.g., Friedman) are inadequate, as they are unable to examine interaction effects. While some statistical techniques exist to handle such data, these techniques are not widely available and are complex. To address these concerns, we present the Aligned Rank Transform (… Show more

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Cited by 1,841 publications
(1,024 citation statements)
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References 16 publications
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“…Using a non-parametric ANOVA [21], we also found significant main effect of health literacy on trust (p=.007) and a marginally significant interaction between health literacy and number of interactions with the agent (single vs. multiple) on trust (p=.08). In general, low health literacy participants trusted the agent more, and trust in the agent increased after more interactions with the agent, but only for patients with low health literacy ( Figure 6).…”
Section: Literacymentioning
confidence: 72%
“…Using a non-parametric ANOVA [21], we also found significant main effect of health literacy on trust (p=.007) and a marginally significant interaction between health literacy and number of interactions with the agent (single vs. multiple) on trust (p=.08). In general, low health literacy participants trusted the agent more, and trust in the agent increased after more interactions with the agent, but only for patients with low health literacy ( Figure 6).…”
Section: Literacymentioning
confidence: 72%
“…The 2-factor interaction 'Waste content * Silane content' also has a non-disregarded contribution to global variation. Though, caution should be taken when analysing the interaction effects results obtained by Kruskal-Wallis test as this analysis may be unable to properly identify interaction effects when multiple factors are involved (Wobbrock et al, 2011).…”
Section: Discussionmentioning
confidence: 99%
“…Though, caution should be taken when analysing the interaction effects results obtained by Kruskal-Wallis test as this analysis may be unable to identify interaction effects when multiple factors are involved (Wobbrock et al, 2011).…”
Section: Compressive Strength (Mpa)mentioning
confidence: 99%