Rank-score tests in factorial designs with repeated measures
Journal of Multivariate Analysis
Interaction Design
The interplay of beauty, goodness, and usability in interactive products
Human-Computer Interaction
Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics
Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics
The aligned rank transform for nonparametric factorial analyses using only anova procedures
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
Likert-type scales, statistical methods, and effect sizes
Communications of the ACM
Rethinking statistical analysis methods for CHI
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Statistics from an HCI perspective: Illmo - interactive log likelihood modeling
Proceedings of the International Working Conference on Advanced Visual Interfaces
Towards adaptive information visualization: on the influence of user characteristics
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Evaluating rating scales personality
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
ACM Transactions on Computer-Human Interaction (TOCHI)
Journal of Ambient Intelligence and Smart Environments - Context Awareness
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Likert-type scales are used extensively during usability evaluations, and more generally evaluations of interactive experiences, to obtain quantified data regarding attitudes, behaviors, and judgments of participants. Very often this data is analyzed using parametric statistics like the Student t-test or ANOVAs. These methods are chosen to ensure higher statistical power of the test (which is necessary in this field of research and practice where sample sizes are often small), or because of the lack of software to handle multi-factorial designs nonparametrically. With this paper we present to the HCI audience new developments from the field of medical statistics that enable analyzing multiple factor designs nonparametrically. We demonstrate the necessity of this approach by showing the errors in the parametric treatment of nonparametric data in experiments of the size typically reported in HCI research. We also provide a practical resource for researchers and practitioners who wish to use these new methods.