Paper Reading #20: The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only ANOVA Procedures

Reference Information

The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only ANOVA Procedures
Jacob O. Wobbrock, Leah Findlater, Darren Gergle, James J. Higgins

Presented at: CHI 2011 May 7–12, 2011 Vancouver, BC, Canada

Author Bios

• Jacob O. Wobbrock is an Associate Professor at the University of Washington and has a PhD from  Carnegie Mellon University.
• Leah Findlater will be an Assistant Professor at the University of Maryland next year, has taught at the University of Washington, and has a PhD from the University of British Columbia.
• Darren Gergle is an Associate Professor at Northwestern University and has a PhD from Carnegie Mellon University.
• James J. Higgins is a Professor of Statistics at Kansas State University and has a PhD from the University of Missouri-Columbia.

Summary
Hypothesis
That current techniques and procedures used for analyzing the results of studies in HCI are inadequate, and that the Aligned Rank Transform yields better, clearer results with less work.

Methods
The authors used their software with data sets from published HCI work and compared the results with the original authors' findings. One case evaluated the use of ART to provide interaciton effects. The second showed how ART  is not bound to the distributional assumptions of ANOVA. The third is nonparametric testing of repeated measures data.

Results
The authors looked into three different ART procedures to show their applicability. One case showed how the usage of ART can show interaction effects that might not be shown with Friedman tests. One case showed how it can allow analysts from going through distributional assumptions of ANOVA. The last case showed nonparametric tests of repeatedly measured data.

Contents
The authors presented their Aligned Rank Transform tool, which useful for the nonparametric analysis of factorial experiments and makes use of the familiar F-test.  They discuss the exact process in detail, then go on to show three examples of where it could prove useful and effective with real data.

Discussion
This paper was mainly over my head, but the gist of it seemed that the author created a new method of statistical analysis that makes conclusions more valid and easier to find. I think this is a good thing.