Paper Reading #20:The aligned rank transform for nonparametric factorial analyses using only anova procedures

References

Jacob O. Wobbrock, Leah Findlater, Darren Gergle, and James J. Higgins.  "Reflexivity in digital anthropology". UIST '11 Proceedings of the 23rd annual ACM symposium on User interface software and technology.  ACM New York, NY, USA ©2011.

 Author Bios

Jacob Wobbrock is an associate professor in the Information School at the University of Washington.  He directs the AIM Research Group which is part of the DUB Group.

Leah Findlater is currently a professor at the University of Washington.

Darren Gergle is an associate professor at the Northwestern University School of Communication.

James Higgins is a professor in the Department of Statistics at Kansas State Unversity. 

Summary 

• Hypothesis - The researchers hypothesized that modifying the Aligned Rank Transform to support an arbitrary number of factors would be useful for researchers in analyzing data.
• Method - The researchers developed the method for the expanded ART and then created a desktop tool (ARTool) and a Java-based verson (ARTWeb). After creating these tools the researchers analyzed three sets of previously published data. This analysis allowed them to show the effectiveness and usability of their software.
• Results - The results were positive. Reexamining old studies showed data that had not shown up before. For one of them, data was found that was unexaminable by a Friedman test. The second case showed how the new system can free analysts from assuming distributions of ANOVA. The last was run using the nonparametric ART method, new information was revealed.
• Content - The authors presented their Aligned Rank Transform (ART) tool, which is useful for nonparametric analysis of factorial experiments. They discuss the process in detail, and show three examples of how it is useful and where it is applicable. It is shown that this tool can show some relationships between variables that cannot be seen with other analyses.

 Discussion

Honestly, this paper went way over my head. It did seem obvious to me, however, that the authors were able to effectively support their hypothesis and were able to create a very useful tool for analysts. The amount of information analysts gets out of data greatly affects their ability to extrapolate. I thought their examples were well chosen and explained well (even on a broad spectrum) how the ART system can produce more specific and more accurate results.