More on online experience analytics.
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// published on Conference on Human Factors in Computing Systems-Latest Proceeding Volume // visit site
Non-parametric decision trees and online HCI
Torben Sko, Henry J. Gardner, Michael Martin
This paper proposes that online HCI studies (such as web-surveys and remotely monitored usability tests) can benefit from statistical data analysis using modern statistical learning methods such as classification and regression trees (CARTs). Applying CARTs to the often large amount of data yielded by online studies can easily provide clarity concerning the most important effects underlying experimental data in situations where myriad possible factors are under consideration. The feedback provided by such an analysis can also provide valuable reflection on the experimental methodology.
This paper proposes that online HCI studies (such as web-surveys and remotely monitored usability tests) can benefit from statistical data analysis using modern statistical learning methods such as classification and regression trees (CARTs). Applying CARTs to the often large amount of data yielded by online studies can easily provide clarity concerning the most important effects underlying experimental data in situations where myriad possible factors are under consideration. The feedback provided by such an analysis can also provide valuable reflection on the experimental methodology.