Improved techniques for quantitatively comparing data visualizations.

David S. Pieczkiewicz, Luke V. Rasmussen, Justin B. Starren

Research output: Contribution to journalArticlepeer-review

Abstract

Visualizations are increasingly important in helping users manage large data streams. As a result, researchers often need to compare the performance of several visualizations. We present two statistical techniques, multiple-reader multiple-case receiver operating characteristic curve analysis, and generalized linear mixed models, to compare the accuracy and speed of decisions using data visualizations. These techniques have several advantages over simpler strategies for assessing decision quality, and should be made part of the quantitative evaluation of visualizations.

Original languageEnglish (US)
Pages (from-to)1095
Number of pages1
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
StatePublished - 2008

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