title: Improved techniques for quantitatively comparing data visualizations.
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.
authors.affiliation: Biomedical Informatics Research Center, Marshfield Clinic, Marshfield, WI, USA.
authors.forename: David S
authors.initials: DS
authors.lastname: Pieczkiewicz
authors.forename: David Sean
authors.initials: DS
authors.lastname: Pieczkiewicz
authors.forename: Luke V
authors.initials: LV
authors.lastname: Rasmussen
authors.forename: Justin B
authors.initials: JB
authors.lastname: Starren
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016247:Information Storage and Retrieval / Q000379:methods; D016014:Linear Models; D012372:ROC Curve; D014584:User-Computer Interface; D014922:Wisconsin