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Visualizing missing data: Graph interpretation user study

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2005) 3585 LNCS 861-872
DOI: 10.1007/11555261_68
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Abstract

Most visualization tools fail to provide support for missing data. In this paper, we identify sources of missing data and describe three levels of impact missing data can have on the visualization: perceivable, invisible or propagating. We then report on a user study with 30 participants that compared three design variants. A between-subject graph interpretation study provides strong evidence for the need of indicating the presence of missing information, and some direction for addressing the problem. © IFIP International Federation for Information Processing 2005.

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Eaton, C., Plaisant, C., & Drizd, T. (2005). Visualizing missing data: Graph interpretation user study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3585 LNCS, pp. 861–872). https://doi.org/10.1007/11555261_68

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