We introduce grounded evaluation as a process that attempts to ensure that the evaluation of an information visualization tool is situated within the context of its intended use. We discuss the process and scope of grounded evaluation in general, and then describe how qualitative inquiry may be a beneficial approach as part of this process. We advocate for increased attention to the field of qualitative inquiry early in the information visualization development life cycle, as it tries to achieve a richer understanding by using a more holistic approach considering the interplay between factors that influence visualizations, their development, and their use. We present three case studies in which we successfully used observational techniques to inform our understanding of the visual analytics process in groups, medical diagnostic reasoning, and visualization use among computational linguists.

Heuristic evaluation is a well known discount evaluation technique in human-computer interaction (HCI) but has not been utilized in information visualization (InfoVis) to the same extent. While several sets of heuristics have been used or proposed for InfoVis, it is not yet known what kind of heuristics are useful for finding general InfoVis problems. We performed a meta-analysis with the goal of exploring the issues of heuristic evaluation for InfoVis. This meta-analysis concentrates on issues pertaining to the selection and organization of heuristics, and the process itself. For this purpose, we used three sets of previously published heuristics to assess a visual decision support system that is used to examine simulation data. The meta-analysis shows that the evaluation process and results have a high dependency on the heuristics and the types of evaluators chosen. We describe issues related to interpretation, redundancy, and conflict in heuristics. We also provide a discussion of generalizability and categorization of these heuristics.

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Abstract. This article gathers and consolidates the issues involved in uncertainty relating to reasoning and analyzes how uncertainty visualizations can support cognitive and meta-cognitive processes. Uncertainty in data is paralleled by uncertainty in reasoning processes, and while uncertainty in data is starting to get some of the visualization research attention it deserves, the uncertainty in the reasoning process is thus far often overlooked. While concurring with the importance of incorporating data uncertainty visualizations, we suggest also developing closely integrated visualizations that provide support for uncertainty in reasoning.

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