Jim Burkman

Academics strive to bring real-world experiences and examples into the classroom thereby creating a richer experience for the instructor and student. This goal of relevance is particularly challenging in the instruction of statistics where the instructor often must choose between “canned” simulated datasets that lack richness and relevance versus using their own research data. Real-world research datasets offer familiarity, storytelling opportunities, and an intimate understanding of the dataset providing a fuller understanding for the student; however, the public release of research data could be problematic. This article examines a solution that offers the richness and relevance of real-world datasets while safeguarding the integrity of the data and research. Experimental results support the use of these derived datasets.

With the increased amount of data generated by social networking sites there is also increased difficulty in the analysis of this data, including time-based changes, which can provide unique insights in social network analysis. Information visualization is a vital tool in assisting social scientists with analysis of large quantities of data; however, the gathering, formatting, and visualizing of time-related data from social networking sites still remains an obstacle. This research explores the process of gathering time-based data in real time and using dynamic visualization techniques to visualize and analyze time-based changes in data generated by discussions on the social networking site Reddit. The outcome culminates in our deliverable, the Real Time Conversation Project. KEYWORDS: Visualization; Network Analysis; Social Network; Reddit; Gephi

This paper explains how visual representations of data enable individual sensemaking in data exploration tasks. We build upon theories of human perception and cognition, including Cognitive Fit Theory, to explain what aspects of visual representations facilitate sensemaking for the viewer. We make three primary contributions. First, we give a general characterization of visual representations that would be used for data exploration tasks. These representations consist of a scene, objects within the scene, and the characteristics of those objects. Second, we extend Cognitive Fit Theory into the data exploration task domain. We explain that the data exploration task has a number of spatial subtasks including observing data points, looking for patterns or outliers, making inferences, comparing observed facts or patterns to one’s own knowledge, generating hypotheses about the data, and drawing analogies from the context being observed to another context. Third, we offer a set of theoretical propositions about how visual representations of data can serve the sensemaking goal. Specifically, visual representations best facilitate sensemaking in data exploration tasks when they (1) support the four basic human visual perceptual approaches of association, differentiation, ordered perception, and quantitative perception, (2) have strong Gestalt properties, (3) are consistent with the viewer’s stored knowledge, and (4) support analogical reasoning. We propose that visual representations should possess several of these four aspects to make them well-suited for the task of data exploration.

The European Journal of Information Systems provides a distinctive European perspective on the theory and practice of information systems for a global audience. We encourage first rate research articles by academics, but also case studies and reflective articles by practitioners. We provide a critical view on technology, development, implementation, strategy, management and policy.