Wong, Caroline2020-08-282020-08-282020-08-282020-08-21http://hdl.handle.net/10012/16187With my two exploratory studies I contribute a deeper understanding of the different experiences people have when manipulating data representations using mouse, touch, and physical interaction. To uncover experiences rather than performance measures I employed two different methodologies in the context of “data connectedness.” My first study used Likert-based questionnaires to determine differences in how connected participants felt to the data they were interacting with. To gain a deeper understanding, my second study employed a word selection activity (using the Desirability Toolkit), which led to much richer data. I found that people associated words like “engaged,” “direct,” and “satisfying” with touch and physical interaction, but often used words like “awkward,” “dull,” and “distant” with the mouse. My findings help to tease apart the characteristics of experienced interaction modalities in relation to how people feel about their connection to the data. Furthermore, my work provides a deeper look into how to measure abstract concepts such as connectedness that are highly elusive but important to understanding why certain ways of interacting with data may be more attractive, more liked, or even more effective.eninteractionvisualizationmousetouchphysicalexploratory studyThe Desirability ToolkitconnectednessMaster Thesis