ABSTRACT

As called data democratization and data deluge, we are living in an era in which the amount of available data is multitudinous. People want to explore data, extract information form the data, and grasp insights from the data. Given this situation, the challenge that is being faced is how to facilitate the use of data in their process of making decisions. One way to overcome this challenge is to increase legibility by showing data through visualizations. By using visual aids, we can amplify the cognitive and analytical capabilities of people, and it allows them to complete tasks and make logical decisions with large and complex data. Even though data visualizations effectively and concisely represent complex data, it does not necessarily mean that data visualization users comprehend the visualizations well and appropriately read and interpret visually represented data. Thus, my doctoral research has focused on investigating the visualization literacy of users through a series of studies.

In Study 1, I qualitatively explored how people make sense of data visualizations as a first step toward visualization literacy. Visualization comprehension is one of the critical components of visualization literacy. Thus, I endeavored to identify data visualization sensemaking activities. I observed users when they tried to make sense of data visualizations and collected think-aloud data from the observation. I analyzed the data using a qualitative inquiry approach, the grounded theory method. Finally, I identified five salient cognitive activities (i.e., encountering visualization, constructing a frame, exploring visualization, questioning the frame, and floundering on visualization) and constructed a grounded model of a novice’s information visualization sensemaking. In Study 2, I developed a visualization literacy assessment test. In order to gain deeper understanding of visualization literacy, we should be able to measure and evaluate the human ability in a practical manner. However, we still lack instruments for measuring visualization literacy. In order to address this gap, I systematically developed a visualization literacy assessment test by following the established procedure of test development in Psychological and Educational Measurement. The instrument consisted of 53 multiple-choice items and covered 12 data visualization types and eight essential visualization tasks. I also showed the validity and reliability of the instrument by conducting the expert evaluation and the test tryout. Lastly, in Study 3, I examined the effects of cognitive characteristics on visualization literacy. In particular, I focused on three cognitive characteristics: numeracy as a cognitive ability, the need for cognition as cognitive motivation, and visualizer-verbalizer as a cognitive style. I measured users' visualization literacy and the three cognitive characteristics using the visualization literacy assessment test (VLAT), the decision research numeracy test (DRNT), the need for cognition scale (NCS), and the verbalizer-visualizer questionnaire (VVQ), and analyzed the measurement scores. From the analysis, I confirmed that an individual's numeracy and need for cognition influenced his/her visualization literacy, but visualizer-verbalizer cognitive style did not.