Date of Award
Master of Science (MS)
Danielle Albers. Szafir
Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. I conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may bias perceptions of data quality. The experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. The results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.
Song, Hayeong, "Measuring the Role of Visualization on Missing Values in Time Series Data" (2018). Computer Science Graduate Theses & Dissertations. 170.
Available for download on Sunday, October 10, 2021