Understanding the Effects of Visualizing Missing Values on Visual Data Exploration
This addresses a practical problem for data analysts and visualization designers, but it is incremental as it builds on existing research about missing data handling.
The study investigated how visualizing missing values affects decision-making in visual data exploration, finding that participants' workflows differed when missing values were omitted versus shown with error bars.
When performing data analysis, people often confront data sets containing missing values. We conducted an empirical study to understand the effects of visualizing those missing values on participants' decision-making processes while performing a visual data exploration task. More specifically, our study participants purchased a hypothetical portfolio of stocks based on a dataset where some stocks had missing values for attributes such as PE ratio, beta, and EPS. The experiment used scatterplots to communicate the stock data. For one group of participants, stocks with missing values simply were not shown, while the second group saw such stocks depicted with estimated values as points with error bars. We measured participants' cognitive load involved in decision-making with data with missing values. Our results indicate that their decision-making workflow was different across two conditions.