A Visual-Interactive Idiom to Diagnose Missing Data Mechanisms

With vast amounts of data, comes vast numbers of problems. The process of collecting data is far from perfect, either due to human factors or technological errors, which can lead to inaccuracies and uncertainties in the data. One such issue is missing data: the absence of information. Several methods can deal with missing values, but to choose the correct approach, it is necessary to diagnose the missing data mechanisms, which describe how the distribution of missingness in a given data variable correlates to other variables. This diagnosis can be made with statistical tests or data visualization techniques. However, statistical tests provide an uncertainty estimation that is often misinterpreted, and the visualizations readily available in data analysis packages have some scalability issues, such as cognitive overload and lack of screen space. Thus, this paper proposes a visual-interactive idiom for diagnosing missing data mechanisms. The proposed solution consists of a set of visual encodings and two derived metrics that synthesizes the missing data mechanisms and the uncertainty associated with this synthesis. We present the concepts behind the visual encodings, derived metrics, and interactions of the idiom.