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Where's My Data? Evaluating Visualizations with Missing Data.

Hayeong Song, Danielle Albers Szafir

    IEEE Transactions on Visualization and Computer Graphics
    |August 24, 2018
    PubMed
    Summary
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    Visualizing incomplete data impacts analyst confidence. Different chart designs for imputation and visualization methods affect perceptions of data quality and conclusions drawn from time series datasets.

    Area of Science:

    • Data Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Real-world datasets frequently contain missing values due to collection errors or data integration issues.
    • Effective visualization of incomplete data is crucial for analysts to make sound judgments while understanding data limitations.

    Purpose of the Study:

    • To investigate how different imputation and visualization techniques for incomplete datasets influence analysts' perceptions of data quality.
    • To assess the impact of visualization design choices on analysts' confidence in conclusions drawn from incomplete time series data.

    Main Methods:

    • Conducted two crowdsourced studies involving participants analyzing incomplete time series datasets.
    • Experimented with various design choices for line graphs and bar charts to represent imputed and missing data.

    Related Experiment Videos

  • Collected data on participants' perceptions of data quality and their confidence in estimated averages and trends.

    • Main Results:

    • The methods used for data imputation and visualization significantly affected how analysts perceived data quality.
    • Specific design choices in line graphs and bar charts influenced analysts' confidence levels in their conclusions.
    • Found variations in perception based on the type of visualization and imputation strategy employed.

    Conclusions:

    • Results offer preliminary guidance for designing effective visualizations for incomplete data.
    • Highlights the importance of considering visualization design when presenting data with missing values.
    • Emphasizes the need for analysts to be aware of how visualization choices can shape their interpretation of data quality and findings.