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The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics.

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    This study analyzes visual analytics (VA) evaluation methods, proposing a new metric "summative quality" to compare their effectiveness in proving VA solution usefulness.

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    Area of Science:

    • Computer Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Numerous methods exist to evaluate Visual Analytics (VA) solutions, leading to confusion regarding their proofing capabilities.
    • A lack of discussion on evaluation processes hinders the development of specialized VA evaluation methods.

    Purpose of the Study:

    • To analyze and categorize summative evaluation methods for VA solutions.
    • To propose a new metric, "summative quality," for comparing VA evaluation methods.
    • To provide recommendations for selecting appropriate VA evaluation methods.

    Main Methods:

    • Surveying and taxonomizing VA evaluation methods from recent VAST literature.
    • Analyzing methods based on validity, generalizability, and feasibility.
    • Developing and applying the "summative quality" metric.

    Main Results:

    • Identified a variety of VA evaluation methods with differing assumptions and goals.
    • Established a taxonomy of VA evaluation methods from the past two years.
    • Proposed "summative quality" as a metric to assess the proofing ability of evaluation methods.

    Conclusions:

    • The proposed "summative quality" metric aids in comparing the effectiveness of VA evaluation methods.
    • Recommendations are provided for selecting evaluation methods based on their summative quality.
    • This work aims to improve the development and application of VA evaluation strategies.