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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
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Dispersion vs Disparity: Hiding Variability Can Encourage Stereotyping When Visualizing Social Outcomes.

Eli Holder, Cindy Xiong

    IEEE Transactions on Visualization and Computer Graphics
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    Summary
    This summary is machine-generated.

    Data visualization design impacts social perceptions. Charts hiding data variability can increase stereotyping, while those emphasizing variability reduce it, influencing how we understand social inequality.

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    Area of Science:

    • Data Visualization
    • Social Psychology
    • Human-Computer Interaction

    Background:

    • Research often overlooks how chart designs influence perceptions of individuals represented in data.
    • Chart designs may interact with social cognitive biases, potentially perpetuating harmful stereotypes.
    • Bar charts, common for social inequality data, may promote deficit thinking by attributing disparities to personal factors rather than external causes.

    Purpose of the Study:

    • To investigate how specific data visualization design choices influence attribution biases.
    • To examine if chart designs can mitigate or exacerbate deficit thinking and stereotyping in social inequality contexts.

    Main Methods:

    • Conducted four experiments using crowdworkers.
    • Participants viewed visualizations (bar charts, dot plots, jitter plots, prediction intervals) of social outcomes.
    • Evaluated agreement with personal vs. external explanations for visualized disparities.

    Main Results:

    • Visualizations masking within-group variability (e.g., bar charts) led to greater agreement with personal explanations.
    • Visualizations emphasizing within-group variability (e.g., jitter plots) led to less agreement with personal explanations.
    • Design choices significantly influenced attribution biases and potential for stereotyping.

    Conclusions:

    • Data visualizations of social inequity can be misinterpreted, leading to harmful stereotyping.
    • Hiding data variability in visualizations increases stereotyping; emphasizing variability reduces it.
    • Conscious design choices in data visualization can mitigate biased interpretations and reduce stereotyping.