CHS: Small: Collaborative Research: Validating and Communiciating Model-Based Approaches for Data Visualization Ability Assessment

现在，人们比以往任何时候都更多地遇到图形、图表和其他数据的可视化表示。然而，这些可视化的创造者目前必须用稀疏和相互矛盾的证据来解释人们如何阅读他们发布的可视化。 目前的指南没有考虑到不同的人在解释视觉数据时有不同的优势和劣势的可能性。这个项目将使用可视化效果的研究，以告知我们的能力和观众的偏见，无论是个人和集体的理解。 为了做到这一点，项目团队将使用实验，统计建模和访谈研究的组合来挑战长期存在的关于可视化有效性的假设，并为未来的实验奠定基础，解释可视化阅读能力的差异。这项工作还将支持一个更广泛的教育目标，即在实验中使用强大的统计建模技术，通过课程模块，可以集成到现有的数据可视化课程，并通过推广活动，让个人看到他们如何执行可视化任务相比，其他人谁采取了实验。第一个是通过大规模众包实验来确定个人在数据可视化执行基本任务的能力方面的差异程度，这些实验使用透明的统计方法来建立数据可视化性能的个体差异。第二个问题评估低层次的可视化性能和更高层次的评估，如可视化素养和认知能力之间的关系，招募专家和新手人口，以评估这些假设的可视化素养的措施相互关联的程度。第三个问题确定如何替代方式呈现可视化实验结果形状的设计建议的研究人员和设计师从中得出，通过比较评估长期的方式呈现可视化实验结果，并通过设计新的方式呈现结果，可能会导致更成熟的解释实验结果更广泛的可视化社区。这项工作将通过增加图表阅读实验与可视化能力的新措施，并通过研究创作者目前如何利用现有的可视化设计准则在他们的设计过程中提供可视化素养的新视角。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

People are encountering graphs, charts, and other visual representations of data now more than ever before. Yet creators of these visualizations currently must reason with sparse and conflicting evidence on how well people can read the visualizations they publish. Current guidelines do not take into account the possibility that different people have different strengths and weaknesses when interpreting visual data. This project will use studies of visualization effectiveness to inform our understanding of the abilities and biases of viewers, both individually and collectively. To do this, the project team will use a combination of experiments, statistical modeling, and interview studies to both challenge long-standing assumptions about visualization effectiveness, and to lay a foundation for future experiments that account for differences in visualization reading ability. The work will also support a broader educational goal of using robust statistical modeling techniques in experimentation, through course modules that can be integrated into existing data visualization courses, and through outreach activities that allow individuals to see how well they perform visualization tasks compared to others who have taken the experiments.This work seeks to answer three primary research questions. The first is to determine the extent to which individuals differ in their ability to perform basic tasks with data visualizations, through large-scale crowdsourced experiments that use transparent statistical methodologies to establish individual differences in data visualization performance. The second question evaluates the relationship between low-level visualization performance and higher-level assessments such as visualization literacy and cognitive abilities, recruiting both expert and novice populations to evaluate the extent to which these hypothesized measures of visualization literacy correlate with each other. The third question determines how alternative ways of presenting visualization experiment results shape the design recommendations researchers and designers draw from them, through a comparative evaluation of longstanding ways of presenting visualization experiment results, and by designing new ways of presenting results that may lead to more mature interpretation of experiment results by broader visualization community. The work will provide new perspectives on visualization literacy by augmenting chart reading experiments with novel measures of visualization ability, and by studying how creators currently make use of existing visualization design guidelines in their design process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.