We developed and validated an instrument to measure the perceived readability in data visualization: PREVis. Researchers and practitioners can easily use this instrument as part of their evaluations to compare the perceived readability of different visual data representations. Our instrument can complement results from controlled experiments on user task performance or provide additional data during in-depth qualitative work such as design iterations when developing a new technique. Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns. We provide the questionnaire as a document with implementation guidelines on osf.io/9cg8j. Beyond this instrument, we contribute a discussion of how researchers have previously assessed visualization readability, and an analysis of the factors underlying perceived readability in visual data representations.

翻译：我们开发并验证了一种用于衡量数据可视化感知可读性的工具：PREVis。研究人员和从业人员可以轻松地将此工具作为评估的一部分，用于比较不同视觉数据表示的感知可读性。我们的工具可以补充受控用户任务绩效实验的结果，或在开发新技术时（如设计迭代阶段）为深入的定性研究提供额外数据。尽管可读性被公认为数据可视化的关键质量属性，但迄今为止，在视觉表示背景下尚未形成对该概念的统一界定。因此，研究人员在确定如何让用户评估可视化感知可读性时往往缺乏指导。为解决这一问题，我们通过严谨的流程开发了首个针对视觉数据表示主观可读性的验证工具。最终工具包含4个维度共11个测量项：可理解性、布局清晰度、数据值可读性以及数据模式可读性。我们在osf.io/9cg8j上提供了包含实施指南的问卷文档。除该工具外，我们还探讨了以往研究者评估可视化可读性的方法，并分析了影响视觉数据表示感知可读性的潜在因素。