The ability to read, interpret, and critique data visualizations has mainly been assessed using data visualization tasks like value retrieval. Although evidence on different facets of Visual Data Literacy (VDL) is well established in visualization research and includes numeracy, graph familiarity, or aesthetic elements, they have not been sufficiently considered in ability assessments. Here, VDL is considered a multidimensional ability whose facets can be partially self-assessed. We introduce an assessment in which VDL is deconstructed as a process of understanding, in reference to frameworks from the learning sciences. MAVIL, Multidimensional Assessment of Visual Data Literacy, is composed of six ability dimensions: General Impression/Abstract Thinking, Graph Elements/Familiarity, Aesthetic Perception, Visualization Criticism, Data Reading Tasks and Numeracy/Topic Knowledge. MAVIL was designed for general audiences and implemented in a survey (n=438), representative of Austria's age groups (18-74 years) and gender split. The survey mirrors the population's VDL and shows the perception of two climate data visualizations, a line and bar chart. We found that $48\%$ of respondents make mistakes with the simple charts, while $5\%$ believe that they cannot summarize the visualization content. About a quarter have deficits in comprehending simple data units, and $19-20\%$ are unfamiliar with each displayed chart type.

翻译：阅读、解释和批判数据可视化的能力主要通过数值检索等数据可视化任务进行评估。尽管视觉数据素养的不同维度（包括计算能力、图表熟悉度或美学要素）在可视化研究中已得到充分证实，但这些维度在能力评估中尚未得到充分考虑。本文将视觉数据素养视为一种多维能力，其各维度可部分通过自评实现。我们引入一种评估方法，该方法参照学习科学框架，将视觉数据素养解构为理解过程。多维视觉数据素养评估由六个能力维度构成：整体印象/抽象思维、图表要素/熟悉度、美学感知、可视化批判、数据读取任务以及计算能力/主题知识。该评估面向普通受众设计，并在奥地利具有年龄（18-74岁）和性别代表性的调查（n=438）中实施。调查反映了人群的视觉数据素养水平，并展示了对两种气候数据可视化（折线图与柱状图）的认知情况。研究发现：$48\%$的受访者在简单图表解读中出现错误，$5\%$的受访者认为自己无法概括可视化内容；约四分之一受访者在理解基础数据单位方面存在不足，$19-20\%$的受访者对各类展示图表类型感到陌生。