How audiences read, interpret, and critique data visualizations is mainly assessed through performance tests featuring tasks like value retrieval. Yet, other factors shown to shape visualization understanding, such as numeracy, graph familiarity, and aesthetic perception, remain underrepresented in existing instruments. To address this, we design and test a Multidimensional Assessment Method of Situated Visualization Understanding (MdamV). This method integrates task-based measures with self-perceived ability ratings and open-ended critique, applied directly to the visualizations being read. Grounded in learning sciences frameworks that view understanding as a multifaceted process, MdamV spans six dimensions: Comprehending, Decoding, Aestheticizing, Critiquing, Reading, and Contextualizing. Validation was supported by a survey (N=438) representative of Austria's population (ages 18-74, male/female split), using a line chart and a bar chart on climate data. Findings show, for example, that about a quarter of respondents indicate deficits in comprehending simple data units, roughly one in five people felt unfamiliar with each chart type, and self-assessed numeracy was significantly related to data reading performance (p=0.0004). Overall, the evaluation of MdamV demonstrates the value of assessing visualization understanding beyond performance, framing it as a situated process tied to particular visualizations.

翻译：受众如何阅读、解释和批判数据可视化主要通过包含数值检索等任务的性能测试进行评估。然而，其他已知影响可视化理解的因素，如计算能力、图表熟悉度和审美感知，在现有评估工具中仍体现不足。为此，我们设计并测试了一种情境化可视化理解的多维评估方法（MdamV）。该方法将基于任务的测量与自我感知能力评分及开放式批判相结合，直接应用于所阅读的可视化图表。基于学习科学框架——该框架将理解视为多层面的过程，MdamV涵盖六个维度：理解、解码、审美化、批判、阅读和情境化。验证过程通过一项代表奥地利人口（年龄18-74岁，男女比例均衡）的调查（N=438）进行，使用气候数据的折线图和柱状图作为评估材料。研究结果显示，例如约四分之一的受访者在理解简单数据单元方面存在不足，约五分之一的人对每种图表类型感到陌生，且自我评估的计算能力与数据阅读表现显著相关（p=0.0004）。总体而言，对MdamV的评估证明了超越性能测试来评估可视化理解的价值，将其视为与特定可视化紧密相连的情境化过程。