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Visualization literacy is an essential skill for accurately interpreting data to inform critical decisions. Consequently, it is vital to understand the evolution of this ability and devise targeted interventions to enhance it, requiring concise and repeatable assessments of visualization literacy for individuals. However, current assessments, such as the Visualization Literacy Assessment Test ( vlat ), are time-consuming due to their fixed, lengthy format. To address this limitation, we develop two streamlined computerized adaptive tests ( cats ) for visualization literacy, a-vlat and a-calvi , which measure the same set of skills as their original versions in half the number of questions. Specifically, we (1) employ item response theory (IRT) and non-psychometric constraints to construct adaptive versions of the assessments, (2) finalize the configurations of adaptation through simulation, (3) refine the composition of test items of a-calvi via a qualitative study, and (4) demonstrate the test-retest reliability (ICC: 0.98 and 0.98) and convergent validity (correlation: 0.81 and 0.66) of both CATS via four online studies. We discuss practical recommendations for using our CATS and opportunities for further customization to leverage the full potential of adaptive assessments. All supplemental materials are available at https://osf.io/a6258/ . 

reVISit is an open-source software toolkit and framework for creating, deploying, and monitoring empirical visualization studies. Running a quality empirical study in visualization can be demanding and resource-intensive, requiring substantial time, cost, and technical expertise from the research team. These challenges are amplified as research norms trend towards more complex and rigorous study methodologies, alongside a growing need to evaluate more complex interactive visualizations. reVISit aims to ameliorate these challenges by introducing a domain-specific language for study set-up, and a series of software components, such as UI elements, behavior provenance, and an experiment monitoring and management interface. Together with interactive or static stimuli provided by the experimenter, these are compiled to a ready-to-deploy web-based experiment. We demonstrate reVISit's functionality by re-implementing two studies --- a graphical perception task and a more complex, interactive study. reVISit is an open-source community project, available at https://revisit.dev/. 

We contribute an autoethnographic reflection on the complexity of defining and measuring visualization literacy (i.e., the ability to interpret and construct visualizations) to expose our tacit thoughts that often exist in-between polished works and remain unreported in individual research papers. Our work is inspired by the growing number of empirical studies in visualization research that rely on visualization literacy as a basis for developing effective data representations or educational interventions. Researchers have already made various efforts to assess this construct, yet it is often hard to pinpoint either what we want to measure or what we are effectively measuring. In this autoethnography, we gather insights from 14 internal interviews with researchers who are users or designers of visualization literacy tests. We aim to identify what makes visualization literacy assessment a ``wicked'' problem. We further reflect on the fluidity of visualization literacy and discuss how this property may lead to misalignment between what the construct is and how measurements of it are used or designed. We also examine potential threats to measurement validity from conceptual, operational, and methodological perspectives. Based on our experiences and reflections, we propose several calls to action aimed at tackling the wicked problem of visualization literacy measurement, such as by broadening test scopes and modalities, improving test ecological validity, making it easier to use tests, seeking interdisciplinary collaboration, and drawing from continued dialogue on visualization literacy to expect and be more comfortable with its fluidity. 

Graphical perception studies typically measure visualization encoding effectiveness using the error of an “average observer”, leading to canonical rankings of encodings for numerical attributes: e.g., position > area > angle > volume. Yet different people may vary in their ability to read different visualization types, leading to variance in this ranking across individuals not captured by population-level metrics using “average observer” models. One way we can bridge this gap is by recasting classic visual perception tasks as tools for assessing individual performance, in addition to overall visualization performance. In this article we replicate and extend Cleveland and McGill's graphical comparison experiment using Bayesian multilevel regression, using these models to explore individual differences in visualization skill from multiple perspectives. The results from experiments and modeling indicate that some people show patterns of accuracy that credibly deviate from the canonical rankings of visualization effectiveness. We discuss implications of these findings, such as a need for new ways to communicate visualization effectiveness to designers, how patterns in individuals’ responses may show systematic biases and strategies in visualization judgment, and how recasting classic visual perception tasks as tools for assessing individual performance may offer new ways to quantify aspects of visualization literacy. Experiment data, source code, and analysis scripts are available at the following repository: https://osf.io/8ub7t/?view_only=9be4798797404a4397be3c6fc2a68cc0 .