# Mini-VLAT: A Short and Effective Measure of Visualization Literacy

arXiv: https://arxiv.org/abs/2304.07905 · HTML: https://mlsanity.com/paper/2304.07905/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2023.

## Built on by (most cited first, 16 of 16)

- [Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning](https://mlsanity.com/paper/2407.20174/) (2024)
- [Benchmarking Visual Language Models on Standardized Visualization Literacy Tests](https://mlsanity.com/paper/2503.16632/) (2025)
- [Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly](https://mlsanity.com/paper/2504.05445/) (2025)
- [Visualization Literacy of Multimodal Large Language Models: A Comparative Study](https://mlsanity.com/paper/2407.10996/) (2024)
- [See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs](https://mlsanity.com/paper/2504.09809/) (2025)
- [MapIQ: Evaluating Multimodal Large Language Models for Map Question Answering](https://mlsanity.com/paper/2507.11625/) (2025)
- [Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks](https://mlsanity.com/paper/2605.21825/) (2026)
- [Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention](https://mlsanity.com/paper/2508.03713/) (2025)
- [DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessment](https://mlsanity.com/paper/2508.04160/) (2025)
- [Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systems](https://mlsanity.com/paper/2601.12585/) (2026)
- [AnnoBench: A Benchmark for Visualization Annotation Generation](https://mlsanity.com/paper/2607.25911/) (2026)
- [Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction](https://mlsanity.com/paper/2602.05687/) (2026)
- [Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy](https://mlsanity.com/paper/2606.03142/) (2026)
- [Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy](https://mlsanity.com/paper/2607.15176/) (2026)
- [Attention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMs](https://mlsanity.com/paper/2607.16105/) (2026)
- [LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventions](https://mlsanity.com/paper/2609.24172/) (2026)

## Abstract (arXiv)

The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini-VLAT, a brief but practical visualization literacy test. The Mini-VLAT is a 12-item short form of the 53-item Visualization Literacy Assessment Test (VLAT). The Mini-VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini-VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini-VLAT by demonstrating a strong positive correlation between study participants' Mini-VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini-VLAT items showed a similar pattern of validity and reliability as the 53-item VLAT. The results show that Mini-VLAT is a psychometrically sound and practical short measure of visualization literacy.