Large-scale study of web accessibility metrics

Abstract

Evaluating the accessibility of web resources is usually done by checking the conformance of the resource against a standard or set of guidelines (e.g., the WCAG 2.1). The result of the evaluation will indicate what guidelines are respected (or not) by the resource. While it might hint at the accessibility level of web resources, often it will be complicated to compare the level of accessibility of different resources or of different versions of the same resource from evaluation reports. Web accessibility metrics synthesize the accessibility level of a web resource into a quantifiable value. The fact that there is a wide number of accessibility metrics, makes it challenging to choose which ones to use. In this paper, we explore the relationship between web accessibility metrics. For that purpose, we investigated eleven web accessibility metrics. The metrics were computed from automated accessibility evaluations obtained using QualWeb. A set of around three million web pages were evaluated. By computing the metrics over this sample of nearly three million web pages, it was possible to identify groups of metrics that offer similar results. Our analysis shows that there are metrics that behave similarly, which, when deciding what metrics to use, assists in picking the metric that is less resource intensive or for which it might be easier to collect the inputs.

A large-scale web accessibility analysis considering technology adoption

“…We used the A3 metric since a study comparing eleven accessibility metrics [36] found it to be one of the metrics with the expected behavior (being capable to correctly judge the level of accessibility of the pages in the study). Additionally, from the three metrics with the expected behavior, A3 was the most discriminating one.…”