Typicality effect in data graphs
Year: 2022
Type: article
Abstract: Some types of data graphs are more easily understood than others. Following the suggestion that typically encountered graphs may activate individuals’ cognitive schema quickly, this study investigated prior exposure to and typicality of three common graph types: vertical bar graphs, horizontal bar graphs and line graphs; and three common data patterns: rising, neutral and falling. Results from two samples ( N = 57 and N = 30) suggest that vertical bar graphs are encountered more frequently, are rated as more typical and are identified more quickly than horizontal bar graphs and line graphs; also that rising data patterns are more typical than falling and neutral data patterns. The findings contribute new knowledge about the hierarchical structure of graph schema and can inform design choices about which graph types might best facilitate viewers’ understanding of data visualizations.
Source: Visual Communication
Authors: Daniel Reimann, Marie Struwe, Nilam Ram, Robert Gaschler
Institutions: FernUniversität in Hagen, Stanford University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,250
OpenAlex ID: W4309114629