The Effect of Gestalt Laws of Perceptual Organization on the Comprehension of Three-Variable Bar and Line Graphs. (February 2013)

• Record Type: Journal Article
• Authors: Ali, Nadia; Peebles, David
• Other Names: Cooke Nancy J. guest-editor.
• Is Part Of: Human factors. Volume 55:Number 1(2013)
• Page Start: 183
• Page End: 203
• Publication Date: 2013-02
• Subjects: graph comprehension -- diagrammatic reasoning -- verbal protocols; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720812452592
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: We report three experiments investigating the ability of undergraduate college students to comprehend 2 × 2 "interaction" graphs from two-way factorial research designs. Background: Factorial research designs are an invaluable research tool widely used in all branches of the natural and social sciences, and the teaching of such designs lies at the core of many college curricula. Such data can be represented in bar or line graph form. Previous studies have shown, however, that people interpret these two graphical forms differently. Method: In Experiment 1, participants were required to interpret interaction data in either bar or line graphs while thinking aloud. Verbal protocol analysis revealed that line graph users were significantly more likely to misinterpret the data or fail to interpret the graph altogether. Results: The patterns of errors line graph users made were interpreted as arising from the operation of Gestalt principles of perceptual organization, and this interpretation was used to develop two modified versions of the line graph, which were then tested in two further experiments. One of the modifications resulted in a significant improvement in performance. Conclusion: Results of the three experiments support the proposed explanation and demonstrate the effects (both positive and negative) of Gestalt principles of perceptual organization on graph comprehension. Application: We propose that our new design provides a more balanced representation of the data than the standard line graph for nonexpert users to comprehend the full range of relationships in two-way factorial research designs and may therefore be considered a more appropriate representation for use in educational and other nonexpert contexts.