Analyzing card-sorting data using graph visualization

This paper describes a method for visualizing and analyzing co-occurrence in card-sorting data. Card sorting is a popular knowledge elicitation method used in information architecture and user experience design. However, analyzing card-sorting data can be a challenge. Detailed qualitative analysis is difficult and time consuming, especially for larger studies. Quantitative analysis can be automated and is scalable, but can be difficult to interpret. A graph visualization offers a novel way to analyze and understand the relationships between cards and the mental models elicited in a card-sorting study. Graph visualizations are graphs that illustrate connections between concepts, such as cards in a card-sorting study. A visualization can quickly show relationships between cards and clusters of cards that represent topics that may not be obvious from traditional card-sort analysis methods. A case study describes how graph visualization can be used to analyze the data. The results of the analysis are compared and contrasted with a popular histogram-matrix analysis method. Strengths and weaknesses of the proposed graph-visualization analysis method are discussed.