1. ¹Department of Mathematics and Computer Science, Eindhoven University of Technology, the Netherlands
2. ²Department of Industrial Design, Eindhoven University of Technology, the Netherlands

Correspondence: Jing Li, Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, the Netherlands. Tel: +31(0)40 247 3883; Fax: +31(0)40 246 8508; E-mail: J.Li@tue.nl

Received 12 December 2007; Revised 11 March 2008; Accepted 16 March 2008; Published online 1 May 2008.

Abstract

Scatterplots and parallel coordinate plots (PCPs) can both be used to assess correlation visually. In this paper, we compare these two visualization methods in a controlled user experiment. More specifically, 25 participants were asked to report observed correlation as a function of the sample correlation under varying conditions of visualization method, sample size and observation time. A statistical model is proposed to describe the correlation judgment process. The accuracy and the bias in the judgments in the different conditions are established by interpreting the parameters in this model. A discriminability index is proposed to characterize the performance accuracy in each experimental condition. Moreover, a statistical test is applied to derive whether or not the human sensation scale differs from a theoretically optimal (i.e., unbiased) judgment scale. Based on these analyses, we conclude that users can reliably distinguish twice as many different correlation levels when using scatterplots as when using PCPs. We also find that there is a bias towards reporting negative correlations when using PCPs. Therefore, we conclude that scatterplots are more effective than parallel plots in supporting visual correlation analysis.