Multiple Factors that Determine Performance with Tables and Graphs

Abstract

Two experiments assessed the relative efficiency of line graphs, bar graphs, and tables, applying a multiple-factors approach to study the effects of the type of the required information, the complexity of the data, and the user's familiarity with the display. information extraction tasks included reading exact values, comparing values, identifying trends, and reading maximum values. Tables led to faster responses for all tasks, and the accuracy for tables was equally high or higher than for graphs. Bar graphs and line graphs differed in their relative efficiency for the different tasks. The complexity of the data also affected the tasks differentially, as did prior familiarity with the display. Performance for most conditions improved with experience. Our findings demonstrate the benefits of a multiple-factors approach to the study of displays. Generalizations about the relative efficiency of displays and computational models of the task performance with displays must consider the various relevant factors if they are to serve as valid design aids.