This study investigates the relationship between three visual representations (two-dimensional, three-dimensional fixed, and three-dimensional rotatable) of multidimensional data, and the subjects' ability to make predictions based on the data. Output of a momentum accounting system was simulated and graphics were rendered based on that information. An interactive computer program was developed and used to administer the laboratory experiment and collect the results.

Subjects made prediction decisions based on the graphics produced for four companies. The companies were stratified based on size (high or low) and growth patterns (high or low). Each subject made predictions for one type representation for each of the four companies. Because of inconsistencies of the sample distributions for the different representations, nonparametric analyses were used to examine the data.

The subjects using the three-dimensional data that could be rotated were found to provide the most accurate predictions. No differences between the treatments were found based on the subject's visual acuity, as measured by the Visual Vividness Imagery Questionnaire (VVIQ). The subjects using the two-dimensional representations were found to take the least amount of time for their predictions.