Ricardo Martins, Professor, Federal University of Parana
As in the human sciences, design deal with social facts that are impossible to be reproduced twice. That’s why it is hard to define immutable rules or laws that could help graphic designers to predict the effect of design. Some scientific studies have tried to establish cause and effect relationships, however without success. Whether in typography, color, composition, animation, interaction, experience and in other fields, many have tried to create rules using scientific experiments. This rarely works because they usually show correlations, but not cause and effect explanations. However, it is not only the design that suffers from this pain. Economics also deals with social phenomena, and needed to develop ways to make the study of human behavior more objective. My proposal is to use the same tools economists employ to better understand how design works and make it more objective in the moment of teaching it. Some tools are useful, such as multivariate linear regression, conjoint analysis, and other types of inferential statistics. They make it possible to see which design factors are affecting preference, at a much deeper level than diffused concepts such as the golden ratio, for example. Does the human being prefers layouts that follow the proportions of Fibonacci? Does yellow color stimulate appetite? Are serifed letters easier to read in long texts? Or would there be other design factors that would be generating these effects? Using inferential statistics it is possible to change dozens of graphing factors simultaneously and find out which one has had real impact on people’s choices, just as in the studies of behavioral economists like Dan Ariely, Steven Levitt and Richard Thaler. Obviously, design still needs to deal with what is subjective, but the study of statistical probabilities may show a new way of studying the effects generated by design, facilitating its teaching.