Judging the relatedness of variables: The psychophysics of covariation detection.

Journal of Experimental Psychology: Human Perception and Performance, 11, 640-649.

Abstract

Previous research on how people judge the relation between continuous variables has indicated that judgments of scatterplots are curvilinearly related to Pearson's correlation coefficient. In this article, we argue that because Pearson's correlation is composed of three distinct components (slope, error variance, and variance of X) it is better to look at judgments as a function of these components rather than as a function of Pearson's correlation. These three components of Pearson's correlation and presentation format (graphical and tabular) were manipulated factorially in three experiments. The first two experiments used naive subjects, and the third experiment used expert subjects. The major conclusions were (a) scatterplots with the same value of Pearson's correlation are judged to possess different degrees of relation if the correlations are based on different combinations of the three components; (b) with Pearson's correlation held constant, the error variance is the most important component; and (c) graphical formats lead to higher judgments of relatedness than do tabular formats, with this effect being larger for naive than for expert observers. It was also concluded that attempts to determine the psychophysical function between Pearson's correlation and judgments of relatedness are of questionable value.

©1985 by the American Psychological Association.

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