Seeing the forest from the trees: When predicting the behavior or status of groups, correlate means.

Abstract

When measures of individual differences are used to predict group performance, the reporting of correlations computed on samples of individuals invites misinterpretation and dismissal of the data. In contrast, if regression equations, in which the correlations required are computed on bivariate means, as are the distribution statistics, it is difficult to underappreciate or lightly dismiss the utility of psychological predictors. Given sufficient sample size and linearity of regression, this technique produces cross-validated regression equations that forecast criterion means with almost perfect accuracy. This level of accuracy is provided by correlations approaching unity between bivariate samples of predictor and criterion means, and this holds true regardless of the magnitude of the “simple” correlation (e.g., rxy= .20, or rxy= .80). We illustrate this technique empirically using a measure of general intelligence as the predictor and other measures of individual differences and socioeconomic status as criteria. In addition to theoretical applications pertaining to group trends, this methodology also has implications for applied problems aimed at developing policy in education, medical, and psychological clinics, business, industry, the military, and other domains of public welfare. Linkages between this approach and epidemiological research reinforce its utility as a tool for making decisions about policy.

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