SEEING THE FOREST FROM THE TREES - WHEN PREDICTING THE BEHAVIOR OR STATUS OF GROUPS, CORRELATE MEANS

When measures of individual differences are used to predict group perf ormance, the reporting of correlations computed on samples of individu als invites misinterpretation and dismissal of the data. In contrast, if regression equations, in which the correlations required are comput ed on bivariate means, as are the distribution statistics, it is diffi cult to underappreciate or lightly dismiss the utility of psychologica l predictors. Given sufficient sample size and linearity of regression , this technique produces cross-validated regression equations that fo recast criterion means with almost perfect accuracy. This level of acc uracy is provided by correlations approaching unity between bivariate samples of predictor and criterion means, and this holds true regardle ss of the magnitude of the ''simple'' correlation (e.g., r(xy) = .20, or r(xy) .80). We illustrate this technique empirically using a measur e of general intelligence as the predictor and other measures of indiv idual 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 t his approach and epidemiological research reinforce its utility as a t ool for making decisions about policy.