Incorporating general intelligence into epidemiology and the social sciences

The purpose of this article is to demonstrate the value of examining a variety of pressing behavioral, medical, and social phenomena as they relate to gradations in general intelligence. Although few (if any) variables in the social sciences can compete with the construct of general intelligence in its ability to forecast an array of socially valued attributes and outcomes, measures of general intelligence are seldom incorporated into correlational and experimental designs aimed at understanding maladaptive behavior (e.g., crime, dropping out of high school, unwise financial planning, health-risk behaviors, poor parenting, and vocational discord) or its opposite, highly adaptive behavior. We contend that, if consulted more often, the construct of general intelligence would contribute to understanding many puzzling human phenomena, because successive gradations of intelligence reflect successive degrees of risk. A method is provided for uncovering group trends, one expressly designed to reveal the range and prevalence of the many different kinds of human phenomena that vary as a function of intellectual gradations. By employing this method, policymakers and the public can more readily apprehend the significant, but often unsuspected, contribution made by general intelligence to many socially important outcomes. Our approach is similar to traditional epidemiological research aimed at ascertaining antecedents to maladies through the defining features of high-risk groups (e.g., for lung cancer, smokers and passive smokers; for AIDS victims, participants in unsafe sex; for academic mediocrity, among the intellectually gifted in nonaccelerative educational tracks; for mental retardation, high blood-lead levels). Once Such high-risk groups are defined (i.e., groups of persons whose behavioral dispositions predispose them, and often others around them, to unfortunate outcomes), policymakers and scientists are in a better position to disentangle genuine causes from families of correlations and can concentrate ameliorative resources more effectively. Data from educational and medical contexts are analyzed to show how measures of general intelligence, and other dimensions from differential psychology, can complement epidemiological and social science inquiry. We also argue that by incorporating such measures of human variation into policy development and research, policymakers are more likely to forestall ''iatrogenic effects'' (maladies caused by treatment).