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Many important variables in behavioral development are presumed to be unrelated because of repeated failures to obtain substantial correlations. In this article, we explore the possibility that such null findings have often been due to failures to aggregate. The principle of aggregation states that the sum of a set of multiple measurements is a more stable and representative estimator than any single measurement. This greater representation occurs because there is inevitably some error associated with measurement. By combining numerous exemplars, such errors of measurement are averaged out, leaving a clearer view of underlying relationships. We illustrate the usefulness of this principle in 12 major areas of developmental research in which the issue of negligible correlations figures prominently: (a) the validity of judges' ratings, (b) the cross-situational consistency of moral character and personality, (c) the longitudinal stability of personality, (d) the coherence of stages of cognitive development, (e) metacognition, (f) the attitude-behavior relationship, (g) the personality-behavior relationship, (h) the role-taking/altruism relationship, (i) the moral-judgment/altruism relationship, (j) the legitimacy of the construct of attachment, (k) the existence of sex differences, and (1) the assessment of emotionality in animals. In a final section, we also discuss the implications of the principle of aggregation for conducting experimental research. A preliminary version of this paper was presented at the Merrill-Palmer Society, Detroit, May 1982. We would like to thank Jack Block, John Borkowski, Douglas Jackson, and Linda Siegel for discussions of the issues involved in this paper. Requests for reprints should be sent to J.

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