UC Riverside

Indices of cumulative risk (CR) have long been used in developmental research to encode the number of risk factors a child or adolescent experiences that may impede optimal developmental outcomes. Initial contributions concentrated on indices of cumulative environmental risk; more recently, indices of cumulative genetic risk have been employed. In this article, regression analytic methods are proposed for interrogating strongly the validity of risk indices by testing optimality of compositing weights, enabling more informative modeling of effects of CR indices. Reanalyses of data from two studies are reported. One study involved 10 environmental risk factors predicting Verbal IQ in 215 four-year-old children. The second study included an index of genetic CR in a G×E interaction investigation of 281 target participants assessed at age 15 years and then again at age 31 years for observed hostility during videotaped interactions with close family relations. Principles to guide evaluation of results of statistical modeling are presented, and implications of results for research and theory are discussed. The ultimate goals of this paper are to develop stronger tests of conjectures involving CR indices and to promote methods for improving replicability of results across studies.