Testing predictive developmental hypotheses

Abstract

Predictive developmental hypotheses play a crucial role in developmental theories. These hypotheses link early developmental behaviors or processes to later developmental outcomes. Empirical tests of predictive developmental hypotheses are generally based on standard regression models. It is argued that hierarchical linear models or longitudinal multilevel models offer a better alternative. A multivariate longitudinal model linking developmental data to a criterion is described and an application is given. The application, derived from attachment theory, pertains to the prediction of infant behavior in the Strange Situation. It is concluded that the proposed approach offers a valuable tool to the developmentalist, both from a theoretical and methodological point of view.