Despite widespread acknowledgment that close relationships frequently involve tumultuous and dynamic experiences, most models in relationship psychology focus on linear relationship processes. Modeling nonlinear patterns can, however, be an important way to assess and better understand the complexities inherent in close relationships. In this article, I draw on one of the most widely studied theories in relationship science—attachment theory—to illustrate how modeling nonlinear effects between variables (i.e., curvilinear effects) and nonlinear dynamics across time (i.e., within-person variation and within-dyad flexibility) can reconcile inconsistencies in the literature, reveal unique relationship experiences, and broaden our understanding of complex relationship processes.
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