In this review, we outline how a methodologically based framework, the discontinuous growth model (DGM), can be used to advance research and theory on transitions. Our review focuses on identifying the types of hypotheses and research questions that can be specified and tested using this framework. Three parameters of the DGM are described: the pre-event covariate (TIME^pre), a transition covariate (TRANS), and a recovery covariate (RECOV). We discuss relevant parameters by analyzing the relative and absolute changes following a transition event. We illustrate the framework with a variety of studies from different contexts and address the difficulty of interpreting responses to events without TIME^pre data. In addition, we discuss the role of large longitudinal databases as sources for advancing research and theory surrounding transitions, particularly for rare and unexpected events. Finally, we discuss ways in which transition research can inform our understanding of individual, team, and organizational resilience and adaptation.

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