the institutions with which they interact, their communities, and society as a whole (Bronfenbrenner, 1986)—all of which exert an influence on children’s ability to adapt to adverse conditions. Factors across all of these domains are often divided into two categories: those that “promote” adaptive competencies in children, and those that “protect” them from the negative consequences of exposure to adverse events leading to psychopathologies or stunted development. Wright and colleagues (2013) have referred to this phase of inquiry in the scientific evolution as the first two of four waves of resilience research: the first wave identified resilience factors, and the second wave explored resilience processes within individuals and across these multiple social systems.
With each succeeding wave, the resilience research field expanded beyond the original boundaries of developmental psychology. Wright et al. (2013) referred to the third wave as the examination of interventions that might enhance or facilitate resilience, and the still-emerging fourth wave is focused on a consideration of multiple system effects, notably within the fields of epigenetics and neurobiology. In the second and third waves, social scientists, education researchers, and social epidemiologists applied their disciplinary perspectives, particularly as the research explored the intersection of multiple levels (e.g., How does one measure a family or community’s social capital and its relationship to a child’s ability to adapt?); the relationship of resilience to health outcomes (including the biological mechanisms of action of adverse events triggering stress responses, which, in turn, lead to biochemical and genetic changes); and the institutional settings most conducive to resilience interventions for children (e.g., schools and day care centers).
These succeeding waves of resilience research have resulted in significant analytical shifts in the field as well. What began in the first wave as qualitative case-based research and quantitative variable-based research that generally relied on correlational analyses such as regression modeling, analysis of covariance, and categorical data analyses has evolved to include hierarchical modeling; latent growth curve analyses (particularly when looking at the relationship of resilience factors compared to recovery over time) (Bonanno et al., 2011); structural equation and propensity score modeling (Abramson et al., 2010b, Stehling-Ariza et al., 2012); and complex system science approaches (Sherrieb et al., 2010). The benefit of such sophisticated analyses is that they permit far more nuanced tests of frameworks and models that can incorporate multiple social levels, as well as dimensions of time. The cost to such complexity is that it may be regarded as out of reach for a practice community eager to translate such findings in to programs and interventions.