Quantitative Approaches to Examine Resilience and Aging

In this chapter, we review six quantitative approaches to examining resilience in the context of aging. We categorize these approaches based on the distinct statistical methods that are used to operationally define resilience: estimating “buffering” effects of hypothesized protective factors in the effect modification approach, scale construction in the psychometric approach, comparison of a profile of characteristics between groups based on predefined resilience responses in the a priori approach, data-driven subgroup identification based on resilience responses in the clustering approach, analyzing predictors of adversity-outcome residual values in the residual approach, and analyzing stressor-response patterns in high-density time-series in the complex system approach. We illustrate each of the methods with multiple examples from the literature and pay special attention to the theoretical and conceptual assumptions inherent to each approach about what resilience is and how its correlates can be identified. The approaches are not mutually exclusive. Researchers may choose to combine multiple approaches and may analyze the same data using multiple approaches to compare the findings between them. After reading this chapter, the reader will be familiar with commonly used quantitative approaches to resilience, their implicit and explicit theoretical assumptions, and their strengths and limitations.