Conceptualising and operationalising resilience in older adults

Abstract

Context: As a result of increases in life expectancy and decreases in fertility, the proportion of the population entering later life has increased dramatically in recent decades. When faced with age-related challenges, some older adults respond more positively to adversity than would be expected given the level of adversity that they have experienced, demonstrating 'resilience'. Objectives: Having a clear conceptual framework for resilience is a prerequisite to operationalising resilience in a research context. Methods: Here we compare and contrast several approaches to the operationalisation of resilience: psychometric-driven and data-driven (variable-centred and individual-centred) methods. Results: Psychometric-driven methods involve the administration of established questionnaires aimed at quantifying resilience. Data-driven techniques use statistical procedures to examine and/or operationalise resilience and can be broadly categorised into variable-centred methods, i.e. interaction and residuals, and individual-centred methods, i.e. categorical and latent class. Conclusions: The specific question(s) driving the research and the nature of the variables a researcher intends to use in their adversity-outcome dyad will largely dictate which methods are more (or less) appropriate in that circumstance. A measured approach to the ways in which resilience is investigated is warranted in order to facilitate the most useful application of this burgeoning field of research.

Keywords: Resilience; conceptualisation; methods; older adults; operationalisation.

Figure 1.

Plot of adaptation linearly regressed on adversity in a continuous residuals model. * The residual values, i.e. distance from the fitted regression line indicated by the dotted line, quantify the level of resilience.

Figure 2.

Plot of adaptation linearly regressed on adversity in a categorical residuals model. * The shaded area identifies individuals as ‘resilient’.