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Review
. 2019 Mar 28;7(1):90-104.
doi: 10.1080/21642850.2019.1593845.

Conceptualising and operationalising resilience in older adults

Theodore D Cosco  1   2 , Almar Kok  3 , Andrew Wister  1 , Kenneth Howse  2
Affiliations
Review

Conceptualising and operationalising resilience in older adults

Theodore D Cosco et al. Health Psychol Behav Med. .

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.

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Conflict of interest statement

No potential conflict of interest was reported by the authors.

Figures

Figure 1.
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.
Figure 2.
Plot of adaptation linearly regressed on adversity in a categorical residuals model. * The shaded area identifies individuals as ‘resilient’.
See this image and copyright information in PMC

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