Conceptualising and operationalising resilience in older adults

Figures & data

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’.

Table 1. Masten's resilience categorisation matrix.

		Adaptation
		Lowest Tertile	Middle Tertile	Highest Tertile
Adversity	Highest Tertile	Maladaptive	X	Resilient
	Middle Tertile	X	X	X
	Lowest Tertile	Highly Vulnerable	X	Competent

Table 2. Methods of operationalising resilience and their strengths and limitations.

		Variable-centered			Individual-centered
	Psychometric-driven	Interaction	Residuals		Researcher-driven Thresholds	Latent Class Approach
			Continuous	Categorical
Acceptable Variable Types	n/a	Ordinal, continuous	Continuous	Continuous	Nominal, ordinal, continuous	Nominal, ordinal, continuous
Minimum Sample Size Requirements	No	Yes	Yes	Yes	No	Yes
Statistical Power	n/a	Low	High	Low	Low	Depends on sample
Granularity^a	Moderate	Depends on measurement variables	High	Low	Low	Variable
Resilience Quantified	Yes	No	Yes	Yes	Yes	Yes
Limitations	Must be included in existing dataset or collected prospectively	Individuals never explicitly identified as ‘resilient’	Restrictive criteria for included variables	Restrictive criteria for included variables	Subjective group membership criteria, i.e. set by researchers	Group sizes are not known in advance
Strengths	Integrate levels of resilience into a single psychometrically-robust score	Identifies targets for intervention	Greater insight into adversity-outcome relationship	Interpretation of analysis very straightforward	Accommodates a range of variable types	Effective method for longitudinal studies

^aLevel of detail possible.