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What Is Successful Aging? A Psychometric Validation Study of Different Construct Definitions.
Gerontologist 2018 July 18
Background and Objectives: We examined the validity of 5 successful aging (SA) operationalizations that assessed different facets of the SA construct (cognitive and physical health and disability; well-being; social engagement).
Research Design and Methods: A total of 2,478 participants (mean age = 82.5 years, standard deviation [SD] = 3.47) were studied. We used confirmatory factor analysis to investigate the relationships between facets and to determine the convergent validity as well as short-term (1.5 years) and long-term (4.5 years) predictive validity of the 5 SA operationalizations for measures of quality of life (QoL) and objective health outcomes.
Results: A general SA operationalization that included all SA facets but also allowed differences between them showed the best model fit and construct validity. A biomedical operationalization of SA that excluded either the well-being or the social engagement facet showed lower convergent and predictive validity for subjective measures (e.g., QoL) but higher associations with objective measures (e.g., health). A purely psychosocial SA operationalization that excluded the physiological facet did not allow good prediction of objective health outcomes.
Discussion and Implications: Our results suggest that a well-balanced SA operationalization should include measures assessing health, disability, well-being, and social engagement.
Research Design and Methods: A total of 2,478 participants (mean age = 82.5 years, standard deviation [SD] = 3.47) were studied. We used confirmatory factor analysis to investigate the relationships between facets and to determine the convergent validity as well as short-term (1.5 years) and long-term (4.5 years) predictive validity of the 5 SA operationalizations for measures of quality of life (QoL) and objective health outcomes.
Results: A general SA operationalization that included all SA facets but also allowed differences between them showed the best model fit and construct validity. A biomedical operationalization of SA that excluded either the well-being or the social engagement facet showed lower convergent and predictive validity for subjective measures (e.g., QoL) but higher associations with objective measures (e.g., health). A purely psychosocial SA operationalization that excluded the physiological facet did not allow good prediction of objective health outcomes.
Discussion and Implications: Our results suggest that a well-balanced SA operationalization should include measures assessing health, disability, well-being, and social engagement.
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