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Key Predictors of Generativity in Adulthood: A Machine Learning Analysis

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  • Mohsen Joshanloo

Abstract

ObjectivesThis study aimed to explore a broad range of predictors of generativity in older adults. The study included over 60 predictors across multiple domains, including personality, daily functioning, socioeconomic factors, health status, and mental well-being.MethodsA random forest machine learning algorithm was used. Data were drawn from the Midlife in the United States (MIDUS) survey.ResultsSocial potency, openness, social integration, personal growth, and achievement orientation were the strongest predictors of generativity. Notably, many demographic (e.g., income) and health-related variables (e.g., chronic health conditions) were found to be much less predictive.DiscussionThis study provides new data-driven insights into the nature of generativity. The findings suggest that generativity is more closely associated with eudaimonic and plasticity-related variables (e.g., personal growth and social potency) rather than hedonic and homeostasis-oriented ones (e.g., life satisfaction and emotional stability). This indicates that generativity is an inherently dynamic construct, driven by a desire for exploration, social contribution, and personal growth.

Suggested Citation

  • Mohsen Joshanloo, 2025. "Key Predictors of Generativity in Adulthood: A Machine Learning Analysis," The Journals of Gerontology: Series B, The Gerontological Society of America, vol. 80(4), pages 141-184.
    • Handle: RePEc:oup:geronb:v:80:y:2025:i:4:p:141-184.
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    File URL: http://hdl.handle.net/10.1093/geronb/gbae204
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