Key Predictors of Generativity in Adulthood: A Machine Learning Analysis

Abstract

Objectives: This 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. Methods: A random forest machine learning algorithm was used. Data were drawn from the Midlife in the United States (MIDUS) survey. Results: Social 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. Discussion:..