*연구과제명: Unraveling the Most Important Predictors of Eudaimonic and Hedonic Well-Being in Korean Adults: A Machine Learning Approach
-연구 기관: 서울대학교 행복연구센터, 서울대학교 심리학과 (연구팀)
-저자: Mina Jyung, Sung-Ha Lee, Incheol Cho
Abstract
The quest to unravel what contributes to happiness continues to captivate interest in both everyday experiences and academic discourse. Nonetheless, empirical research on the relative importance of possible candidates and their associations with two key aspects of well-being—eudaimonia (the good life) and hedonia (pleasure)—is limited. This study addresses this gap by exploring the relative strength of 32 predictors from multiple do- mains on psychological well-being (PWB) and subjective well-being (SWB). Using a machine learning approach on a dataset of 559 Korean adults, we identified distinct pri- mary determinants for each well-being aspect. For PWB, meaning in life, self-esteem, and essentialist beliefs about happiness emerged as the strongest predictors requiring careful consideration. For SWB, depressive symptoms, subjective socioeconomic status, and emo- tional stability were salient predictors. Our findings highlight potential cultural nuances in the prioritization of happiness and offer valuable insights for policymakers and decision- makers in tailoring interventions and strategies to optimize individual well-being.
Keywords Psychological well-being · Subjective well-being · Machine learning approach · Korean · Essentialist beliefs about happiness
*연구과제명: Unraveling the Most Important Predictors of Eudaimonic and Hedonic Well-Being in Korean Adults: A Machine Learning Approach
-연구 기관: 서울대학교 행복연구센터, 서울대학교 심리학과 (연구팀)
-저자: Mina Jyung, Sung-Ha Lee, Incheol Cho
Abstract
The quest to unravel what contributes to happiness continues to captivate interest in both everyday experiences and academic discourse. Nonetheless, empirical research on the relative importance of possible candidates and their associations with two key aspects of well-being—eudaimonia (the good life) and hedonia (pleasure)—is limited. This study addresses this gap by exploring the relative strength of 32 predictors from multiple do- mains on psychological well-being (PWB) and subjective well-being (SWB). Using a machine learning approach on a dataset of 559 Korean adults, we identified distinct pri- mary determinants for each well-being aspect. For PWB, meaning in life, self-esteem, and essentialist beliefs about happiness emerged as the strongest predictors requiring careful consideration. For SWB, depressive symptoms, subjective socioeconomic status, and emo- tional stability were salient predictors. Our findings highlight potential cultural nuances in the prioritization of happiness and offer valuable insights for policymakers and decision- makers in tailoring interventions and strategies to optimize individual well-being.
Keywords Psychological well-being · Subjective well-being · Machine learning approach · Korean · Essentialist beliefs about happiness