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自尊与个人特质、收入、消费和资产之间的关系:日本面板研究。

Relationships Between Self-Esteem and Personal Attributes, Income, Consumption, and Assets: Japanese Panel Study.

作者信息

Nakakita Makoto, Oya Sakae, Kubota Naoki, Toyabe Tomoki, Nakatsuma Teruo

机构信息

RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan.

Centre for Finance, Technology and Economics at Keio, Keio University, Tokyo 108-8345, Japan.

出版信息

Eur J Investig Health Psychol Educ. 2025 May 12;15(5):78. doi: 10.3390/ejihpe15050078.

Abstract

Self-esteem is a key topic in psychology and health research. It influences well-being, happiness, and even medicine. However, existing studies on self-esteem have yielded conflicting results, suggesting that a global consensus remains elusive. This study examines how demographic and socioeconomic factors influence self-esteem in Japan. We analyzed panel data sampled from the entire Japanese population, with separate estimates performed for marital status and gender to account for potential differences in the impact of these factors. Using a Bayesian panel logit model with the Markov chain Monte Carlo method and the ancillarity-sufficiency interweaving strategy for efficient estimation, we found similarities and differences compared with studies from other countries. Furthermore, when comparing the overall data with data stratified by marital status and gender, we observed significant differences in how these factors influenced self-esteem, even among the same individuals. These findings underscore the importance of considering such variations when incorporating self-esteem into medical and healthcare contexts.

摘要

自尊是心理学和健康研究中的一个关键主题。它影响幸福感、快乐感,甚至医学。然而,现有的关于自尊的研究结果相互矛盾,这表明全球共识仍然难以达成。本研究考察了人口统计学和社会经济因素如何影响日本的自尊。我们分析了从全体日本人口中抽样得到的面板数据,并针对婚姻状况和性别进行了单独估计,以考虑这些因素影响的潜在差异。使用带有马尔可夫链蒙特卡罗方法和辅助充分交织策略以进行有效估计的贝叶斯面板逻辑模型,我们发现与其他国家的研究相比既有相似之处也有不同之处。此外,当将总体数据与按婚姻状况和性别分层的数据进行比较时,我们观察到即使在同一人群中,这些因素影响自尊的方式也存在显著差异。这些发现强调了在将自尊纳入医学和医疗保健背景时考虑此类差异的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3046/12109891/9fc689e0a9e5/ejihpe-15-00078-g001.jpg

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