動態性情緒調節之集群特性與心理適應之關聯：基於經驗取樣之探索

Abstract

本研究透過經驗取樣收集之連續性情緒狀態資料，結合個體中心的集群分析方法，深入探討動態性情緒調節指標對個體心理適應的影響。在為期七天的實驗期間，本研究共收集103位參與者的日常經驗情緒狀態。基於Kuppens等人（2010a）提出的動態情緒系統理論（DynAffect System），本研究將這些情緒價性和激發程度數據，透過Oravecz等人（2016）的高階層多維模型BHOUM，建構出個體獨有的三大動態情緒指標——基準、變異性和調節力——來分析動態情緒調節與心理適應之間的相互作用。藉由這三項指標，以及正負向情緒、生活滿意度、安適幸福感和心理幸福感等心理適應的量測，本研究揭示了幾個重要發現：（一）動態性情緒指標的組型能為個體差異提供有意義的描述。（二）情緒變異性與心理適應指標之間，並未呈現過往研究所發現的關聯性。（三）情緒價性和基準皆高的個體，透過較強的調節力作用，整體經驗較多的正向情緒和較少的負向情緒，也是高心理適應的群組。（四）高調節力對於心理適應具有顯著影響，這一作用也反映在華人特有的安適幸福感指標上。本研究的發現不僅為動態情緒分析提供了多維且多階層的實證支持，也對動態情緒調節特徵的個體差異進行深入的洞察，進一步豐富了情緒調節的理論基礎。

This study, utilizing experience sampling to collect continuous emotional state data and integrating person-centered cluster analysis, delves deeply into the impact of dynamic emotional indicators on individual psychological adjustment. Over a seven-day experience sampling method, emotional states of 103 participants were collected daily. Based on the Dynamic Affect System proposed by Kuppens et al. (2010a), this study utilized Oravecz et al. (2016)'s hierarchical multi-dimensional model BHOUM to construct three individual-specific dynamic affect indexes—baseline, variability, and regulation (also known as attractor strength)—to analyze the interplay between emotion regulation dynamics and psychological adjustment. Along with the measurements of positive and negative affect schedule (PANAS), satisfaction with life scale (SWLS), peace of mind (POM) and psychological well-being (PWB), the study has made several key findings: (1) Dynamic emotional characteristics patterns provided meaningful descriptions of individual differences of emotion regulation. (2) Emotional variability did not show the correlations with psychological adjustment indicators found in previous studies. (3) Individuals with higher valence and arousal baselines, through the influence of strong emotion regulation, experienced more positive and fewer negative emotions, representing the group with high psychological adjustment. (4) High emotion regulation significantly impacted psychological adjustment, an effect also reflected in the POM indicator unique to Chinese culture. These findings not only provide empirical support for multi-dimensional dynamic emotion analysis, but also offer deeper insights into individual differences in emotional regulation dynamic features, further enriching the theoretical foundations of emotion regulation.