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Apply Day Reconstruction Method and Classification and Regression Tree to Explore Subjective Well-Being
Day Reconstruction Method (DRM)
Classification and Regression Tree (CART)
|Abstract:||本研究旨在運用新穎的主觀幸福感（subjective well-being）調查方法一日重建法（Day Reconstruction Method, DRM）及非線性的分類與迴歸樹（Classification and Regression Tree, CART）統計方法，探究主觀幸福感的情緒面。針對100名大學生為對象，用DRM蒐集到1657個以事件為單位的情緒資料，再運用CART統計方法，分析事件的情境與情緒間的關係。
The purpose of this study was to investigate the affect part of subjective well-being (SWB) through a new kindsurvey method called Day Reconstruction Method (DRM) with non-liner statistical analysis method Classification and Regression Tree (CART). In this study, 1657 episodes of affect data was collected from 100 undergraduate students using DRM, these data then analyzed by CART in order to further understand the relation between situation and affect of episodes. When analyzing DRM, one could get net affect scores from different situation variables; CART analysis provided further information of relationship of situation conditions combination and affect; if summarize episodic data, one could get individual subjective well-being scores, including duration-weighted net affect and U-index; individual affect then could be compared with cognitive part of SWB, that is, life satisfaction. Mean scores of affect from different situation showed that undergraduate students had the most positive affect when the episodes involved with friendship and playing, such as dating/intimacy relationship, relax/playing games, eating, watching TV/movies/listening to music, shopping; and had the most negative affect when the episodes involved with schoolwork, school clubs, working/part time jobs. CART analysis showed that for all episodic affect, schoolwork, relax/playing games, meeting/group discussion, interaction with good friends, traffic/moving have the most important relationships. CART analysis also divided samples into 16 terminal nodes, these nodes have different conditions, which can be used to predict affect score. When summarized episodic data to individual level, that is, the affect part of SWB, one could find it differs from the cognitive part of SWB. These two parts only had low correlation. This study also found strength of CART for DRM data analysis, including the ability of analyzing large number and different type of variables, being able to find the specific conditions combination of situation variables, sorting situation variables from importance. Finally, these findings suggested some ways to improve undergraduate students' subjective well-being. It's suggested that future research making use of the flexibility of DRM for different kinds of application, including change the situation variables, subject group, background variables, and affect terms, and use other recording tools with time information to gain understanding of relationship of situation and affect. On the other hand, one can establish well-being predicting model by CART with collecting samples with good representativeness.
|Appears in Collections:||學位論文|
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