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Social Indicators Research

, Volume 81, Issue 1, pp 1–33 | Cite as

Investigating the Intra-Individual Variability and Trajectories of Subjective Well-being

  • Anne M. GadermannEmail author
  • Bruno D. Zumbo
Article

Abstract

The present study explored the short-term temporal characteristics of subjective well-being (SWB) and its potential correlates. Specifically, we examined the intra-individual variability and the trajectories of SWB and its components life satisfaction, positive, and negative affect. Over a two-week period, 27 participants (78% university students, 85% females, mean age of 29 years) provided SWB data every other day. Before and after this period, participants completed several questionnaires assessing personality and environmental variables, as well as several open-ended questions. The results provide evidence that participants experienced a significant amount of intra-individual variability in their SWB and its components. Furthermore, inter-individual differences in the intra-individual variability were related to several of the personality and environmental variables, as well as to participant’s age. Results of multiple regression analyses indicated that deliberation (one facet of the Big Five dimension conscientiousness), social support, and age accounted for 58% of the total variance of intra-individual variability in SWB. In contrast, the slopes of the individual trajectories of SWB were on average statistically non-significantly different from zero. These results emphasize that information is gained by looking at both the patterns and the intra-individual variability over time: The individual trajectories of the participants on average appeared to be stable, but intra-individual variability of SWB was meaningfully related to several of the variables.

Keywords

Life Satisfaction Negative Mood Positive Mood Median Absolute Deviation Individual Trajectory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.Department of ECPSUniversity of British ColumbiaVancouverCanada

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