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Testing Set-Point Theory in a Swiss National Sample: Reaction and Adaptation to Major Life Events

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Abstract

Set-point theory posits that individuals react to the experience of major life events, but quickly adapt back to pre-event baseline levels of subjective well-being in the years following the event. A large, nationally representative panel study of Swiss households was used to examine set-point theory by investigating the extent of adaptation following the experience of marriage, childbirth, widowhood, unemployment, and disability. Our results demonstrate that major life events are associated with marked changes in life satisfaction and, for some events (e.g., marriage, disability), these changes are relatively long lasting even when accounting for normative, age related changes.

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Notes

  1. Data from the final wave (collected in 2012) was only available as a beta version and did not include data about employment status. Thus, selection of group who experienced unemployment (and the related comparison group) excluded this final wave of data.

  2. It is worth noting that the method for selecting our disability sample in this study differs from the method used in past research by Lucas (2007b) in the GSOEP and BHPS. In these past studies, analyses were restricted to people who not only stayed disabled for the rest of the time they participated in the study, but also who stayed in the study for at least 3 years after onset of disability.

  3. Although age often has curvilinear effects, the life events we examined in this paper often occur within a constrained age range. In this case, for the simplicity of the models and their interpretation, a linear trend is a useful simplification.

References

  • Baird, B. M., Lucas, R. E., & Donnellan, M. B. (2010). Life satisfaction across the life span: Findings from two nationally representative panel studies. Social Indicators Research, 99, 183–203.

    Article  Google Scholar 

  • Bates, D., & Maechler, M. (2010). lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-35. http://cran.r-project.org/web/packages/lme4/lme4.pdf.

  • Blanchflower, D. G., & Oswald, A. J. (2008). Is well-being U-shaped over the life cycle? Social Science and Medicine, 66, 1733–1749.

    Article  Google Scholar 

  • Boyce, C. J., & Wood, A. M. (2011). Personality prior to disability determines adaptation: Agreeable individuals recover lost life satisfaction faster and more completely. Psychological Science, 22, 1397–1402.

    Article  Google Scholar 

  • Boyce, C. J., Wood, A. M., & Brown, G. D. A. (2010). The dark side of conscientiousness: Conscientious people experience greater drops in life satisfaction following unemployment. Journal of Research in Personality, 44, 535–539.

    Article  Google Scholar 

  • Budowski, M., Tillmann, R., Zimmermann, E., Wernli, B., Scherpenzeel, A., & Gabadinho, A. (2001). The Swiss Household Panel 1999–2003: Data for research on micro-social change. ZUMA-Nachrichten, 49, 100–125.

    Google Scholar 

  • Busseri, M. A., & Sadava, S. W. (2011). A review of the tripartite structure of subjective well-being: Implications for conceptualization, operationalization, analysis, and synthesis. Personality and Social Psychology Review, 15, 290–314.

    Article  Google Scholar 

  • Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of American life. New York: Russell Sage Foundation.

    Google Scholar 

  • Deaton, A. (2008). Income, health and well-being around the world: Evidence from the Gallup World Poll. Journal of Economic Perspectives, 22, 53–72.

    Article  Google Scholar 

  • Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95, 542–575.

    Article  Google Scholar 

  • Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49, 71–75.

    Article  Google Scholar 

  • Diener, E., Lucas, R. E., & Scollon, C. N. (2006). Beyond the hedonic treadmill: Revising the adaptation theory of well-being. American Psychologist, 61, 305–314.

    Article  Google Scholar 

  • Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125, 276–302.

    Article  Google Scholar 

  • Dyrdal, G. M., & Lucas, R. E. (2013). Reaction and adaptation to the birth of a child: A couple-level analysis. Developmental Psychology, 49, 749–761.

    Article  Google Scholar 

  • Fredrick, S., & Loewenstein, G. (1999). Hedonic adaptation. In D. Kahneman, E. Diener, & N. Schwartz (Eds.), Well-being: The foundations of hedonic psychology (pp. 302–329). New York: Sage.

    Google Scholar 

  • Frey, B. S., & Stutzer, A. (2004). Happiness and economics: How the economy and institutions affect human well-being. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Galatzer-Levy, I. R., Bonanno, G. A., & Mancini, A. D. (2010). From marianthal to latent growth mixture modeling: A return to the exploration of individual differences in response to unemployment. Journal of Neuroscience, Psychology, and Economics, 3, 116–125.

    Article  Google Scholar 

  • Gelman, A., & Hill, J. (2009). Data analysis using regression and multilevel/hierarchical models. New York: Cambridge University Press.

    Google Scholar 

  • Gelman, A., Su, Y., Yajima, J., Hill, J., Pittau, M. G., Kerman, J., & Zheng, T. (2011). Arm: Data analysis using regression and multilevel hierarchical models. R package version 1.4-14. http://cran.r-project.org/web/packages/arm/arm.pdf.

  • Kobau, R., Sniezek, J., Zack, M. M., Lucas, R. E., & Burns, A. (2010). Well-being assessment: An evaluation of well-being scales for public health and population estimates of well-being among US adults. Applied Psychology: Health and Well-Being, 2, 272–297.

    Google Scholar 

  • Lucas, R. E. (2005). Time does not heal all wounds: A longitudinal study of reaction and adaptation to divorce. Psychological Science, 16, 945–950.

    Article  Google Scholar 

  • Lucas, R. E. (2007a). Adaptation and the set-point model of subjective well-being: Does happiness change after major life events? Current Directions in Psychological Science, 16, 75–79.

    Article  Google Scholar 

  • Lucas, R. E. (2007b). Long-term disability is associated with lasting changes in subjective well-being: Evidence from two nationally representative longitudinal studies. Journal of Personality and Social Psychology, 92, 717–730.

    Article  Google Scholar 

  • Lucas, R. E., Clark, A. E., Georgellis, Y., & Diener, E. (2003). Reexamining adaptation and the set point model of happiness: Reactions to changes in marital status. Journal of Personality and Social Psychology, 84, 527–539.

    Article  Google Scholar 

  • Lucas, R. E., Clark, A. E., Georgellis, Y., & Diener, E. (2004). Unemployment alters the set point for life satisfaction. Psychological Science, 15, 8–13.

    Article  Google Scholar 

  • Lucas, R. E., & Donnellan, M. B. (2011). Estimating the reliability of single-item life satisfaction measures: Results from four national panel studies. Social Indicators Research, 105, 323–331.

    Article  Google Scholar 

  • Luhmann, M., & Eid, M. (2009). Does it really feel the same? Changes in life satisfaction following repeated life events. Journal of Personality and Social Psychology, 97, 363–381.

    Article  Google Scholar 

  • Lykken, D., & Tellegen, A. (1996). Happiness is a stochastic phenomenon. Psychological Science, 7, 186–189.

    Article  Google Scholar 

  • Lyubomirsky, S., Sheldon, K. M., & Schkade, D. (2005). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9, 111–131.

    Article  Google Scholar 

  • Okun, M. A., & George, L. (1984). Physician- and self-ratings of health, neuroticism and subjective well-being among men and women. Personality and Individual Differences, 5, 533–539.

    Article  Google Scholar 

  • R Development Core Team. (2010). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

    Google Scholar 

  • Smith, D. M., Schwarz, N., Roberts, T. R., & Ubel, P. A. (2006). Why are you calling me? How survey introductions change response patterns. Quality of Life Research, 15, 621–630.

    Article  Google Scholar 

  • Wilson, W. (1967). Correlates of avowed happiness. Psychological Bulletin, 67, 294–306.

    Article  Google Scholar 

  • Yap, S. C. Y., Anusic, I., & Lucas, R. E. (2012). Does personality moderate reaction and adaptation to major life events? Evidence from the British Household Panel Survey. Journal of Research in Personality, 46, 477–488.

    Article  Google Scholar 

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Acknowledgement

The data used in this article were made available to us by the Swiss Foundation for Research in Social Sciences (FORS). The Swiss Household Panel is financed by the Swiss National Science Foundation and based at FORS in Lausanne, Switzerland. Neither the original collectors of the data nor the data archive bear any responsibility for the analyses or interpretations presented here. This research was supported in part by Doctoral Fellowships from the Social Sciences and Humanities Research Council of Canada awarded to Ivana Anusic and Stevie Yap, and NIA grants AG032001 and AG040715 awarded to Richard Lucas.

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Authors and Affiliations

  1. Department of Psychology, Michigan State University, 316 Physics Road, East Lansing, MI, 48824, USA

    Ivana Anusic, Stevie C. Y. Yap & Richard E. Lucas

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  1. Ivana Anusic
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  2. Stevie C. Y. Yap
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  3. Richard E. Lucas
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Correspondence to Ivana Anusic.

Appendices

Appendix 1: Nonlinear Model Equation and R Script for the Traditional Nonlinear Model

The equation for the basic nonlinear model is a piecewise function specified as:

$${\text{Life}}\,{\text{Satisfaction = }}\left\{ {\begin{array}{*{20}c} {{\text{BA + PC\,*\,(1/{(1}}\text{ - }{\text{R}}_{\text{before}} )} )^{\text{yearEvent}} } \hfill & {{\text{if}}\,{\text{yearEvent < 0}}} \hfill \\ { ( {\text{BA + AC) + (PC}} - {\text{AC)\,*\,(1}} - {\text{R}}_{\text{after}} )^{\text{yearEvent}} } \hfill & {{\text{if}}\,{\text{yearEvent}} \ge 0} \hfill \\ \end{array} } \right.$$

where BA = baseline asymptote, PC = peak change, AC = asymptote change, R before  = pre-event rate of change, R after  = post-event rate of change, yearEvent = 0 in the year that the event occurred, and otherwise reflects the number of years from the event year (i.e., −2, −1, 0, 1, 2).

The R script to estimate the nonlinear model is:

Appendix 2: Nonlinear Model Equation and R Script for the Nonlinear Model with Normative Trends

The equation for the nonlinear model that includes the comparison group and models normative trends in life satisfaction is a piecewise function specified as:

$${\text{Life}}\,{\text{Satisfaction}}=\left\{ {\begin{array}{*{20}c} {\left. {\begin{array}{*{20}c} {{\text{yearStudy\,*\,YC + FY + PC\,*\,(1/{(1-}}{\text{R}}_{\text{before}} )} )^{\text{yearEvent}} } \hfill & {{\text{if}}\,{\text{yearEvent}} < 0} \hfill \\ {{\text{yearStudy\,*\,YC + (FY + AC) + (PC + AC)\,*\,{(1-}}{\text{R}}_{\text{after}})}^{\text{yearEvent}} } \hfill & {{\text{if}}\,{\text{yearEvent}} \ge 0} \hfill \\ \end{array} } \right\}{\text{for}}\,{\text{event}}\,{\text{group}}} \hfill \\ {\left. {\text{yearStudy\,*\,YC + FY + G}} \right\}\,{\text{for}}\,{\text{comparison}}\,{\text{group}}} \hfill \\ \end{array} } \right.$$

where YC = yearly change, FY = first year, PC = peak change, AC = asymptote change, R before  = pre-event rate of change, R after  = post-event rate of change, yearStudy = number of years in the study, with the first year in the study coded as 0, yearEvent = 0 in the year that the event occurred, and otherwise reflects the number of years from the event year (i.e., −2, −1, 0, 1, 2).

The R script to estimate the nonlinear model is:

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Anusic, I., Yap, S.C.Y. & Lucas, R.E. Testing Set-Point Theory in a Swiss National Sample: Reaction and Adaptation to Major Life Events. Soc Indic Res 119, 1265–1288 (2014). https://doi.org/10.1007/s11205-013-0541-2

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