Abstract
Subjective measures of well-being are increasingly seen by scholars and policy makers as valuable tools to assess quality of life. Hedonic accounts focus on people’s experience of life in positive ways while eudaimonic accounts are concerned with realization of personal potential. However, to what extent do an “enjoyable” and a “flourishing” life overlap? Using an innovative clustering-and-projection technique (Self-Organized Map), the joint distributional patterns of multiple hedonic and eudaimonic well-being indicators were examined in a nationally representative longitudinal study of US adults (MIDUS). Results show that the two accounts largely converged with about 70% of the sample observations registering high/low scores in both well-being dimensions. However, the remaining 30% of respondents experienced divergent well-being levels. Association between these combined profiles and a series of socio-demographic characteristics and social stratification factors were investigated. Findings showed that chances of uniformly high well-being increase with age, while higher income, educational level, marriage, and being a female are linked to lesser probabilities of experiencing joint low well-being patterns. Experiencing a combination of high hedonic/low eudaimonic well-being was more frequent for less educated individuals, and men. Finally, the persistence over time of these combined well-being profiles was more frequent in case of convergent hedonic/eudaimonic levels. For divergent patterns we revealed substantial changes over a 10-year period with respondents registering low hedonic/high eudaimonic well-being at time t having greater chances of upward movement toward improved well-being compared to individuals who experienced high hedonic/low eudaimonic levels in the first time period.
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Notes
Formally, in the first step of the learning process each observation is assigned to its BMU or node with closest weight vector mi according to the following equation: ||x − mc||= mini {||x − mi||}. The learning process then continues with the weight vectors of the BMU and its closest neighbors in the map being updated over T iterations, called learning epochs. Such adjustments aim at ensuring a better match with the input vectors and are done as follows: mi (t + 1) = mi (t) + hci(t) [x(t)—mi (t)] where mi is the weight vector; x is the input vector; and hci is the neighborhood function around the winner unit indexed c, monotonically decreasing over time and normally taken to be Gaussian around the BMU.
The quantization error, normalized so as to take values in the interval (0–1), is a measure of the SOM resolution and corresponds to the average distance between each input vector and its best matching unit (BMU). Our self-organized map exhibited a normalized quantization error equal to 0.016. This means that, on average, each element of the input vector differed from its corresponding BMU weight vector by 1.6 percentage points. The topographic error, on the other hand, is a measure of the SOM’s degree of topology preservation and corresponds to the proportion of all input vectors for which the best matching unit and the second-best matching unit are not adjacent on the two-dimensional grid; our SOM exhibited a topographic error equal to 0.009, meaning that only 43 out of 4786 observations are affected by some degree of ‘topological misplacement’.
Note, every single node composing the five macro clusters combines all nine well-being indicators at the levels presented in the component planes above.
However, such evidence should be regarded with caution as there are only 68 cases of unemployment present in our subsample.
These well-being profiles were additionally confirmed through the implementation of the SOM technique on the same set of PWB and SWB variables collected for a different nationally representative sample of 2353 American adults (MIDUS Refresher 2013) replicating the MIDUS baseline study (1995/96). Figure 3 reports the component planes of the single well-being indicators which, clearly, follow the same distributional patterns as in the MIDUS II/MIDUS III sample discussed in this inquiry.
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Pancheva, M.G., Ryff, C.D. & Lucchini, M. An Integrated Look at Well-Being: Topological Clustering of Combinations and Correlates of Hedonia and Eudaimonia. J Happiness Stud 22, 2275–2297 (2021). https://doi.org/10.1007/s10902-020-00325-6
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DOI: https://doi.org/10.1007/s10902-020-00325-6