Abstract
Subjective well-being (SWB) research is characterized by many large samples, which often results in virtually all variables being significantly related to well-being, even if the associations are small. In this article we explore the strengths of associations between various predictors and SWB outcomes. In addition to standard effect-size statistics, we also examined the range of the SWB scale covered in the distribution of the predictor, allowing us to estimate the strength of influence of each variable, independent of variability in the sample. We analyzed just a few variables to illustrate what our approach reveals. Our analyses included a representative sample of both the world and the United States, and our data included three types of SWB (life satisfaction (LS), positive affect (PA), and negative affect (NA)). The largest effect sizes emerged for societal characteristics, such as between-nations differences, as well as personal characteristics, such as perceived social support. Small or very small effect sizes were consistently found for demographic characteristics, such as sex, age, and marital status. Other effect sizes varied by the type of SWB being considered. For example, income resulted in a large effect size for LS, but small to medium effect sizes for PA and NA. We suggest that when scholars report and interpret the associations of predictor variables with SWB, they consider the strengths of their significant associations.
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Notes
However, this study was not conducted across nations, and therefore omitted an important source of variability.
Unless otherwise noted, these items were coded as 1 = Yes, 2 = No.
The religiosity item was only included in earlier versions of the World Poll (2005–2011), so the sample size is smaller here than for all other variables.
We used each group’s standard deviation and sample size to compute pooled variance in the denominator of our Cohen’s d calculation.
Cohen’s d effect sizes were interpreted as small, medium, or large using standards in the field (e.g., Cohen 1988). We recognize that these interpretations are arbitrary, and different scholars have different suggestions for these benchmarks. However, these labels give us a way to compare the impact of each SWB predictor relative to the others, and thus we use the listed benchmarks for our effects, with the recognition that comparing the effect size values themselves (as we do in Table 2) is more precise.
In addition to calculating a percent variance explained measure of effect size, we also computed Pearson’s r correlations with age, life satisfaction, PA, and NA. The correlation between age and life satisfaction was of medium size (r = .061), while the correlations between age and PA, and age and NA, were small-to-medium (r’s = .027, .035, respectively). This suggests that there is a moderate linear association between age and SWB.
We used this technique consistently across all self-report items, unless otherwise noted.
Age was not included in the percent range of scale analyses, as we were interested in the effects of age across the adult lifespan, rather than comparing any two specific age groups.
The decision to include v. exclude certain variables was determined by their presence v. absence in the Gallup-Sharecare 2015 Daily survey. That is, we excluded variables that were present in the Gallup World Poll, but were absent or worded differently in the Gallup-Sharecare 2015 Daily survey.
Note that this differs from the measurement categories utilized in the Gallup World Poll, which ranged from making 0–365 International Dollars, to making 150,000 or more International Dollars.
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Geerling, D.M., Diener, E. Effect Size Strengths in Subjective Well-Being Research. Applied Research Quality Life 15, 167–185 (2020). https://doi.org/10.1007/s11482-018-9670-8
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DOI: https://doi.org/10.1007/s11482-018-9670-8