The scientific value of numerical measures of human feelings

Abstract

Human feelings measured in integers ( my happiness is an 8 out of 10 , my pain 2 out of 6 ) have no objective scientific basis. They are “made-up” numbers on a scale that does not exist. Yet such data are extensively collected—despite criticism from, especially, economists—by governments and international organizations. We examine this paradox. We draw upon longitudinal information on the feelings and decisions of tens of thousands of randomly sampled citizens followed through time over four decades in three countries ( n = 700,000 approximately). First, we show that a single feelings integer has greater predictive power than does a combined set of economic and social variables. Second, there is a clear inverse relationship between feelings integers and subsequent get-me-out-of-here actions (in the domain of neighborhoods, partners, jobs, and hospital visits). Third, this feelings-to-actions relationship takes a generic form, is consistently replicable, and is fairly close to linear in structure. Therefore, it seems that human beings can successfully operationalize an integer scale for feelings even though there is no true scale. How individuals are able to achieve this is not currently known. The implied scientific puzzle—an inherently cross-disciplinary one—demands attention.

Gratitude, self-esteem, and optimism distinctively impact the subjective and psychological well-being of Japanese individuals

“…Furthermore, the results from the multivariate regression analyses, including the effect sizes, are Another limitation of the current results is the fact that all data was obtained via self-reports, at times using ratings systems that may not feel entirely natural to respondents, and which may be affected by demand characteristics and influenced by sociodemographic and cultural factors. Though there is no question that self-reports are valuable (Howard, 1994) and can provide unique insights on the affective-cognitive processes underlying the measures of interest (Kaiser & Oswald, 2022), future work should seek for converging evidence using behavioral, physiological or observer data. Arguably, experience sampling maximizes ecological validity, making it less prone to suffer from some of the deficiencies found in laboratory-based paradigms.…”

Computational models of subjective feelings in psychiatry

“…Fifth, we can evaluate whether self-report ratings are associated with subsequent behavior. For example, low mood in the current situation (e.g., mood in the current job) can be associated with change from the current situation (e.g., switching jobs) (Kaiser & Oswald, 2022). Participants in a high mood act as if rewards are perceived as better than they are, choosing rewarded options more frequently (Eldar & Niv, 2015).…”

New approaches to measuring welfare

“…23 The measure also correlates well with objective outcomes such as health and longevity 24 or productivity, 25 and it has been shown to be a predictor for individual behaviour such as voting for the incumbent government 26 or 'get-me-out-of-here' actions. 27 Importantly, the measure is uniquely capable of capturing important behavioural scientific phenomena (and, crucially, of integrating these into social welfare analyses), including anticipation and misprediction, 28 adaptation to changing life circumstances, 29 relative comparisons and status concerns, 30 well-being spillovers from one person to another, 31 and important aspects related to procedural utility such as being treated fairly and with dignity, which may be just as important as the target outcome of a policy itself. 32 Importantly, data on life satisfaction are cheap and easy to collect, easy to interpret and straightforward to analyse.…”