Social Indicators Research

, Volume 130, Issue 2, pp 845–865 | Cite as

The Metrics of Subjective Wellbeing Data: An Empirical Evaluation of the Ordinal and Cardinal Comparability of Life Satisfaction Scores

Article

Abstract

This paper is motivated by the lack of consensus on the metrics of subjective wellbeing measurement scales. Subjective wellbeing data are frequently treated as though they are cardinally comparable both across and within individuals, though very little evidence exists to support these assumptions. Because wellbeing cannot be observed directly, cardinality must remain an assumption, which is usually imposed based on statistical convenience rather than on reason. The premise of this paper is that it is both possible and useful to make this assumption more informed. The analysis applies the principle of simultaneous conjoint measurement to improve our understanding of what information is contained within subjective wellbeing scores. Specifically, the metrics of the eleven-point numeric life satisfaction scale is evaluated using the MH5 mental health survey instrument. Under the assumption that the response function for MH5 is identifiable by the Rasch model, the shape of the response function for life satisfaction is potentially observable indirectly via the association between life satisfaction and MH5. The results presented here suggest life satisfaction scores are ordinally distinct, in terms of these mental health data, which supports the assumption of ordinal comparability. Under the aforementioned assumption, these scores are also approximately equidistant, which supports cardinal comparability. This pattern is found both across individuals and within individuals across time.

Keywords

Life satisfaction Subjective wellbeing Mental health Cardinality Ordinality Response functions Methodology 

References

  1. Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561–573.CrossRefGoogle Scholar
  2. Bernoulli, D. (1738 [1954]). Exposition of a new theory on the measurement of risk. Econometrica, 22, 23–36.Google Scholar
  3. Blanchflower, D. G., & Oswald, A. J. (2004). Well-being over time in Britain and the USA. Journal of Public Economics, 88, 1359–1386.CrossRefGoogle Scholar
  4. Blanchflower, D. G., & Oswald, A. J. (2005). Happiness and the human development index: The paradox of Australia. NBER Working Paper Series (No. 11416).Google Scholar
  5. Blanton, H., & Jaccard, J. (2006). Arbitrary metrics in psychology. American Psychologist, 61(1), 27–41.CrossRefGoogle Scholar
  6. Boyce, C. J. (2010). Understanding fixed effects in human well-being. Journal of Economic Psychology, 31, 1–16.CrossRefGoogle Scholar
  7. Boyce, C. J., & Wood, A. J. (2011). Personality and the marginal utility of income: Personality interacts with increases in household income to determine life satisfaction. Journal of Economic Behavior & Organization, 78, 183–191.CrossRefGoogle Scholar
  8. Brogden, H. E. (1977). The Rasch model, the law of comparative judgement and additive conjoint measurement. Psychometrika, 42, 631–634.CrossRefGoogle Scholar
  9. Clark, A. E. (2003). Unemployment as a social norm: Psychological evidence from panel data. Journal of Labour Economics, 21(2), 323–351.CrossRefGoogle Scholar
  10. Clark, A. E., Frijters, P., & Shields, M. A. (2008). Relative income, happiness, and utility: An explanation for the Easterlin Paradox and other puzzles. Journal of Economic Literature, 46(1), 95–144.CrossRefGoogle Scholar
  11. Crooker, K. J., & Near, J. P. (1998). Happiness and satisfaction: Measures of affect and cognition? Social Indicators Research, 44, 195–224.CrossRefGoogle Scholar
  12. Diener, E., & Lucas, R. E. (1999). Personality and subjective well-being. In D. Kahneman, E. Diener, & N. Schwartz (Eds.), Well-being: The foundations of hedonic psychology (pp. 213–229). New York: Sage.Google Scholar
  13. Edgeworth, Y. F. (1881 [1961]). Mathematical psychics: An essay on the application of mathematics to the moral sciences. New York: Augustus M. Kelly.Google Scholar
  14. Ferrer-i-Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimates of the determinants of happiness? The Economic Journal, 114(July), 641–659.CrossRefGoogle Scholar
  15. Gardner, J., & Oswald, A. J. (2001). Does money buy happiness? A longitudinal study using data on windfalls. Warwick: Warwick University.Google Scholar
  16. Gardner, J., & Oswald, A. (2006). Do divorcing couples become happier by braking up? Journal of the Royal Statistical Society: Series A (Statistics in Society), 169(2), 319–336.CrossRefGoogle Scholar
  17. Guttman, L. (1977). What is not what in statistics. The Statistician, 26, 81–107.CrossRefGoogle Scholar
  18. Headey, B., & Wooden, M. (2004). The effects of wealth and income on subjective well-being and ill-being. Economic Record, 80(Special Issue), S24–S33.CrossRefGoogle Scholar
  19. Hirschauer, N., Lehberger, M., & Musshoff, O. (2014). Happiness and utility in economic thought—or: What can we learn from happiness research for public policy analysis and public policy making? Social Indicators Research, 121, 647–674.CrossRefGoogle Scholar
  20. Katzner, D. W. (1998). The misuse of measurement in economics. Metroeconomica, 49(1), 1–22.CrossRefGoogle Scholar
  21. Kristoffersen, I. (2010). The metrics of subjective wellbeing: Cardinality, neutrality and additivity. The Economic Record, 86(272), 98–123.CrossRefGoogle Scholar
  22. Larsen, R. J., & Fredrickson, B. L. (1999). Measurement issues in emotional research. In D. Kahneman, E. Diener, & N. Schwarz (Eds.), Well-being: The foundations of hedonic psychology. New York: Sage.Google Scholar
  23. Lau, A. L. D. (2007). Measurement of subjective wellbeing: Cultural issues. In 9th Quality of Life Conference. Deakin University, Melbourne.Google Scholar
  24. Layard, R., Mayraz, G., & Nickell, S. (2008). The marginal utility of income. Journal of Public Economics, 92, 1846–1857.CrossRefGoogle Scholar
  25. Luce, R. D., & Tukey, J. W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1–27.CrossRefGoogle Scholar
  26. Masin, S. C., Zudini, V., & Antonelli, M. (2009). Early alternative derivations of Fechner’s law. Journal of the History of the Behavioral Sciences, 45(1), 56–65.CrossRefGoogle Scholar
  27. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2), 81–97.CrossRefGoogle Scholar
  28. Ng, Y.-K. (1996). Happiness surveys: Some comparability issues and an exploratory survey based on just perceivable increments. Social Indicators Research, 38, 1–27.CrossRefGoogle Scholar
  29. Ng, Y.-K. (2008). Happiness studies: Ways to improve comparability and some public policy implications. The Economic Record, 84(265), 253–266.CrossRefGoogle Scholar
  30. Oswald, A. (2008). On the curvature of the reporting function from objective reality to subjective feelings. Economics Letters, 100(3), 369–372.CrossRefGoogle Scholar
  31. Parducci, A. (1995). Happiness, pleasure, and judgment: The contextual theopry and its applications. Hillsdale, NJ: Erlbaum.Google Scholar
  32. Perneger, T. V., & Bovier, P. A. (2001). Application of the Rasch model to the SF36 mental health 5 item scale (MH5). ISPOR Sixth Annual International Meeting, Value In Health.Google Scholar
  33. Raczek, A. E., Ware, J. E., Bjorner, J. B., Gandek, B., Haley, S. M., Aaronson, N. K., et al. (1998). Comparisons of Rasch and summated rating scales constructed from SF-36 physical functioning items in seven countries: Results from the IQOLA Project. International Quality of Life Assessment. Journal of Clinical Epidemiology, 51(11), 1203–1214.CrossRefGoogle Scholar
  34. Rasch, G. (1961). On general laws and the meaning of measurement in psychology. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability Berkeley, California, University of California Press.Google Scholar
  35. Sandvik, E., Diener, E., & Seidlitz, L. (1993). Subjective well-being: The convergence and stability of self-report and non-self-report measures. Journal of Personality, 61, 317–342.CrossRefGoogle Scholar
  36. Savage, L. J. (1954). The foundations of statistics. New York: Wiley.Google Scholar
  37. Schwartz, N. (1995). What respondents learn from questionnaires: The survey interview and the logic of conversation. International Statistical Review, 63, 153–177.CrossRefGoogle Scholar
  38. Van Praag, B. M. S. (1991). Ordinal and cardinal utility: An integration of the two dimensions of the welfare concept. Journal of Econometrics, 50, 69–89.CrossRefGoogle Scholar
  39. Van Praag, B. M. S., & Ferrier-i-Carbonell, A. (2004). Happiness quantified. New York: Oxford University Press.CrossRefGoogle Scholar
  40. Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (2000). SF-36 health survey: Manual and interpretation guide. Lincoln, RI: QualityMetric Inc.Google Scholar
  41. Wright, B. D. (1997). Measurement for social science and education: History of social science measurement. http://www.rasch.org/memo62.htm.

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.School of BusinessThe University of Western AustraliaPerthAustralia

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