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Measuring Indecision in Happiness Studies

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Metrics of Subjective Well-Being: Limits and Improvements

Part of the book series: Happiness Studies Book Series ((HAPS))

Abstract

The main objective of this paper is to evaluate the degree of uncertainty in self-reported happiness responses by means of a statistical model able to detect the relevant features of the expressed ratings. We consider a mixture model to address a twofold research question: how can we measure the indecision in expressed well-being; how to assess if this latent trait varies depending on the covariates of those surveyed? The selected modelling approach investigates the feeling/agreement component, making the underlying indecision explicit without imposing extra constraints to the model. Furthermore, our proposal allows to enhance the presence of a “refuge” option in the response patterns. The effects of individual characteristics may be highlighted, when significant. Results are presented stemming from an observational study showing that responses are characterized by a large variability among subjects. The methodology here experimented may be considered a general one since it can be exploited both in observational and in experimental surveys.

There is in the world a great variety of things which give satisfaction: there are at least as many kinds of satisfaction as there are different kinds of goods.

Wladyslaw Tatarkiewicz, Analysis of Happiness, 1976

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References

  • Agresti, A. (2010). Analysis of ordinal categorical data (2nd ed.). Hoboken: Wiley.

    Book  Google Scholar 

  • Bartolini, S., & Bilancini, E. (2010). If not only GDP, what else? Using relational goods to predict the trends of subjective well-being. International Review of Economics, 57, 199–213.

    Article  Google Scholar 

  • Bauman, Z. (1998). Work, consumerism and the new poor. Buckingham: Open University Press.

    Google Scholar 

  • Bertrand, M., & Mullainathan, S. (2001). Do people mean what they say? Implications for subjective survey data. American Economic Review, Papers and Proceedings, 91(2), 67–72.

    Article  Google Scholar 

  • Bjørnskov, C. (2010). How comparable are the Gallup World poll life satisfaction data? Journal of Happiness Studies, 11(1), 41–60.

    Article  Google Scholar 

  • Blanchflower, D., & Oswald, A. J. (2011). International happiness: A new view on the measure of performance. Academy of Management Perspectives, 25(1), 6–22.

    Article  Google Scholar 

  • Brulé, G., & Veenhoven, R. (2017). The ‘10 Excess’ phenomenon in responses to survey questions on happiness. Social Indicators Research. doi:10.1007/s11205-016-1265-x.

    Google Scholar 

  • Bruni, L. (2008). Reciprocity, altruism and the civil Society. Routledge, New York

    Google Scholar 

  • Bruni, L., & Stanca, L. (2008). Watching alone: Relational goods, television and happiness. Journal of Economic Behavior & Organization, 65, 506–528.

    Article  Google Scholar 

  • Cantril, H. (1965). The pattern of human concerns. New Brunswick: Rutgers University Press.

    Google Scholar 

  • Capecchi, S. (2015). Modelling the perception of conflict in working conditions. Electronic Journal of Applied Statistical Analysis, 8, 298–311.

    Google Scholar 

  • Capecchi, S., & Ghiselli, S. (2014). Modelling job satisfaction of Italian graduates. In: M. Carpita et al. (Eds.), Studies in theoretical and applied statistics. Springer, Berlin, pp. 37–48.

    Google Scholar 

  • Capecchi, S., & Piccolo, D. (2014). Modelling the latent components of personal happiness. In C. Perna & M. Sibillo (Eds.), Mathematical and statistical methods for actuarial sciences and finance (pp. 49–52). Berlin: Springer.

    Chapter  Google Scholar 

  • Capecchi, S., & Piccolo, D. (2016). Dealing with heterogeneity in ordinal responses. Quality & Quantity. doi:10.1007/s11135-016-0393-3.

    Google Scholar 

  • Capecchi, S., Iannario, M., & Simone, R. (2016). Well-being and relational goods: A model-based approach to detect significant relationships. Social Indicators Research. doi:10.1007/s11205-016-1519-7.

    Google Scholar 

  • Corrado, L., & Joxhe, M. (2016). The effect of survey design on extreme response style: Rating job satisfaction. CEIS working paper (Vol. 365), January 2016.

    Google Scholar 

  • Dale, G. (2011). Lineages of embeddedness: On the antecedents and successors of a polanyian concept. The American Journal of Economics and Sociology, Social Methods, and Microeconomics: Contributions to Doing Economics Better, 70(2), 306–339. April 2011.

    Article  Google Scholar 

  • D’Elia, A. (2003). Finite sample performance of the E-M algorithm for ranks data modelling. Statistica, 63, 41–51.

    Google Scholar 

  • D’Elia, A., & Piccolo, D. (2005). A mixture model for preference data analysis. Computational Statistics & Data Analysis, 49, 917–934.

    Article  Google Scholar 

  • Dolan, P., & Metcalfe, R. (2012). Measuring subjective wellbeing: recommendations on measures for use by national governments. Journal of Social Policy, 41(2), 409–427.

    Article  Google Scholar 

  • Dolan, P., Peasgood, T., & White, M. (2008). Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being. Journal of Economic Psychology, 29, 94–122.

    Article  Google Scholar 

  • Dolnicar, S., & Grün, B. (2013). Validly measuring destination image in survey studies. Journal of Travel Research, 52(1), 3–14.

    Article  Google Scholar 

  • Easterlin, R. A. (1974). Does economic growth improve the human lot? In P. A. David, & M. W. Reder (Eds.), Nations and households in economic growth: Essays in honor of Moses Abramovitz. Academic Press, Inc., New York.

    Google Scholar 

  • Eurofound. (2013). Third European quality of life survey “Quality of life in Europe: Trends 2003–2012”. Luxembourg: Publications Office of the European Union.

    Google Scholar 

  • European Commission. (2010). Europe 2020. A strategy for smart, sustainable and inclusive growth. Communication from the Commission of 3 March 2010, Bruxelles.

    Google Scholar 

  • Ferguson, N. (2010). Complexity and collapse: Empires on the edge of chaos. Foreign Affairs, 89(2), 18–32.

    Google Scholar 

  • Frey, B., & Stutzer, A. (2002). What can economists learn from happiness research? Journal of Economic Literature, XL, 402–435

    Google Scholar 

  • Gadrich, T., Bashkansky, E., & Zitickis, R. (2015). Assessing variation: A unifying approach for all scales of measurement. Quality and Quantity, 49, 1145–1167.

    Google Scholar 

  • Gambacorta, R., & Iannario, M. (2013). Measuring job satisfaction with CUB models, LABOUR, 27, 198–224.

    Google Scholar 

  • Georgescu Roegen, N. (1971). The entropy law and the economic process. Cambridge, Massachusetts: Harvard University Press.

    Google Scholar 

  • Gottard, A., Iannario, M., & Piccolo, D. (2016). Varying uncertainty in CUB models. Advances in Data Analysis and Classification. doi:10.1007/s11634-016-0235-0

  • Helliwell, J. F., Layard, R., & Sachs, J. (2015). World happiness report 2015. New York: Sustainable Development Solutions Network.

    Google Scholar 

  • Iannario, M. (2012a). Modelling shelter choices in a class of mixture models for ordinal responses. Statistical Methods and Applications, 21, 1–22.

    Article  Google Scholar 

  • Iannario, M. (2012b). Hierarchical CUB models for ordinal variables. Communications in Statistics. Theory and Methods, 41, 3110–3125.

    Article  Google Scholar 

  • Iannario, M. (2014). Modelling uncertainty and overdispersion in ordinal data. Communications in Statistics. Theory and Methods, 43, 771–786.

    Article  Google Scholar 

  • Iannario, M. (2015a). Detecting latent components in ordinal data with overdispersion by means of a mixture distribution. Quality & Quantity, 49, 977–987.

    Article  Google Scholar 

  • Iannario, M. (2015b). Modelling scale effects and uncertainty in rating surveys. Electronic Journal of Applied Statistical Analysis, 8, 329–345.

    Google Scholar 

  • Iannario, M., & Piccolo, D. (2015). A generalized framework for modelling ordinal data. Statistical Methods and Applications, 25, 163–189.

    Article  Google Scholar 

  • Iannario, M., & Piccolo, D. (2016). A comprehensive framework of regression models for ordnal data. Metron, 74, 233–252.

    Article  Google Scholar 

  • Iannario, M., Piccolo, D., & Simone, R. (2016). CUB: A class of mixture models for ordinal data. R package version 1.0. http://CRAN.R-project.org/package=CUB

  • Iannario, M., Manisera, M., & Zuccolotto, P. (2016). Treatment of “don’t know” responses in the consumers’ perceptions about sustainability in the agri-food sector. Quality & Quantity, 51, 765–778.

    Article  Google Scholar 

  • Kahneman, D. (2000). Experienced utility and objective happiness: A moment-based approach. In D. Kahneman & A. Tversky (Eds.), Choices, values and frames. Massachusetts: The Russell Sage Foundation, Cambridge University Press, Cambridge.

    Google Scholar 

  • Kankaraš, M., Vermunt, J. K., & Moors, G. (2011). Measurement equivalence of ordinal items: A comparison of factor analytic, item response theory, and latent class approaches. Sociological Methods & Research, 40(2), 279–310.

    Article  Google Scholar 

  • Kapteyn, A., Van Praag, B. M. S., & Van Herwaarden, F. G. (1976). Individual welfare functions and social reference spaces. Economic Institute of Leyden University, Report 76.1.

    Google Scholar 

  • Layard, R. (2005). Happiness, lessons from a new science. New York: The Penguin Press.

    Google Scholar 

  • Maggino, F. (2009). The state of the art in indicators construction in the perspective of a comprehensive approach in measuring well-being of societies. Archivio E-Prints, Firenze: Firenze University Press.

    Google Scholar 

  • Maggino, F. (2016). Challenges, needs and risks in defining wellbeing indicators. In F. Maggino (Ed.), A life devoted to quality of life festschrift in Honor of Alex C. Michalos (pp. 209–235). Springer International Publishing, Switzerland.

    Google Scholar 

  • Manisera, M., & Zuccolotto, P. (2014a). Modeling “Don’t know” responses in rating scales. Pattern Recognition Letters, 45, 226–234.

    Article  Google Scholar 

  • Manisera, M., & Zuccolotto, P. (2014b). Modeling rating data with Nonlinear CUB models. Computational Statistics and Data Analysis, 78, 100–118.

    Article  Google Scholar 

  • Michalos, A. C. (1997). Combining social, economic and environmental indicators to measure sustainable human well-being. Social Indicators Research, 40, 221–258.

    Article  Google Scholar 

  • Michalos, A. C. (Ed.) (2014). Encyclopedia of quality of life and well-being. Dordrecht, The Netherlands: Springer.

    Google Scholar 

  • Mikucka, M., Sarracino, F., & Dubrow, J. K. (2017). When does economic growth improve life satisfaction? Multilevel analysis of the roles of social trust and income inequality in 46 Countries, 1981–2012. World Development, 93, 447–459.

    Article  Google Scholar 

  • OECD. (2013). OECD guidelines on measuring subjective well-being. OECD Publishing. Available at http://dx.doi.org/10.1787/9789264191655-en

  • Ostrom, E. (2009). A general framework for analyzing the sustainability of social-ecological systems. Science, 325(5939), 419–422.

    Article  Google Scholar 

  • Piccolo, D. (2003). On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica, 5, 85–104.

    Google Scholar 

  • Piccolo, D. (2006). Observed information matrix for MUB models. Quaderni di Statistica, 8, 33–78.

    Google Scholar 

  • Piccolo, D. (2015). Inferential issues on CUBE models with covariates. Communications in Statistics. Theory and Methods, 44, 5023–5036.

    Article  Google Scholar 

  • Piccolo, D., & D’Elia, A. (2008). A new approach for modelling consumers’ preferences. Food Quality and Preference, 19, 247–259.

    Article  Google Scholar 

  • Polanyi, K. (1944). The great transformation: The political and economic origins of our time. College of Arts and Sciences, University of Tennessee.

    Google Scholar 

  • Pugno, M. (2016). On the foundations of happiness in economics: reinterpreting Tibor Scitovsky. Abingdon, Oxon, New York: Routledge.

    Google Scholar 

  • Putnam, R. D. (1995). Bowling alone: America’s declining social capital. Journal of democracy, 6(1), January.

    Google Scholar 

  • Ravallion, M. (2012). Poor, or just feeling poor? On subjective data in measuring poverty, Policy Research Working Paper, 5968, The World Bank, Washington, DC.

    Google Scholar 

  • Rossiter, J. R., Dolnicar, S., & Gran, B. (2015). Why level-free forced choice binary measures of brand benefit beliefs work well. International Journal of Market Research, 57(2), 1–9.

    Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of research on hedonic and eudaemonic well-being. Annual Review of Psychology, 52, 141–166.

    Google Scholar 

  • Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069–1081.

    Article  Google Scholar 

  • Sarracino, F. (2014). Richer in money, poorer in relationships and unhappy? Time-series comparisons of social capital and well-being in Luxembourg. Social Indicators Research, 115, 561–622.

    Article  Google Scholar 

  • Stiglitz, J. E., & Uzawa, H. (1969). Readings in the modern theory of economic growth. Cambridge, Massachusetts: MIT Press.

    Google Scholar 

  • Tourangeau, R. (1984). Cognitive science and survey methods. In T. Jabine et al. (Eds.), Cognitive aspects of survey methodology: building a bridge between disciplines (pp. 73–100). Washington: National Academy Press.

    Google Scholar 

  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Article  Google Scholar 

  • Tutz, G. (2012). Regression for categorical data. Cambridge: Cambridge University Press.

    Google Scholar 

  • Tutz, G., Schneider, M., & Iannario, M. (2016). Mixture models for ordinal responses to account for uncertainty of choice. Advanced in Data Analysis and Classification. doi:10.1007/s11634-016-0247-9.

    Google Scholar 

  • Uhlaner, C. J. (1989). Relational goods and participation, incorporating sociability into a theory of rational action. Public Choice, 62, 253–285.

    Article  Google Scholar 

  • Van Praag, B. (1968). Individual welfare functions and consumer behavior. Amsterdam: North-Holland.

    Google Scholar 

  • Veenhoven, R. (2000). The four qualities of life: Ordering concepts and measures of the good life. Journal of Happiness Studies, 1, 1–39.

    Article  Google Scholar 

  • Veenhoven, R. (2016). What we have learnt about happiness: classic qualms in the light of recent research. In F. Maggino (Ed.), A life devoted to quality of life festschrift in honor of Alex C. Michalos (pp. 151–170). Switzerland: Springer International Publishing.

    Google Scholar 

  • Veenhoven, R. (2017). Bibliography of Happiness, World database of happiness. The Netherlands: Erasmus University Rotterdam. Assessed on March 13, 2017 at: http://worlddatabaseofhappiness.eur.nl/hap_bib/bib_fp.php

  • Yusoff, R., & Janor, R. M. (2014). Generation of an interval metric scale to measure attitude, SAGE Open, January–March, 1–16.

    Google Scholar 

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Authors and Affiliations

  1. Department of Political Sciences, University of Naples Federico II, Via Leopoldo Rodinò 22, 80138, Naples, Italy

    Stefania Capecchi

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  1. Stefania Capecchi
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Correspondence to Stefania Capecchi .

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Editors and Affiliations

  1. Happiness Studies, Erasmus University Rotterdam Happiness Studies, Rotterdam, The Netherlands

    Gaël Brulé

  2. Dipartimento di Scienze Statistiche, Università di Roma "La Sapienza" , Roma, Italy

    Filomena Maggino

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Capecchi, S. (2017). Measuring Indecision in Happiness Studies. In: Brulé, G., Maggino, F. (eds) Metrics of Subjective Well-Being: Limits and Improvements. Happiness Studies Book Series. Springer, Cham. https://doi.org/10.1007/978-3-319-61810-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-61810-4_7

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