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Journal of Happiness Studies

, Volume 15, Issue 4, pp 915–935 | Cite as

Evaluation of Affect in Mexico and Spain: Psychometric Properties and Usefulness of an Abbreviated Version of the Day Reconstruction Method

  • Francisco Félix Caballero
  • Marta Miret
  • Beatriz Olaya
  • Jaime Perales
  • Ruy López-Ridaura
  • Josep Maria Haro
  • Somnath Chatterji
  • José Luis Ayuso-MateosEmail author
Research Paper

Abstract

The aims of the present study were to assess the psychometric properties of the Spanish-language version of the abbreviated Day Reconstruction Method (DRM), and to investigate differences in affective experience in Mexico and Spain. A total of 2,629 adults from Mexico and 4,583 from Spain were interviewed. Information was obtained using an abbreviated version of the DRM, which had been translated into Spanish. Reliability, validity, and the structure of affect were assessed and compared between countries. The diurnal variation of affect, the changes in affect along the life span, time use, and the relationship between affect and socio-demographic characteristics were also analysed. Adequate psychometric properties for the Spanish-language version of the abbreviated DRM were found in both the Mexican and the Spanish samples, and affect tended to improve along the life span in both countries. However, net affect did not have the same distribution function (Kolmogorov–Smirnov statistic = 0.25, p < 0.001) in both countries, being higher in Spain. Moreover, both samples showed opposite patterns in the diurnal variation of affect. The results showed that the Spanish-language version of the DRM is a feasible and valid method to measure affect, its diurnal rhythms, and time use in large-scale surveys.

Keywords

Day Reconstruction Method (DRM) Subjective well-being Net affect U-index 

Notes

Acknowledgments

This paper uses data from WHO SAGE and from COURAGE in Europe. WHO’s Study on Global Ageing and Adult Health is supported by the United States National Institute on Aging’s Division of Behavioral and Social Research through Interagency Agreements (OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005-01) and through a research grant (R01-AG034479) and the World Health Organization’s Department of Health Statistics and Information Systems. The research leading to these results has also received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under Grant agreement Number 223071 (COURAGE in Europe), the Instituto de Salud Carlos III-FIS research Grants Number PS09/00295 and PS09/01845, the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009-1010), and the Mental Health and Disability Instrument Library Platform (CIBERSAM). The study was supported by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III. J.P. is grateful to the Instituto de Salud Carlos III for a predoctoral grant (PFIS).

References

  1. Ayuso-Mateos, J. L., Miret, M., Caballero, F. F., Olaya, B., Haro, J. M., Kowal, P., et al. (2013). Multi-country evaluation of affective experience: Validation of an abbreviated version of the day reconstruction method in seven countries. PLoS ONE, 8, e61534.CrossRefGoogle Scholar
  2. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.CrossRefGoogle Scholar
  3. Burnham, K. P., & Anderson, D. R. (2011). Model selection and multimodel inference: A practical information-theoretic approach. New York: Springer.Google Scholar
  4. Bylsma, L. M., Taylor-Clift, A., & Rottenberg, J. (2011). Emotional reactivity to daily events in major and minor depression. Journal of Abnormal Psychology, 120, 155–167.CrossRefGoogle Scholar
  5. Carstensen, L. L. (2006). The influence of a sense of time on human development. Science, 312, 1913–1915.CrossRefGoogle Scholar
  6. Carstensen, L. L., Pasupathi, M., Mayr, U., & Nesselroade, J. R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79, 644–655.CrossRefGoogle Scholar
  7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York: Academic Press.Google Scholar
  8. Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. The Journal of Nervous and Mental Disease, 175, 526–536.CrossRefGoogle Scholar
  9. Daly, M., Delaney, L., Doran, P. P., Harmon, C., & MacLachlan, M. (2010). Naturalistic monitoring of the affect-heart rate relationship: A day reconstruction study. Health Psychology, 29, 186–195.CrossRefGoogle Scholar
  10. Deaton, A. (2008). Income, health, and well-being around the world: Evidence from the Gallup World Poll. Journal of Economic Perspectives, 22, 53–72.CrossRefGoogle Scholar
  11. Depp, C. A., Schkade, D. A., Thompson, W. K., & Jeste, D. V. (2010). Age, affective experience, and television use. American Journal of Preventive Medicine, 39, 173–178.CrossRefGoogle Scholar
  12. Diener, E., Ng, W., Harter, J., & Arora, R. (2010). Wealth and happiness across the world: Material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling. Journal of Personality Social Psychology, 99, 52–61.CrossRefGoogle Scholar
  13. Dockray, S., Grant, N., Stone, A. A., Kahneman, D., Wardle, J., & Steptoe, A. (2010). A comparison of affect ratings obtained with ecological momentary assessment and the day reconstruction method. Social Indicators Research, 99, 269–283.CrossRefGoogle Scholar
  14. Eurostat. (2008). Gini coefficient. Available online at http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=ilc_di12&lang=en. Accessed June 24, 2013.
  15. Feldt, L. S. (1969). A test of the hypothesis that Cronbach’s Alpha or Kuder-Richardson coefficient twenty is the same for two tests. Psychometrika, 34, 363–373.CrossRefGoogle Scholar
  16. Helliwell, J., Layard, R., & Sachs, J. (2012). World happiness report. Available online at http://www.earth.columbia.edu/sitefiles/file/Sachs%20Writing/2012/World%20Happiness%20Report.pdf. Accessed June 24, 2013.
  17. Hox, J. J., Mass, C. J. M., & Brinkhuis, J. S. (2010). The effect of estimation method and sample size in multilevel structural equation modeling. Statistica Neerlandica, 64, 157–170.CrossRefGoogle Scholar
  18. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  19. Inglehart, R. F. (2010). Faith and freedom: Traditional and modern ways to happiness. In E. Diener, J. F. Helliwell, & D. Kahneman (Eds.), International differences in well-being (pp. 351–397). New York: Oxford University Press.CrossRefGoogle Scholar
  20. Kahneman, D., & Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences of the United States of America, 107, 16489–16493.CrossRefGoogle Scholar
  21. Kahneman, D., & Krueger, A. (2006). Developments in the measurement of subjective well-being. Journal of Economic Perspectives, 20, 3–24.CrossRefGoogle Scholar
  22. Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science, 306, 1776–1780.CrossRefGoogle Scholar
  23. Krueger, A., Kahneman, D., Schkade, D., Schwartz, J. E., & Stone, A. (2008). National time accounting: The currency of life. Available online at www.nber.org/chapters/c5053.pdf?new_window=1. Accessed June 24, 2013.
  24. Krueger, A. B., & Schkade, D. A. (2008). The reliability of subjective well-being measures. Journal of Public Economics, 92, 1833–1845.CrossRefGoogle Scholar
  25. Krueger, A. B., & Stone, A. A. (2008). Assessment of pain: A community-based diary survey in the USA. Lancet, 371, 1519–1525.CrossRefGoogle Scholar
  26. Liu, H. Y., & Weng, L. J. (2009). An effect size index for comparing two independent alpha coefficients. British Journal of Mathematical and Statistical Psychology, 62, 385–400.CrossRefGoogle Scholar
  27. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison-Wesley.Google Scholar
  28. McCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130–149.CrossRefGoogle Scholar
  29. McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  30. Miret, M., Caballero, F. F., Mathur, A., Naidoo, N., Kowal, P., Ayuso-Mateos, J. L., et al. (2012). Validation of a measure of subjective well-being: An abbreviated version of the Day Reconstruction Method. PLoS ONE, 7, e43887.CrossRefGoogle Scholar
  31. Mulaik, S. A. (1972). The foundations of factor analysis. New York: Mc Graw-Hill.Google Scholar
  32. Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide (4th ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
  33. Organisation for Economic Co-operation and Development (OECD). (2012). OECD better life index. Available online at http://www.oecdbetterlifeindex.org. Accessed June 24, 2013.
  34. R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
  35. Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York: Routledge.Google Scholar
  36. Reise, S. P., Widaman, K. F., & Pugh, R. H. (1993). Confirmatory factor analysis and item response theory: Two approaches for exploring measurement invariance. Psychological Bulletin, 114, 552–566.CrossRefGoogle Scholar
  37. Revelle, W., & Rocklin, T. (1979). Very simple structure: An alternative procedure for estimating the optimal number of interpretable factors. Multivariate Behavioral Research, 14, 403–414.CrossRefGoogle Scholar
  38. Schreider, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Education Research, 99, 323–337.CrossRefGoogle Scholar
  39. Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  40. Schwarz, G. (1978). Estimating dimension of a model. Annals of Statistics, 6, 461–464.CrossRefGoogle Scholar
  41. Scorsolini-Comin, F., & Dos Santos, M. A. (2010). The scientific study of happiness and health promotion: An integrative literature review. Revista Latino-Americana de Enfermagem, 18, 472–479.CrossRefGoogle Scholar
  42. Smith, D. M., Brown, S. L., & Ubel, P. A. (2008). Are subjective well-being measures any better than decision utility measures? Health Economics, Policy, and Law, 3, 85–91.CrossRefGoogle Scholar
  43. StataCorp. (2010). Stata statistical software. Release 11. College Station, TX: Stata Corporation.Google Scholar
  44. Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modelling. Personality and Individual Differences, 42, 893–898.CrossRefGoogle Scholar
  45. Stiglitz, J. E., Sen, A., & Fitoussi, J. P. (2009). Report by the Commission on the measurement of economic performance and social progress. Available online at http://www.stiglitz-sen-fitoussi.fr/documents/rapport_anglais.pdf. Accessed June 24, 2013.
  46. Stone, A. A., Schwartz, J. E., Broderick, J. E., & Deaton, A. (2010). A snapshot of the age distribution of psychological well-being in the United States. Proceedings of the National Academy of Sciences of the United States of America, 107, 9985–9990.CrossRefGoogle Scholar
  47. Stone, A. A., Schwartz, J. E., Schkade, D., Schwarz, N., Krueger, A., & Kahneman, D. (2006). A population approach to the study of emotion: Diurnal rhythms of a working day examined with the Day Reconstruction Method. Emotion, 6, 139–149.CrossRefGoogle Scholar
  48. The World Bank. (2008). Gini index. Available online at http://data.worldbank.org/indicator/SI.POV.GINI/countries?display=default. Accessed June 24, 2013.
  49. The World Bank. (2011). Countries and economies. Available online at http://data.worldbank.org/country. Accessed June 24, 2013.
  50. Tucker, L. R. (1951). A method for synthesis of factor analysis studies (Rep. No. Personnel Research Sections Report 984). Washington, D.C.: Department of the Army.Google Scholar
  51. Wilkinson, R. G., & Pickett, K. E. (2007). The problems of relative deprivation: Why some societies do better than others. Social Science and Medicine, 65, 1965–1978.CrossRefGoogle Scholar
  52. Wilkinson, R. G., & Pickett, K. E. (2009). The spirit level: Why more equal societies almost always do better. London: Penguin Books.Google Scholar
  53. Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Doctoral dissertation, University of California, Los Angeles.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Francisco Félix Caballero
    • 1
    • 2
    • 3
  • Marta Miret
    • 1
    • 2
    • 3
  • Beatriz Olaya
    • 4
  • Jaime Perales
    • 4
  • Ruy López-Ridaura
    • 5
  • Josep Maria Haro
    • 2
    • 4
  • Somnath Chatterji
    • 6
  • José Luis Ayuso-Mateos
    • 1
    • 2
    • 3
    Email author
  1. 1.Department of PsychiatryUniversidad Autónoma de MadridMadridSpain
  2. 2.Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III, CIBERSAM, Spain
  3. 3.Department of Psychiatry, Instituto de Investigación Sanitaria Princesa (IP)Hospital Universitario de La PrincesaMadridSpain
  4. 4.Parc Sanitari Sant Joan de DéuUniversitat de BarcelonaSant Boi de LlobregatSpain
  5. 5.Center of Research in Population HealthNational Institute of Public HealthCuernavacaMexico
  6. 6.Department of Health Statistics and Information SystemsWorld Health OrganizationGenevaSwitzerland

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