Domain Importance in Subjective Well-Being Measures
- 334 Downloads
In subjective well-being (SWB) studies, domain importance typically refers to the relative importance of various life domains. Although there appears to be a consensus that domain importance is an important topic, whether or not domain importance should be incorporated into measures of SWB remains contentious. Even though recent studies that examined the claims against incorporating domain importance, also known as domain importance weighting, into SWB measures found that both conceptual and empirical arguments have been far from sufficient, insufficient evidence against importance weighting does not mean there is evidence to support importance weighting. Conducting a secondary analysis, the current study investigates the role of domain importance in SWB measures without making any arbitrary assumptions regarding how domain importance weighting should function. Results of the study show that the relationship between global life satisfaction and the sum of domain satisfaction scores did not remain constant across groups of different domain importance rating patterns. The findings suggest that, when the research objective is to study variability of responses at the level of homogeneous subgroups, it is important to consider domain importance when using domain satisfaction to construct global SWB measures.
KeywordsImportance weighting Domain weighting Latent class analysis Cluster analysis Relative domain importance
- Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Beverly Hills, CA: Sage.Google Scholar
- Campbell, A., Converse, P. E., & Rodgers, W. L. (1971). Quality of American Life, 1971 (Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 1992-02-16. doi:10.3886/ICPSR03508.v1).
- Campbell, A., Converse, P. E., & Rogers, W. L. (1976). The quality of American Life: Perceptions, evaluations, and satisfactions. New York: Russel Sage.Google Scholar
- Ferrans, C. E. (1990). Development of a quality of life index for patients with cancer. Oncology Nursing Forum, 17(3), 15–19.Google Scholar
- Inglehart, R. (1978). Value priorities life satisfaction, and political dissatisfaction among western publics. Comparative Studies in Sociology, 1, 173–202.Google Scholar
- Locke, E. A. (1976). The nature and causes of job satisfaction. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 1297–1349). Chicago: Rand McNally.Google Scholar
- Magidson, J., & Vermunt, J. (2002a). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 89–106). Cambridge: Cambridge University Press.Google Scholar
- Magidson, J., & Vermunt, J. K. (2002b). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20, 37–44.Google Scholar
- Magidson, J., & Vermunt, J. K. (2004). Latent class models. In D. Kaplan (Ed.), Sage handbook of quantitative methodology for social sciences (pp. 175–198). Thousand Oaks, CA: Sage.Google Scholar
- Muthén, B. O. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schuniaker (Eds.), New developments and techniques in structural equation modeling (pp. 1–33). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
- Snedecor, G. W., & Cochran, W. G. (1989). Statistical methods. Ames, IA: Iowa State University Press.Google Scholar
- Vermunt, J. K., & Magidson, J. (2013). Latent GOLD 5.0 upgrade manual. Belmont, MA: Statistical Innovations Inc.Google Scholar