Measurement invariance of the Satisfaction with Life Scale: reviewing three decades of research
- 758 Downloads
The Satisfaction with Life Scale (SWLS) is a widely used measure of life satisfaction, a key aspect in quality of life. The SWLS has been used across many socio-demographic groups. Comparison of life satisfaction across different subgroups (e.g., cultures) is meaningful to researchers; such cross-group comparison presupposes that validity of the inferences from SWLS scores holds across various subgroups (measurement invariance: MI). The aim of the present review was to identify, summarize, and evaluate research testing measurement invariance of the SWLS.
A targeted literature search identified articles (published 1985–2016) that examined MI of the SWLS using multi-group confirmatory factor analysis.
The search retrieved 27 articles, representing 66,380 respondents across 24 nations. Gender, age, and culture were the most common types of MI assessed. Most articles used translated (non-English) versions of the SWLS. The highest level of MI tested in each article (i.e., configural, metric, scalar, strict) varied. Findings generally supported a unidimensional structure (configural MI), but less commonly supported were equivalent factor loadings (metric MI). Over half of the gender invariance analyses supported scalar or strict MI, whereas scalar or strict MI was supported in only 1 of the 11 culture MI analyses and 1 of the 9 age MI analyses.
Findings suggest meaningful comparisons of SWLS means across gender may be valid in some situations, but most likely not across culture or age groups. Participants mostly ascribe similar meaning to like items on the SWLS regardless of their gender, but age and especially culture seem to influence this process.
KeywordsSatisfaction with Life Scale Measurement invariance Multi-group confirmatory factor analysis Gender Culture Age
This research was supported by a Tri-Council Canada Graduate Scholarship awarded to the first author. The last author received research funding from the Lawson Foundation, Canada.
Compliance with ethical standards
Conflict of interest
The authors declare no conflicts of interest.
No human participants were involved in the article as it is a review of previously published research.
- 1.O’Donnell, G. (2013). Using well-being as a guide to policy. World Happiness Report, 2013, 98–111.Google Scholar
- 6.Cummins, R. A. (2013). Measuring happiness and subjective well-being. In S. A. David, I. Boniwell, A. Conley Ayers (Eds.) Oxford handbook of happiness, (pp. 185–200). Oxford: Oxford University Press.Google Scholar
- 11.Miller, M. J., & Sheu, H. (2008). Conceptual and measurement issues in multicultural psychology research. Handbook of Counseling Psychology, 4, 103–120.Google Scholar
- 16.Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago: Scientific Software International.Google Scholar
- 20.Gustavsson, J. P., Eriksson, A. K., Hilding, A., Gunnarsson, M., & Östensson, C. G. (2008). Measurement invariance of personality traits from a five-factor model perspective: multi-group confirmatory factor analyses of the HP5 inventory. Scandinavian Journal of Psychology, 49(5), 459–467.CrossRefPubMedGoogle Scholar
- 21.Sass, D. (2011). Testing measurement invariance and comparing latent factor means within a confirmatory factor analysis framework. Journal of Psychoeducational Assessment, 29(4), 347–363.Google Scholar
- 23.McArdle, J. (1998). Contemporary statistical models for examining test bias. In J. J. McArdle & R. W. Woodcock (Eds.), Human abilities in theory and practice. (pp. 157–195). Mahwah, NJ: Erlbaum.Google Scholar
- 24.Millsap, R. E., & Meredith, W. (2007). Factorial invariance: Historical perspectives and new problems. In Em R. Cudeck & R. C. MacCallum (Eds.) Factor analysis at 100: Historical developments and future directions, (pp. 131–152). Mahwah, NJ: Erlbaum.Google Scholar
- 25.Wu, A. D., Li, Z., & Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factor analysis: A demonstration with TIMSS data. Practical Assessment, Research and Evaluation, 12(3), 1–26.Google Scholar
- 26.DeShon, R. P. (2004). Measures are not invariant across groups without error variance homogeneity. Psychology Science, 46, 137–149.Google Scholar
- 27.Steiger, J. H., & Lind, J. C. (1980). Statistically based tests for the number of common factors. In annual meeting of the Psychometric Society, Iowa City, IA, (Vol. 758, pp. 424–453).Google Scholar
- 30.Hooper, D., Coughlan, J., & Mullen, M. Structural equation modelling: Guidelines for determining model fit. Journal of Business Research, 6(1), 53–60.Google Scholar
- 37.Chinni, M. L., & Hubley, A. M. (2014). A research synthesis of validation practices used to evaluate the satisfaction with life scale (SWLS). In Validity and validation in social, behavioral, and health sciences (pp. 35–66). New York: Springer.Google Scholar
- 68.Jöreskog, K., & Sörbom, D. (2006). LISREL 8.8 statistical program [Scientific Software]. Chicago, IL: Scientific Software International.Google Scholar
- 73.Van De Schoot, R., Schmidt, P., De Beuckelaer, A., Lek, K., & Zondervan-Zwijnenburg, M. (2015). Editorial: Measurement invariance. Frontier in Psychology, 6, 1064.Google Scholar
- 75.Zumbo, B. D. (2009). Validity as contextualized and pragmatic explanation, and its implications for validation practice. In R. W. Lissitz (Ed.) The concept of validity: Revisions, new directions and applications (pp. 65–82). Charlotte: IAP – Information Age Publishing.Google Scholar