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Estimating the Reliability of Single-Item Life Satisfaction Measures: Results from Four National Panel Studies

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Abstract

Life satisfaction is often assessed using single-item measures. However, estimating the reliability of these measures can be difficult because internal consistency coefficients cannot be calculated. Existing approaches use longitudinal data to isolate occasion-specific variance from variance that is either completely stable or variance that changes systematically over time. In these approaches, reliable occasion-specific variance is typically treated as measurement error, which would negatively bias reliability estimates. In the current studies, panel data and multivariate latent state-trait models are used to isolate reliable occasion-specific variance from random error and to estimate reliability for scores from single-item life satisfaction measures. Across four nationally representative panel studies with a combined sample size of over 68,000, reliability estimates increased by an average of 16% when the multivariate model was used instead of the more standard univariate longitudinal model.

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  1. Schimmack and Lucas (2010) also showed that stationarity assumptions could be tested when eight or more waves are available. Specifically, two different sets of constraints could be made for the first and second halves of the waves. When using this approach, they found that the reliability of the life satisfaction measure used in the GSOEP increased over time. We used this approach for the four datasets used in the current study, and reliability only increased appreciably in the GSOEP. Estimates in the unidimensional model increased from 0.58 to 0.65 in the first half of the waves to the second half in the GSOEP, 0.62 to 0.64 in the HILDA, 0.62 to 0.66 in the SHP, and they were constant at 0.63 in the BHPS.

References

  • Alwin, D. F. (2007). Margins of error: A study of reliability in survey measurement. New York: Wiley.

    Google Scholar 

  • Biemer, P. P., Christ, S. L., & Wiesen, C. A. (2009). A general approach for estimating scale score reliability from panel survey data. Psychological Methods, 14, 400–412. doi:10.1037/a0016618.

    Article  Google Scholar 

  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.

    Google Scholar 

  • Ehrhardt, J. J., Saris, W. E., & Veenhoven, R. (2000). Stability of life-satisfaction over time: Analysis of change in ranks in a national population. Journal of Happiness Studies, 1, 177–205. doi:10.1023/A:1010084410679.

    Article  Google Scholar 

  • Haisken-De New, J. P., & Frick, J. (2005). Desktop companion to the German Socio-Economic Panel Study (GSOEP). Berlin: DIW.

    Google Scholar 

  • Kenny, D. A., & Zautra, A. (1995). The trait-state-error model for multi-wave data. Journal of Consulting and Clinical Psychology, 63, 52–59. doi:10.1037/0022-006X.63.1.52.

    Article  Google Scholar 

  • Kenny, D. A., & Zautra, A. (2001). The trait-state models for longitudinal data. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 243–263). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria: What did they really say? Organizational Research Methods, 9, 202–220. doi:10.1177/1094428105284919.

    Article  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (1998–2009). Mplus user’s guide (5th Ed.). Los Angeles, CA: Muthén & Muthén.

  • Schimmack, U., & Lucas, R. E. (2010). Environmental influences on well-being: A dyadic latent panel analysis of spousal similarity. Social Indicators Research, 98, 1–21.

    Article  Google Scholar 

  • Schimmack, U., Wagner, G. G., Krause, P., & Schupp, J. (2010). Stability and change of well being: An experimentally enhanced latent state-trait-error analysis. Social Indicators Research, 95, 19–31.

    Article  Google Scholar 

  • Taylor, M. F., Brice, J., Buck, N., & Prentice-Lane, E. (2004). British Household Panel Survey user manual volume A: Introduction, technical report, and appendices. Colchester: University of Essex. Available at: http://iserwww.essex.ac.uk/ulsc/bhps/doc/.

  • Thompson, B. (Ed.). (2003). Score reliability: Contemporary thinking on reliability issues. Thousand Oaks, CA: Sage.

    Google Scholar 

  • University of Essex. Institute for Social and Economic Research. (2004). British Household Panel Survey; waves 112, 19912003 [computer file]. Colchester, Essex: UK Data Archive [distributor].

  • Watson, N. (2010). HILDA user manualrelease 8. Melbourne Institute of Applied Economic and Social Research, University of Melbourne.

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Acknowledgments

The BHPS data were made available through the ESRC Data Archive. The data were originally collected by the ESRC Research Centre on Micro-social Change at the University of Essex (now incorporated within the Institute for Social and Economic Research). Neither the original collectors of the data nor the Archive bear any responsibility for the analyses or interpretations presented here. This paper also uses confidentialised unit record file from the Household, Income and Labour Dynamics in Australia (HILDA) survey. The HILDA Project was initiated and is funded by the Commonwealth Department of Families, Community Services and Indigenous Affairs (FaCSIA) and is managed by the Melbourne Institute of Applied Economic and Social Research (MIAESR). The findings and views reported in this paper, however, are those of the author and should not be attributed to either FaCSIA or the MIAESR. The GSOEP data were made available by the German Socio-Economic Panel Study at the German Institute for Economic Research (DIW), Berlin. Finally, the study uses data collected in the “Living in Switzerland” project, conducted by the Swiss Household Panel (SHP), which is based at the Swiss Centre of Expertise in the Social Sciences FORS, University of Lausanne. The SHP project is financed by the Swiss National Science Foundation. This research was supported by National Institute on Aging grant R03AG032001.

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Correspondence to Richard E. Lucas.

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Lucas, R.E., Brent Donnellan, M. Estimating the Reliability of Single-Item Life Satisfaction Measures: Results from Four National Panel Studies. Soc Indic Res 105, 323–331 (2012). https://doi.org/10.1007/s11205-011-9783-z

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