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A multi-process second-order latent growth curve model for subjective well-being

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  • M. Salgueiro
  • Peter Smith
  • Marcel Vieira

Abstract

This article proposes a new approach to modelling longitudinal perceptions of subjective well-being (SWB). Several measures have been proposed in the literature to assess SWB and its determinants. Statistical approaches adopted include ordered probit models, fixed and random effects models and cross-lagged structural equation models. The British Household Panel Survey (BHPS) is a longitudinal national representative survey and contains several measures of SWB. Using BHPS data from 2002 to 2005, this article considers two main latent dimensions of life satisfaction: satisfaction with leisure and satisfaction with material issues. The latent trajectories of these two latent life satisfaction dimensions are simultaneously modeled in Mplus, using a multi-process, second-order latent growth curve model. Significant determinants of leisure and material satisfaction growth trajectories include socio-demographic characteristics, number of children in the household, number of hours worked per week, income and perceived health status. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • M. Salgueiro & Peter Smith & Marcel Vieira, 2013. "A multi-process second-order latent growth curve model for subjective well-being," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 735-752, February.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:2:p:735-752
    DOI: 10.1007/s11135-011-9541-y
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    References listed on IDEAS

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    4. Helliwell, John F., 2003. "How's life? Combining individual and national variables to explain subjective well-being," Economic Modelling, Elsevier, vol. 20(2), pages 331-360, March.
    5. Dolan, Paul & Peasgood, Tessa & White, Mathew, 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, Elsevier, vol. 29(1), pages 94-122, February.
    6. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
    7. Chih-Chien Yang & Chih-Chiang Yang & Kuang-Hui Yeh, 2005. "Ecological-Inference-Based Latent Growth Models: Modeling Changes of Alienation," Quality & Quantity: International Journal of Methodology, Springer, vol. 39(2), pages 125-135, April.
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    Cited by:

    1. Francesca Bassi, 2016. "Dynamic segmentation with growth mixture models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 263-279, June.
    2. Vieira Marcel de Toledo & Smith Peter W.F. & Salgueiro Maria de Fátima, 2016. "Misspecification Effects in the Analysis of Panel Data," Journal of Official Statistics, Sciendo, vol. 32(2), pages 487-505, June.

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