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Detecting Unobserved Heterogeneity in the Relationship Between Subjective Well-Being and Satisfaction in Various Domains of Life Using the REBUS-PLS Path Modelling Approach: A Case Study

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  • Luca Zanin
Abstract
In this article, we propose a model to estimate the direct and indirect effects of the relationship between subjective well-being and satisfaction in various domains of life using a partial least squares path modelling approach in a structural equation model framework. A drawback of these models is that they assume homogeneous behaviour over the observed set of units. To address this issue, Trinchera (Ph.D. thesis, University of Naples, 2007 ) and Esposito Vinzi et al. (Appl Stoch Models Bus Ind 28:439–458, 2008 ) proposed an algorithm, called the response-based unit segmentation in partial least squares (REBUS-PLS) path modelling, to detect sources of heterogeneity in both measurement and structural models. The REBUS-PLS allows researchers to identify classes of units with similar behaviours (with respect to the postulated model) and to estimate one model for each identified class (so-called ‘local models’). Applying the REBUS-PLS algorithm to our case study, we detected three main classes of units with similar behaviours and estimated three local models. We found, for example, that in the estimated model for the entire sample, the relationship between satisfaction with family and social life and subjective well-being is statistically significant. However, this result was not confirmed in all of the estimated local models. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Luca Zanin, 2013. "Detecting Unobserved Heterogeneity in the Relationship Between Subjective Well-Being and Satisfaction in Various Domains of Life Using the REBUS-PLS Path Modelling Approach: A Case Study," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 110(1), pages 281-304, January.
    • Handle: RePEc:spr:soinre:v:110:y:2013:i:1:p:281-304
    DOI: 10.1007/s11205-011-9931-5
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    2. Chonu, Gi Kunchana, 2013. "Comparison between Maximum Likelihood and Bayesian Estimation in Structural Equation Modelling and Effects of Informative Priors," Thesis Commons xef3g, Center for Open Science.
    3. O’Leary, Nigel & Li, Ian W. & Gupta, Prashant & Blackaby, David, 2020. "Wellbeing trajectories around life events in Australia," Economic Modelling, Elsevier, vol. 93(C), pages 499-509.
    4. Chiara Mussida & Luca Zanin, 2020. "Determinants of the Choice of Job Search Channels by the Unemployed Using a Multivariate Probit Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 369-420, November.
    5. Luca Zanin, 2017. "Education and Life Satisfaction in Relation to the Probability of Social Trust: a Conceptual Framework and Empirical Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 132(2), pages 925-947, June.
    6. Luca Zanin, 2016. "On Italian Households’ Economic Inadequacy Using Quali-Quantitative Measures," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 128(1), pages 59-88, August.
    7. Xiwen Fu, 2018. "The Contextual Effects of Political Trust on Happiness: Evidence from China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 139(2), pages 491-516, September.
    8. Lin, Yi-Chen & Hwang, Ruey-Ching & Deng, Wen-Shuenn, 2015. "Heterogeneity in the relationship between subjective well-being and its determinants over the life cycle: A varying-coefficient ordered probit approach," Economic Modelling, Elsevier, vol. 49(C), pages 372-386.

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    More about this item

    Keywords

    PLS path modelling; REBUS-PLS; Satisfaction in various domains of life; Subjective well-being; Unobserved heterogeneity; C20; C30; C80; D01;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles

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