Abstract
Nowadays, a relevant challenge regards the assessment of a global measure of well-being by using composite indicators of different features such as level of wealth, comfort, material goods, standard living, quality and availability of education, etc.
In this paper, we focus on statistical methodologies designed to build composite indicators of well-being by detecting latent components and assessing the statistical relationships among indicators. We will consider some constrained versions of Principal Component Analysis (PCA) which allow to specify disjoint classes of variables with an associated component of maximal variance. Once the latent components are detected, a Structural Equation Model (SEM) has been used to evaluate their relationships. These methodologies will be compared by using a data set from 34 member countries of the Organization for Economic Co-operation and Development (OECD).
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References
Saisana, M., Tarantola, S.: State-of-the-art Report on Current Methodologies and Practices for Composite Indicator Development. EUR 20408 EN, European Commission-JRC (2002)
Kline, R.B.: Principles and Practice of Structural Equation Modeling. The Guilford Press, New York (2011)
Jöreskog, K.: A general approach to confirmatory maximum likelihood factor analysis. Psychometrika 34, 183–202 (1969)
Thurstone, L.L.: Multiple-Factor Analysis. University of Chicago Press, Chicago (1947)
Reiersol, O.: On the identifiability of parameters in Thurstone’s multiple factor analysis. Psychometrika 15, 121–149 (1950)
Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. J. Comput. Graph. Stat. 15(2), 265–286 (2006)
Tenenhaus, M., Esposito Vinzi, V.: PLS regression, PLS path modeling and generalized procrustean analysis: a combined approach for PLS regression, PLS path modeling and generalized multiblock analysis. J. Chemom. 19, 145–153 (2005)
Tenenhaus, A., Tenenhaus, M.: Regularized generalized canonical correlation analysis. Psychometrika 76(2), 257–284 (2011)
Vichi, M., Saporta, G.: Clustering and Disjoint Principal Component. Comput. Stat. Data Anal. 53(8), 3194–3208 (2009)
Bollen, K.A.: Structural Equations with Latent Variables. Wiley, New York (1989)
Kaplan, D.: Structural Equation Modeling: Foundations and Extensions. Thousands Oaks, Sage (2000)
OECD: How’s Life?: Measuring Well-Being. OECD Publishing (2011)
Hall, J., Giovannini, E., Morrone, A., Ranuzzi, G.: A Framework to Measure the Progress of Societies. OECD Statistics Working Papers, No. 2010/05, OECD, Paris (2010)
Esposito, V.V., Russolillo, G.: Partial least squares algorithms and methods. Wiley Interdiscip. Rev. Comput. Stat. 5, 1–9 (2013)
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Ferrara, C., Martella, F., Vichi, M. (2016). Dimensions of Well-Being and Their Statistical Measurements. In: Alleva, G., Giommi, A. (eds) Topics in Theoretical and Applied Statistics. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-27274-0_8
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DOI: https://doi.org/10.1007/978-3-319-27274-0_8
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