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Use and Misuse of PCA for Measuring Well-Being

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  • Matteo Mazziotta

    (Italian National Institute of Statistics)

  • Adriano Pareto

    (Italian National Institute of Statistics)

Abstract

The measurement of well-being of people is very difficult because it is characterized by a multiplicity of aspects or dimensions. Principal Components Analysis (PCA) is probably the most popular multivariate statistical technique for reducing data with many dimensions and, often, well-being indicators are reduced to a single index of well-being by using PCA. However, PCA is implicitly based on a reflective measurement model that is not suitable for all types of indicators. In this paper, we discuss the use and misuse of PCA for measuring well-being, and we show some applications to real data.

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  • Matteo Mazziotta & Adriano Pareto, 2019. "Use and Misuse of PCA for Measuring Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 451-476, April.
  • Handle: RePEc:spr:soinre:v:142:y:2019:i:2:d:10.1007_s11205-018-1933-0
    DOI: 10.1007/s11205-018-1933-0
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