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Social Indicators Research

, Volume 142, Issue 2, pp 451–476 | Cite as

Use and Misuse of PCA for Measuring Well-Being

  • Matteo Mazziotta
  • Adriano ParetoEmail author
Article

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.

Keywords

Data reduction Composite indicator Measurement model Well-being 

Notes

Acknowledgements

The paper is the result of the common work of the authors: in particular M. Mazziotta has written Sects. 2.1, 3.2, 4 and A. Pareto has written Sects. 1, 2.2, 2.3, 2.4, 3.1.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Italian National Institute of StatisticsRomeItaly
  2. 2.Italian National Institute of StatisticsRomeItaly

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