Use and Misuse of PCA for Measuring Well-Being

Matteo Mazziotta () and Adriano Pareto ()
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Matteo Mazziotta: Italian National Institute of Statistics
Adriano Pareto: Italian National Institute of Statistics

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2019, vol. 142, issue 2, No 1, 476 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11205-018-1933-0

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