Subjective–Objective Method of Maximizing the Average Variance Extracted From Sub-indicators in Composite Indicators

Matheus Pereira Libório (), Alexandre Magno Alvez Diniz, Douglas Alexandre Gomes Vieira and Petr Iakovlevitch Ekel
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Matheus Pereira Libório: Pontifical Catholic University of Minas Gerais
Alexandre Magno Alvez Diniz: Pontifical Catholic University of Minas Gerais
Douglas Alexandre Gomes Vieira: Federal Technological Education Centre of Minas Gerais
Petr Iakovlevitch Ekel: Pontifical Catholic University of Minas Gerais

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2024, vol. 175, issue 2, No 11, 613-637

Abstract: Abstract This research presents an innovative method for constructing composite indicators: the Subjective–objective method of maximizing extracted variance (Sommev). Sommev’s hybrid weighting approach fills an important gap within a highly controversial area of the composite indicators’ literature, which criticizes the statistical assignment of weights disconnected from theory and the errors and judgmental biases inherent in the expert opinion-based weighting approach. These innovations contribute to a more coherent and consistent operationalization of the theoretical framework of multidimensional phenomena, reconciling the non-compensability between sub-indicators and the maximum retention of original information through statistically defined weights, in which the expert’s opinion is considered, but does not determine the sub-indicator’s weights. Twenty simulations were carried out to analyze the application of the method in representing social exclusion in a Brazilian city. Composite indicators constructed by Sommev retain twice as much information as those constructed with equal weights or weights defined by experts. This increased informational capacity favors a more comprehensive representation of the multidimensional phenomenon, having a high potential for application in solving problems of a multidimensional nature in the social, economic, and environmental areas.

Keywords: Composite indicators; Non-compensatory aggregation; Hybrid weighting; Multidimensional phenomenon; Group decision (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11205-024-03385-w

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