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Assessment of Composite Indicators Using the ANOVA Model Combined with Multivariate Methods

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  • Cristina Davino
  • Rosaria Romano

Abstract

The synthesis of a number of single observed indicators into a unique composite indicator involves various subjective choices related, for instance, to the type of combination (linear, non-linear) and to the aggregation method (simple average, geometric average) used in its construction. Thus, it is clearly important to analyse the variability of a composite indicator according to all possible alternatives before employing it in any decision-making process. Within such a framework, in this paper, we present a new approach based on a combination of explorative and confirmative analyses aiming to investigate the impact of different subjective choices on the variability of composite indicators. This new approach also allows the analysis of the related individual differences among the statistical units and the use of external information on the same units to enhance the interpretation of the final results. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Cristina Davino & Rosaria Romano, 2014. "Assessment of Composite Indicators Using the ANOVA Model Combined with Multivariate Methods," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 119(2), pages 627-646, November.
    • Handle: RePEc:spr:soinre:v:119:y:2014:i:2:p:627-646
    DOI: 10.1007/s11205-013-0532-3
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    References listed on IDEAS

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    1. Michela Nardo & Michaela Saisana & Andrea Saltelli & Stefano Tarantola & Anders Hoffman & Enrico Giovannini, 2005. "Handbook on Constructing Composite Indicators: Methodology and User Guide," OECD Statistics Working Papers 2005/3, OECD Publishing.
    2. Cristina Davino, Rosaria Romano, 2011. "Sensitivity Analysis of Composite Indicators through Mixed Model Anova," Working Papers 32-2011, Macerata University, Department of Studies on Economic Development (DiSSE), revised Mar 2011.
    3. Laurens Cherchye & Willem Moesen & Nicky Rogge & Tom Puyenbroeck, 2007. "An Introduction to ‘Benefit of the Doubt’ Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 82(1), pages 111-145, May.
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    Cited by:

    1. Kris Boudt & Valentin Todorov & Wenjing Wang, 2020. "Robust Distribution-Based Winsorization in Composite Indicators Construction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(2), pages 375-397, June.
    2. Cristina Davino & Rosaria Romano & Domenico Vistocco, 2020. "On the use of quantile regression to deal with heterogeneity: the case of multi-block data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 771-784, December.
    3. Diego Roldán M & Patricio Cárdenas & Francisco Roldán A. & Pablo Beltrán & Doménica Ávila, 2017. "Comparing Functional Forms Of Aggregation For Synthetic Indicators Of Well-Being: The Ecuadorian Case," Journal of Smart Economic Growth, , vol. 2(3), pages 107-122, December.

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