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Sensitivity Analysis of Composite Indicators through Mixed Model Anova

Author

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  • Cristina Davino, Rosaria Romano

    (University of Macerata)

Abstract

The paper proposes a new approach for analysing the stability of Composite Indicators. Starting from the consideration that different subjective choices occur in their construction, the paper emphasizes the importance of investigating the possible alternatives in order to have a clear and objective picture of the phenomenon under investigation. Methods dealing with Composite Indicator stability are known in literature as Sensitivity Analysis. In such a framework, the paper presents a new approach based on a combination of explorative and confirmative analysis aiming to investigate the impact of the different subjective choices on the Composite Indicator variability and the related individual differences among the statistical units as well.

Suggested Citation

  • 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.
    • Handle: RePEc:mcr:wpaper:wpaper00032
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    File URL: http://www.unimc.it/sviluppoeconomico/wpaper/wpaper00032/filePaper
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    Cited by:

    1. 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.
    2. Rosalia Castellano & Antonella Rocca, 2015. "Assessing the gender gap in labour market index: volatility of results and reliability," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 42(8), pages 749-772, August.

    More about this item

    Keywords

    sensitivity analysis; composite indicators; analysis of variance; principal component analysis;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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