Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators

By Michaela Saisana, Andrea Saltelli, Stefano Tarantola. 2005

852 citations recorded by the source corpus.

Subfields: Physical and Theoretical Chemistry, Sociology and Political Science, Urban Studies

View Paper: https://www.semanticscholar.org/paper/7bad3bc442c77eea8520d78c600cc29d50a220fa

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Citations in the corpus

An Introduction to ‘Benefit of the Doubt’ Composite Indicators (2007, background): “One interesting idea, that we have not pursued in this paper, is checking the robustness of the country rankings (or scores) to this different scenario’s by uncertainty/sensitivity analysis (see e.g. Nardo et al. (2005), or Saisana et al. (2005)).”
Tools for Composite Indicators Building (2005, background): “In Saisana et al. (2005) one reads: “[…] it is hard to imagine that debate on the use of composite indicators will ever be settled […] official statisticians may tend to resent composite indicators, whereby a lot of work in data collection and editing is “wasted” or “hidden” behind a single number…”
Climate vulnerability mapping: A systematic review and future prospects (2019, background): “In such situations, demonstrating that indices are robust to both data inputs and outcomes of interest, including the characterization of their uncertainties and limitations, is important (Hinkel, 2011; Saisana et al., 2005; Tate, 2012; Weeks, Walker, Overton, & Clarkson, 2013).”
Economic crisis and regional resilience: detecting the‘geographical footprint’ of economic crisis in Greece (2014, background): “Composite indicators, however, do stir controversy, since their use presents advantages and disadvantages (Saisana and Tarantola 2002; Nardo et al. 2005; Saisana et al. 2005).”
NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set (2014, background): “…investigating other possible solutions for the ﬁnal choice of the best number of clusters, such as the building of a composite index and the consideration of sensibility analysis, see Saisana, Saltelli, and Tarantola (2005) and Marozzi (2014), by combining well-chosen indices into a single index.”
Sensitivity analysis practices: Strategies for model-based inference (2006, methodology): “(I) changes in the selection of the underlying indicators, (II) error in the underling indicators, (III) changes in the scaling method, (IV) changes in the aggregation weights [24].”
A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis (2014, methodology): “(5) Assess the robustness of weights in both MCDM techniques in light of GSA results.”
Composite Indices: Rank Robustness, Statistical Association, and Redundancy (2013, background): “See, for example, Blackorby, Donaldson, and Weymark (1984).”
Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices (2018, background): “8 Influence Analysis is a particular case of Uncertainty Analysis (Saisana et al. 2005) that aims to empirically quantify the ‘weight’ of each individual indicator in the calculation of the composite index.”
Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index (2008, neutral): “Saisana et al (2005) studied the uncertainties in the Technology Achievement Index, focusing on the type of normalization for the sub-indicators, the way of eliciting information from the experts on the weight issue and the sub-indicators’ weights.”