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Statistical quality management pillar

Reliable indicators on poverty, social exclusion, and related areas generally are based on highly sophisticated statistical estimation methods. These figures need to be highly accurate at different aggregation levels for geographical areas and have to grasp the multidimensionality of poverty and vulnerability.



Acknowledging the multidimensionality and complex nature of poverty determine the need for the availability of statistical data and the adequate tools for analysing it. Regarding the methodology utilised in a multivariate setting, there are essentially two relevant types of approaches.

The first consists of theoretical constructions that are based on opportune and consistent logic models of reference, such as the approach that derives from the mathematical theory of fuzzy sets. This technique has been effectively developed also at the dynamic level, allowing the measurement of both persistent and transitory poverty.

The second approach refers to multivariate statistical techniques (discriminant analysis, factor, cluster, multiple correspondences, etc.) and aims to aggregate the disperse information contained in a multiplicity of poverty indicators so as to analyse it within a space of reduced dimension.

Both types of approaches have a common goal: to derive meaningful indicators and measures from the basic statistical information. the definition of multidimensional poverty and living conditions indicators has also been one objective of the FP7 project SAMPLE.


Other important research subjects are 

  • Small area estimations
  • Measuring change



Research activities


For the statistical quality research the objectives are:

  • to improve share and consolidate knowledge on theories and best practices to judge the quality and appropriateness of indicators through an empirical analysis;
  • to provide appropriate definitions of the indicators at multidimensional level;
  • to define in a robust way their values to obtain statistically sound estimates for unplanned do-mains (typically small areas);
  • to contribute to a correct usage and formulation of survey weights with attention to the impact of the weighting system and survey designs both on the definition of indicators and on the fitting of models for the analysis;
  • to formulate and estimate measures of accuracy of the estimates, such as mean squared errors and non-sampling errors due to non-response, imputation of missing data and measurement errors arising from disclosure limitation methods;
  • to implement case studies in order to demonstrate the methodology as well as strengths and weaknesses;
  • to use the result of the scenario analysis and simulation studies to develop best practice recommendations and to point out needs of further research.



Partners involved








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  • Last modified 29-11-2016