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Knowledge4Policy
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Competence Centre on Composite Indicators and Scoreboards

Our expertise on statistical methodologies and in developing sound composite indicators provides policy-makers with the ‘big picture’ for informed policy decisions and progress monitoring.

  • Navigation page | 27 Mar 2025

10 Step Guide

This short guide stresses the importance of conducting an internal coherence assessment prior to the uncertainty and sensitivity analysis, so as to further refine and eventually correct the composite indicator structure. Expert opinion is needed in this phase in order to assess the results of the statistical analysis. Second, it stresses that there is a trade-off between multidimensionality and robustness in a composite indicator. One could have a very robust yet mono-dimensional index or a very volatile yet multi-dimensional one. This does not imply that the first index is better than the second one. In fact, this table suggests treating robustness analysis NOT as an attribute of a composite indicator but of the inference which the composite indicator has been called upon to support. Third, it highlights the iterative nature of the ten steps, which although presented consecutively in the Handbook, the benefit to the developer is in the iterative nature of the steps.

Step 1: Theoretical framework

The theoretical framework provides the basis for the selection and combination of variables into a meaningful composite indicator which is fit for purpose.

Step 2. Data selection

The selection of data and indicators should be based on the analytical soundness, measurability, country coverage, and relevance of the indicators to the phenomenon being measured and their relationship to each other.

Step 3: Imputation of missing data

After assembling a set of indicators, missing data can be imputed, outliers treated and transformations can be applied to indicators where necessary and appropriate.

Step 4: Multivariate analysis

The Multivariate analysis can be used to study the overall structure of the dataset, assess its suitability, and guide subsequent methodological choices (e.g., weighting and aggregation).

Step 5: Normalisation

Normalisation brings indicators onto a common scale, which renders the variables comparable.

Step 6: Weighting

When indicators are aggregated into a composite measure, they can be assigned individual weights. This allows the effect or importance of each indicator to be adjusted according…

Step 7: Aggregating indicators

Aggregation combines the values of a set of indicators into a single summary ‘composite’ or ‘aggregate’ measure.

Step 8: Sensitivity analysis

Sensitivity analysis quantifies the uncertainty caused by each individual assumption, which identifies particularly sensitive assumptions which might merit closer consideration.

Step 9: Link to other measures

The scores of the composite indicator (or its dimensions) should be correlated with other existing composite indicators and other indicators/data and to identify linkages through regressions.

Step 10: Visualisation

Composite indicators are ultimately a communication tool, which can be greatly enhanced by proper visualisation, both static and interactive (online). Good visualisation helps…