The Idecis

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    Already a Refinitiv customer? Find your product login. These scores allow the analysis of the way countries are distributed in terms of enabling and outcome conditions with respect to each other and with respect to the EU 28 average score. The construction of an index per enabling condition and outcome dimension was based on the analysis of data availability and coverage for each Member State.

    Keith Ainsworth - The Idecis, ISBN

    Many indicators across enabling conditions are available until , with a few exceptions mainly in the dimensions of e-leadership, entrepreneurial culture, and supply and demand of digital skills. Furthermore, the availability of data for outcome indicators varies mostly between and The construction of the indices followed rules on data imputation and transformation to overcome availability issues and ensure comparability across countries.

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    • In addition, following the application of the imputation procedure, two other steps were carried out in the construction of all rank indices one per each enabling and output dimension : a data transformation process to limit the effects of extreme or outlying values, and a min-max normalisation process to define the rank index within a common scale for each Member State. This first step highlighted the fact that several of the national indicators present limitations regarding data coverage.

      In order to overcome such limitations, a strategy of data imputation was adopted; this strategy allowed the construction of the set of indices based on the following rules:. Data transformation to limit the influence of extreme values and standardisation to ensure comparability The second step following analysis and imputation involved a process of data transformation to control for outlying values present in the different groups of indicators. A min-max normalisation procedure was selected and performed in order to allow country comparisons by means of a common range between 0 and 1.

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      The implementation of this procedure requires careful attention to the treatment of extreme or outlying values which can induce a distortion to the resulting rank index. Within this context, the effects of extreme or outlying values were addressed by transforming the dataset of indicators through the application of a Winsorisation method which limits and reduces the impact of outlying data OECD, Following this transformation, the normalisation process was engaged to ensure comparability of scales in the construction of the indices.