As is frequent with data projects, some of the variables included in the Recoupling Dashboard have a degree of missing data. To ensure continuity and comparability between composite scores over time, it is necessary to estimate values for these years. Missing data can be located in the interior of the available time series or at the exterior. For the former, the linear interpolation method is used – values are replaced with numbers incrementally higher or lower than the neighboring data points. For the latter, the missing values are replaced using the closest data point from source (last value carried forward – LVCF – or first value carried backward – FVCB). 

Data collected to compute the indexes are diverse. At source, the variables collected are produced on different scales, and can also have different polarities – higher is better or higher is worse. In order for them to be meaningfully combined and compared, raw data are standardized before being included in the indexes. We employ a min-max normalization whereby all raw data are transformed to a scale of 0.0-1.0 (where a score of 1.0 is the best score a country can achieve). While this constitutes an order-preserving linear transformation of the data, a score of 1.0 after normalization does not imply that a country’s score in raw data terms is perfect, but rather is the best score in the set of countries.

The authors of the Recoupling Dashboard use linear, additive aggregation and weigh each sub-component equally within its dimension.

Because the EPI is released biennially in even-numbered years, the reported EPI score in the baseline year (2007) is taken from the EPI report 2008. The score for 2018 is replaced with the EPI score from 2016. Because innovations in the 2018 EPI data and methodology make comparison of scores with the EPI score of 2008 difficult.