Technical Features of R-sw Drivers (SV)

What does the R-sw Drivers do?

  • it provides derived importance scores based on the R-square contribution averaged over orderings among independent variables (aggregate level analysis). In other words, this approach produces coefficients based on R^2 decomposition. This approach is often referred to as “Shapley value regression” or “dominance analysis”.


  • the derived importance analysis can be based on either a linear or a logistic model, depending on the scale available for the dependent variable;
  • independent variables should ideally be collected on a rating or ratio scale;
  • the dependent variable can be dichotomous (for logistic Shapley Value analysis) or on a rating / ratio scale (for linear Shapley Value analysis);
  • derived importance scores add up to 100; each output coefficient refers to a specific statement (independent variable);
  • derived importance scores can be presented along with their confidence intervals estimates obtained through bootstrapping;
  • data can be easily imported and outcomes can be exported as CSV/text files.

Support, Manual and Examples:

  • technical support and statistical consulting is available free of charge (within reasonable limits);
  • an ‘html’ manual is provided with a detailed description of all available functions;
  • full working examples are provided to help the User to become familiar with the package. These examples can be easily adapted by the User for new projects.

Note: this is a pure analysis tool, therefore data must be collected through an external source (such as a CAWI, CAPI or PAPI system).