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).