# Resilience Analysis Grid-Rasch Rating Scale Model for Measuring Organizational Resilience Potential
## 2 Materials and Methods
### The RAG-Rasch Model  
The Rasch model offers significant advantages when applied to the RAG, as long as one substitutes the individual with the organization and the person ability with the level of resilience potential.  
The main benefits are as follows:  
* Concurrent measurement and sample independence: A major advantage of the Rasch model is its property of specific objectivity, which allows for the estimation of item difficulty and the organization resilience potential to be, in principle, independent of the specific samples used. As a consequence, item difficulty estimates are invariant across different samples of organizations, and, similarly, an organization ability estimates do not depend on the specific item used. _This allows for the assessment and comparison of several organizations' resilience potentials at once_.
* Interval-level measurement: The Rasch model allows for the construction of an interval measurement scale where differences between scores are interpretable and represent equal intervals in the latent trait (e.g., anticipating, responding, learning, monitoring). Therefore, the difference between two scores on the scale represents a constant difference in resilience potential, making it easier to interpret and compare results. _This means that different resilience potential levels can be compared across organizations on the same scale._
* Unidimensionality: The Rasch model assumes that all items measure a single latent trait (such a single resilience ability). This requirement ensures that the constructed scale is consistent and provides a unidimensional measure of the construct. _This requires that each pillar ability be considered as a separate construct and analyzed individually._
* Separability of estimates: The Rasch model allows the separate estimation of item and organization parameters. This separability property simplifies item calibration and allows for the addition or replacement of items without affecting the validity of previously obtained measures. _This property allows for a continuous improvement in the assessment tool by expanding, pruning, or refining the questionnaire/_
* Fit statistics for data quality: The Rasch model includes methods for checking the goodness of fit of the data to the model through fit statistics (such as infit and outfit). _These statistics help identify any atypical responses or items that do not fit the structure of the model, allowing the quality and accuracy of the test to be improved by removing or revising items that do not fit._
* Item bias detection: Because of its invariance property and rigorous formulation, the Rasch model is effective in identifying item bias, i.e., items that are systematically easier or harder for certain groups of organizations, regardless of their ability. _This standardizes the assessment and cross-cultural testing._
* Parsimony of the model: Unlike other item response theory (IRT) models, which may include several parameters (e.g., discrimination and guessing parameters in two- or three-parameter models), the Rasch model uses only one parameter to describe each item (i.e., difficulty). _This simplicity guarantees the fast and easy applicability of the model._