Sensitivity Analysis and Robust Approach in Multidimensional Scaling: An Evaluation of Customer Satisfaction

Multidimensional scaling (MDS) methods are devoted par excellence to representing a set of objects in a low-dimensional Euclidean space by preserving, as far as possible, the proximities between the objects themselves. Like other data analysis techniques, MDS methods treat a set of data as the entire population of interest. It is therefore important to determine the stability and robustness of the results against possible perturbations or errors present in the data. This issue is particularly important in customer satisfaction analysis, where satisfaction indicators and customer ranking can be heavily influenced by such anomalies. These kinds of problems are specifically addressed in this work. Starting from commonly applied methods, a combination of sensitivity and robust analysis will be proposed. A case study will then be considered.