Archetypal cases for questionnaires with nominal multiple choice questions
This work addresses the need for better exploratory analysis of nominal data in questionnaires, though it is incremental as it extends an existing method to a new data type.
The paper tackles the problem of identifying extreme patterns in nominal questionnaire data by applying archetypoid analysis for the first time to nominal observations, demonstrating its benefits over existing methods using the German credit dataset.
Archetypal analysis serves as an exploratory tool that interprets a collection of observations as convex combinations of pure (extreme) patterns. When these patterns correspond to actual observations within the sample, they are termed archetypoids. For the first time, we propose applying archetypoid analysis to nominal observations, specifically for identifying archetypal cases from questionnaires featuring nominal multiple-choice questions with a single possible answer. This approach can enhance our understanding of a nominal data set, similar to its application in multivariate contexts. We compare this methodology with the use of archetype analysis and probabilistic archetypal analysis and demonstrate the benefits of this methodology using a real-world example: the German credit dataset.