Organizer: Iodice d’Enza
Chair: Iodice d’Enza
Giuseppe Gismondi, G., D’Ambrosio, A., Vera, G.F. Clustering in an augmented unfolding framework
| In the framework of preference rankings, the interest can lie in clustering individuals or items in order to reduce the complexity of the preference space for an easier interpretation of collected data. In this work we introduce a way to simultaneously produce non-metric unfolding and clustering of individuals keeping fixed the items in an alternating algorithm. The graphical output is an unfolding plot showing the cluster center of the individuals overlapped with the items. |
Alcacer, A., Epifanio, I. Archetypal cases for questionnaires with nominal multiple choice questions.
Abstract
| 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. |
Palazzo L., De Palma M., Palumbo, F. Profiling planned behavior insights by Multinomial Archetypal Analysis
Abstract
| This study investigates the psychological determinants of participation in oncological screening programs using a novel method, Probabilistic Archetypal Analysis (PAA). By applying the extended Theory of Planned Behavior (TPB), we explore the influence of attitudes, subjective norms, anticipated regret, and self-identity on individuals’ intentions to undergo screening. Using survey data from 435 participants, the analysis identifies four distinct archetypal profiles based on these constructs that offer insights into the motivational factors driving health-related behavior. |