2024/09/12 by Anna Emilie J. Wedenborg, Wedenborg, Anna Emilie J., Michael Alexander Harborg +15 · 1 citation
Engineering · Psychology · #Diverse Scientific and Engineering Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Paranormal Experiences and Beliefs #Urban Design and Spatial Analysis
paper · pdf · doi:10.48550/arxiv.2409.07934
openalex publication_date 2024/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper introduces a novel framework for Archetypal Analysis (AA) tailored to ordinal data, particularly from questionnaires. Unlike existing methods, the proposed method, Ordinal Archetypal Analysis (OAA), bypasses the two-step process of transforming ordinal data into continuous scales and operates directly on the ordinal data. We extend traditional AA methods to handle the subjective nature of questionnaire-based data, acknowledging individual differences in scale perception. We introduce the Response Bias Ordinal Archetypal Analysis (RBOAA), which learns individualized scales for each subject during optimization. The effectiveness of these methods is demonstrated on synthetic data and the European Social Survey dataset, highlighting their potential to provide deeper insights into human behavior and perception. The study underscores the importance of considering response bias in cross-national research and offers a principled approach to analyzing ordinal data through Archetypal Analysis.