通过个性化感知尺度分析问卷数据，揭示人类响应的深层模式。
Modeling Human Responses by Ordinal Archetypal Analysis
- 直接处理有序数据，避免转为连续量表的偏差
- 在欧洲社会调查数据上验证，提升对跨国家行为的理解
- 适合研究跨文化问卷差异与个体认知偏见的学者
本文提出一种针对有序数据（如问卷）的新型弧形分析框架——序数弧形分析（OAA），突破传统方法需先将有序数据转换为连续尺度的两步流程，直接建模原始有序数据。进一步引入响应偏差序数弧形分析（RBOAA），在优化过程中为每位受试者学习个性化的感知尺度，以反映个体对量表理解的差异。该方法在合成数据和欧洲社会调查（European Social Survey）数据集上得到验证，结果表明其能更准确捕捉人类行为与感知的内在结构，尤其在跨国研究中有效缓解因响应偏差带来的误解，为有序数据提供一种具理论基础的分析路径。
原文摘要 · Abstract (English)
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.
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