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[2602.12575] Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification
Summary
This article presents a novel framework for simplifying psychological scales by discovering semantic latent structures without relying on traditional response-based methods. It leverages natural language processing to enhance scale efficiency while maintaining psychometric int...
Why It Matters
This research is significant as it addresses the limitations of conventional methods in psychological scale refinement, which often require large datasets and can be culturally biased. By introducing a response-free approach, it opens new avenues for more accessible and efficient psychological assessments, potentially benefiting diverse populations.
Key Takeaways
- Introduces a response-free framework for psychological scale simplification.
- Utilizes natural language processing to identify latent semantic structures.
- Achieves an average scale length reduction of 60.5% while preserving psychometric properties.
- Demonstrates high concordance with established factor structures.
- Provides a user-friendly tool for semantic analysis and scale reduction.
Computer Science > Computation and Language arXiv:2602.12575 (cs) [Submitted on 13 Feb 2026] Title:Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification Authors:Bo Wang, Yuxuan Zhang, Yueqin Hu, Hanchao Hou, Kaiping Peng, Shiguang Ni View a PDF of the paper titled Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification, by Bo Wang and 5 other authors View PDF HTML (experimental) Abstract:Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data availability and cross-cultural comparability. Recent advances in natural language processing suggest that the semantic structure of questionnaire items may encode latent construct organization, offering a complementary response-free perspective. We introduce a topic-modeling framework that operationalizes semantic latent structure for scale simplification. Items are encoded using contextual sentence embeddings and grouped via density-based clustering to discover latent semantic factors without predefining their number. Class-based term weighting derives interpretable topic representations that approximate constructs and enable merging of semantically adjacent clusters. Representative items are selected u...