Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification

2026-02-16 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Advanced, extended

Summary

A new topic-modeling framework has been developed to simplify psychological scales by analyzing the semantic structure of questionnaire items, bypassing the need for large respondent samples. This response-free approach encodes items using contextual sentence embeddings, groups them via density-based clustering to identify latent semantic factors, and then selects representative items based on structure-aware membership criteria. The framework was benchmarked across three widely used instruments: DASS, IPIP, and EPOCH, demonstrating an average item count reduction of 60.5% while preserving psychometric adequacy, including structural recovery, internal consistency, and factor congruence. The results indicate that semantic latent organization provides a robust, response-free approximation of measurement structure, positioning this method as an efficient front-end for scale construction and reduction. An integrated, visualization-supported tool is provided to facilitate adoption by researchers.

Key takeaway

For AI scientists and psychometricians involved in scale development or adaptation, this semantic topic-modeling framework offers a transparent, response-free method to efficiently simplify psychological questionnaires. You should consider integrating this approach as a front-end to generate initial short-form candidates, reducing reliance on extensive response data and streamlining the early stages of scale refinement before traditional psychometric validation.

Key insights

Semantic analysis of questionnaire items can efficiently simplify psychological scales without requiring respondent data.

Principles

• Semantic structure encodes latent construct organization.
• Density-based clustering infers latent factors without predefinition.
• Representative items maintain psychometric adequacy.

Method

The framework involves encoding items into contextual embeddings, reducing dimensionality, density-based clustering, class-based term weighting for topic identification, and selecting representative items based on membership probability.

In practice

• Reduce participant burden in scale administration.
• Generate initial structural hypotheses for new scales.
• Adapt existing scales for cross-cultural contexts.

Topics

• Psychological Scale Simplification
• Topic Modeling
• Natural Language Processing
• Sentence Embeddings
• Density-Based Clustering

Code references

• bowang-rw-02/sem-scale

Best for: AI Scientist, AI Researcher, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.