ArticleJournal of Intelligence2025
An Embedding-Based Semantic Analysis Approach: A Preliminary Study on Redundancy Detection in Psychological Concepts Operationalized by Scales.
Article in Journal of Intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.Behavior research methods · 2026Article
- Observer-Independent Assessment of Content Overlap in Mental Health Questionnaires: Large Language Model-Based Study.JMIR AI · 2025Article
- Statistics is not measurement: The inbuilt semantics of psychometric scales and language-based models obscures crucial epistemic differences.Frontiers in psychology · 2025Article
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Authors and funding
4 authors.
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Abstract
As psychological research progresses, the issue of concept overlap becomes increasing evident, adding to participant burden and complicating data interpretation. This study introduces an Embedding-based Semantic Analysis Approach (ESAA) for detecting redundancy in psychological concepts, which are operationalized through their respective scales, using natural language processing techniques. The ESAA utilizes OpenAI's text-embedding-3-large model to generate high-dimensional semantic vectors (i.e., embeddings) of scale items and applies hierarchical clustering to group semantically similar items, revealing potential redundancy. Three preliminary experiments evaluated the ESAA's ability to (1) identify semantically similar items, (2) differentiate semantically distinct items, and (3) uncover overlap between scales of concepts known for redundancy issues. Additionally, comparative analyses assessed the ESAA's robustness and incremental validity against the advanced chatbots based on GPT-4. The results demonstrated that the ESAA consistently produced stable outcomes and outperformed all evaluated chatbots. As an objective approach for analyzing relationships between concepts operationalized as scales, the ESAA holds promise for advancing research on theory refinement and scale optimization.
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