Huang Zhen, Long Yitian, Peng Kaiping, Tong Song
School of Social Sciences, Tsinghua University, Beijing 100084, China.
Wuhan Britain-China School, Wuhan 430000, China.
J Intell. 2025 Jan 16;13(1):11. doi: 10.3390/jintelligence13010011.
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.
随着心理学研究的进展，概念重叠问题日益明显，这增加了参与者的负担并使数据解释变得复杂。本研究引入了一种基于嵌入的语义分析方法（ESAA），用于检测心理概念中的冗余，这些概念通过各自的量表进行操作化，使用自然语言处理技术。ESAA利用OpenAI的text-embedding-3-large模型生成量表项目的高维语义向量（即嵌入），并应用层次聚类对语义相似的项目进行分组，揭示潜在的冗余。三个初步实验评估了ESAA在以下方面的能力：（1）识别语义相似的项目，（2）区分语义不同的项目，以及（3）发现已知存在冗余问题的概念量表之间的重叠。此外，比较分析评估了ESAA相对于基于GPT-4的先进聊天机器人的稳健性和增量效度。结果表明，ESAA始终产生稳定的结果，并且优于所有评估的聊天机器人。作为一种分析作为量表操作化的概念之间关系的客观方法，ESAA有望推动理论完善和量表优化的研究。
