RESEARCH METHOD

Exploring the Structure of the CPAI-2 Interpersonal Relatedness Factor Using Large Language Models: A Pilot Study

  • LI Wenbing ,
  • ZHU Tianjiao ,
  • GAN Yiqun
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  • School of Psychological and Cognitive Sciences, Peking University, Beijing 100080, China
*GAN Yiqun, ygan@pku.edu.cn

Online published: 2026-10-23

Abstract

Objective: This study aimed to explore the structural model of the Interpersonal Relatedness factor in the Cross-Cultural Personality Assessment Inventory (CPAI-2).
Methods: A sample of 100 Chinese adults was recruited through an online survey. Participants completed the Interpersonal Relatedness scale of the CPAI-2 and an open-ended language task. The study examined whether the structural features of the Interpersonal Relatedness factor could be reflected in participants' natural language expressions. Responses from the open-ended task were coded and analyzed using the large language model ChatGPT-5 for model construction. Cosine similarity analysis was conducted to evaluate the consistency and correspondence of the derived structures.
Results: (1) The results of two independent coding rounds demonstrated a high degree of similarity. (2) The model constructed by ChatGPT showed both convergence and divergence relative to the original CPAI-2 model. Specifically, the dimensions of Harmony and Social Sensitivity were not supported in the present findings. This may be related to differences in textual meaning and levels of coding and warrants further investigation.
Conclusion: By integrating an open-ended language task with large language model coding, this study extends the validation approach of the CPAI-2, provides empirical support for methodological diversification in personality assessment, and contributes to the continued application and development of the CPAI-2.

Cite this article

LI Wenbing , ZHU Tianjiao , GAN Yiqun . Exploring the Structure of the CPAI-2 Interpersonal Relatedness Factor Using Large Language Models: A Pilot Study[J]. Psychological Communications, 2026 , 9(1) : 62 -70 . DOI: 10.12100/j.issn.2096-5494.226010

References

[1] Cheung, F. M., & Cheung, S. F. (2003). Measuring personality and values across cultures:Imported versus indigenous measures.Online Read Psychol Cult, 4(4), 5.
[2] Cheung F. M., Cheung S. F., Zhang J. X., Leung K., Leong F., & Kuang H. (2008). Relevance of openness as a personality dimension in Chinese culture:Aspects of its cultural relevance.J Cross-Cult Psychol, 39(1), 81-108. doi:10.1177/0022022107311968
[3] Cheung F. M., Leung K., Fan R. M., Song W. Z., Zhang J. X., & Zhang J. P. (1996). Development of the Chinese personality assessment inventory.J Cross-Cult Psychol, 27(2), 181-199.
[4] Cheung F. M., Leung K., Zhang J. X., Sun H. F., Gan Y. Q., Song W. Z., & Xie D. (2001). Indigenous Chinese personality constructs:Is the five-factor model complete? J Cross-Cult Psychol, 32(4), 407-433. doi:10.1177/0022022101032004003
[5] Cheung S. F., Cheung F. M., Howard R., & Lim Y.H. (2006). Personality across the ethnic divide in Singapore:Are “Chinese traits” uniquely Chinese? Pers Individ Dif, 41(3), 467-477. doi:10.1016/j.paid.2005.12.023
[6] Costa, P. T., & McCrae, R. R. (1985). Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI):Professional manual. Odessa, FL:Psychological Assessment Resources.
[7] Cui T., Kam C. C., Cheng E. H., & Ho M. Y. (2022). Distinguishing between trait desirability and item desirability in predicting item scores:Is informant evaluation of personality free from social desirability? Pers Individ Dif, 196, 111708. doi:10.1016/j.paid.2022.111708
[8] De Paoli, S., & Mathis, W. S. (2025). Reflections on inductive thematic saturation as a potential metric for measuring the validity of an inductive thematic analysis with LLMs.Qual Quant, 59(1), 683-709. doi:10.1007/s11135-024-01950-6
[9] Devlin J., Chang M. W., Lee K., & Toutanova K. (2019). BERT:Pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies(pp. 4171-4186). Minneapolis, MN:Association for Computational Linguistics. doi:10.18653/v1/N19-1423
[10] Fan W., Li M., Leong F., & Zhou M. (2021). West meets East in a new two-polarities model of personality:Combining self-relatedness structure with independent-interdependent functions.Front Psychol, 12, 739900. doi:10.3389/fpsyg.2021.739900
[11] Guo Q., Cao J., Xie X., Liu S., Li X., Chen B., & Peng X. (2024). Exploring the potential of ChatGPT in automated code refinement:An empirical study. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering(pp. 1-13). New York, NY, USA:ACM. doi:10.1145/3597503.3623306
[12] Habib F., Ali Z., Azam A., Kamran K., & Pasha F. M. (2024). Navigating pathways to automated personality prediction:A comparative study of small and medium language models.Front Big Data, 7, 1387325. doi:10.3389/fdata.2024.1387325
[13] Jain N., Adeyinka S., Roseman L., & Allsop A. (2025). Multi-LLM thematic analysis with dual reliability metrics:Combining Cohen's kappa and semantic similarity for qualitative research validation[Preprint].arXiv. doi:10.48550/ARXIV.2512.20352
[14] Kao P. Y., Chen M. H., Chang W. A., Pan M. L., Shu W. D., Jong Y. J.,.. Lin Y. S. (2023). A genome-wide association study (GWAS) of the personality constructs in CPAI-2 in Taiwanese Hakka populations.PLoS One, 18(2), e0281903. doi:10.1371/journal.pone.0281903
[15] Kharchenko J., Roosta T., Chadha A., & Shah C. (2024). How well do LLMs represent values across cultures? Empirical analysis of LLM responses based on Hofstede cultural dimensions[Preprint].arXiv. doi:10.48550/ARXIV.2406.14805
[16] Lawrence H. R., Schneider R. A., Rubin S. B., Matarić M. J., McDuff D. J., & Jones Bell M. (2024). The opportunities and risks of large language models in mental health.JMIR Ment Health, 11, e59479. doi:10.2196/59479
[17] Lin C. E., Tan E., & Wang K. T. (2021). Perfect believer concepts and wellbeing among Buddhists, Christians, and Muslims in China.Ment Health Relig Cult,26(9), 873-889 doi:10.1080/13674676.2023.2282075
[18] Liu Y., He H., Han T., Zhang X., Liu M., Tian J.,.. Ge B. (2025). Understanding LLMs:A comprehensive overview from training to inference.Neurocomputing, 620, 129190. doi:10.1016/j.neucom.2024.129190
[19] Naeem M., Smith T., & Thomas L. (2025). Thematic analysis and artificial intelligence:A step-by-step process for using ChatGPT in thematic analysis.Int J Qual Methods, 24, 16094069251333886. doi:10.1177/16094069251333886
[20] Ornelas T., Araújo A. A., Araújo J., Araújo M., Trinkenreich B., & Kalinowski M. (2025). LLM-assisted thematic analysis:Opportunities, limitations, and recommendations[Preprint].arXiv. doi:10.48550/ARXIV.2511.14528
[21] Ortloff A. M., Fassl M., Ponticello A., Martius F., Mertens A., Krombholz K., & Smith M. (2023). Different researchers, different results? Analyzing the influence of researcher experience and data type during qualitative analysis of an interview and survey study on security advice. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York, NY, USA:ACM. doi:10.1145/3544548.3580766
[22] Parkington K., Teferra B. G., Rouleau-Tang M., Perivolaris A., Rueda A., Dubrowski A.,.. Bhat V. (2025). Human vs. LLM-based thematic analysis for digital mental health research:Proof-of-concept comparative study[Preprint].arXiv. doi:10.48550/ARXIV.2507.08002
[23] Prescott M. R., Yeager S., Ham L., Rivera Saldana C. D., Serrano V., Narez J.,.. Montoya J. (2024). Comparing the efficacy and efficiency of human and generative AI:Qualitative thematic analyses.JMIR AI, 3, e54482. doi:10.2196/54482
[24] Rathje S., Mirea D. M., Sucholutsky I., Marjieh R., Robertson C. E., & Van Bavel, J. J. (2024). GPT is an effective tool for multilingual psychological text analysis.Proc Natl Acad Sci, 121(34), e2308950121. doi:10.1073/pnas.2308950121
[25] Raza M. Z., Xu J., Lim T., Boddy L., Mery C. M., Well A., & Ding Y. (2025). LLM-TA:An LLM-enhanced thematic analysis pipeline for transcripts from parents of children with congenital heart disease[Preprint].arXiv. doi:10.48550/ARXIV.2502.01620
[26] Wang Q., Erqsous M., Barner K. E., & Mauriello M. L. (2025). LATA:A pilot study on LLM-assisted thematic analysis of online social network data generation experiences.Proc ACM Hum-Comput Interact, 9(2), 1-28. doi:10.1145/3711022
[27] Zhang Y., Huang F., Mo L., Liu X., & Zhu T. (2025). Suicidal ideation data augmentation and recognition technology based on large language models.Acta Psychol Sin, 57(6), 987. doi:10.3724/SP.J.1041.2025.0987
[28] Zhou M., Huang D., Ren F., Fan W., Mu W., Li F.,.. Cheung F. M. (2021). Short forms of the Cross-Cultural (Chinese) Personality Assessment Inventory:Reliability, validity, and measurement invariance across gender.Front Psychol, 12, 709032. doi:10.3389/fpsyg.2021.709032
[29] Zhou M., Li F., Mu W., Fan W., Zhang J., & Zhang M. (2023). Round outside and square inside:The latent profile structure and adaptability of Chinese interpersonal relatedness.Acta Psychol Sin, 55(3), 390. doi:10.3724/SP.J.1041.2023.00390
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