Veri Analizinde Yapay Zeka Kullanımı: PLS-SEM Analizinde ChatGPT ve SmartPLS Çıktılarının Karşılaştırılması

Yıl 2025, ERKEN GÖRÜNÜM, 1 - 1

Yavuz Toraman , Orçun Muhammet Şimşek

https://doi.org/10.2339/politeknik.1739414

Öz

Bu çalışma, Kısmi En Küçük Kareler Yapısal Eşitlik Modellemesi (PLS-SEM) analizi yapmak için geniş bir dil modeli olan ChatGPT'nin kullanılabilirliğini ve güvenilirliğini değerlendirmeyi amaçlamaktadır. Bu amaçla, Teknoloji Kabul Modeline dayalı bir araştıma modeli, hem SmartPLS hem de ChatGPT kullanılarak 523 katılımcıyla test edilmiştir. İç tutarlılık, güvenilirlik ve geçerlilik, VIF değerleri, yol katsayıları ve R-kare değerleri gibi temel istatistiksel göstergeler karşılaştırılmıştır. Sonuçlar, analizin tüm aşamalarında ChatGPT ve SmartPLS çıktıları arasında yüksek düzeyde tutarlılık olduğu gözlemlenmiştir. Ayrıca SmartPLS ve ChatGPT çıktılarının Pearson Correlation (r), Mean Absolute Error (MAE) ve Root Mean Square Error (RMSE) değerleri inclenerek istatistiksel olarakda desteklenmiştir. Her iki araç da aynı anlamlı ve anlamsız ilişkileri tespit etmiş ve benzer aracılık etkileri bulunmuştur. ChatGPT görselleştirme yeteneklerinden yoksun olmasına ve adım adım rehberlik gerektirmesine rağmen, istatistiksel olarak geçerli sonuçları başarılı bir şekilde yeniden üretmiştir. Bu sonuçlar ChatGPT'nin özellikle lisanslı yazılımlara erişimi olmayan araştırmacılar için tamamlayıcı ve uygun maliyetli bir araç olabileceğini göstermektedir. Gelecekteki araştırmalar bu aracın diğer istatistiksel teknikler ve yapay zeka modelleri ile kullanılabilirliğini araştırmalıdır.

Anahtar Kelimeler

SmartPLS , yapay zeka , istatistiksel analiz , ChatGPT , PLS-SEM

Artificial Intelligence (AI) Use in Data Analysis: A Comparison of ChatGPT and SmartPLS Outputs in PLS-SEM Analysis

Öz

This study aims to evaluate the usability and reliability of ChatGPT, a large language model, to conduct Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis. For this purpose, a research model based on the Technology Acceptance Model was tested with 523 participants using both SmartPLS and ChatGPT. Key statistical indicators such as internal consistency, convergent and discriminant validity, VIF values, path coefficients, and R-square values were compared. The results show a high level of consistency between ChatGPT and SmartPLS outputs throughout all stages of the analysis. Furthermore, the statistical validity of SmartPLS and ChatGPT outputs is supported by examining their Pearson Correlation (r), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) values. Both tools identified the same significant and nonsignificant relationships, and similar mediation effects were found. Although ChatGPT lacks visualization capabilities and requires step-by-step guidance, it successfully reproduced statistically valid results. These results suggest that ChatGPT may be a complementary and cost-effective tool for researchers, especially those without access to licensed software. Future research should explore its usability with other statistical techniques and artificial intelligence models.

Anahtar Kelimeler

SmartPLS , artificial intelligence , statistical analysis , ChatGPT , PLS-SEM

Kaynakça

• [1] T. Hayes and S. Usami, ‘Factor Score Regression in the Presence of Correlated Unique Factors’, Educational and Psychological Measurement, 80(1): 5–40, Feb. (2020).
• [2] J. Miles, ‘Structural Equation Modelling’, Historical Social Research, vol. 23, p. 159171, (1998).
• [3] C. M. Musil, S. L. Jones, and C. D. Warner, ‘Structural equation modeling and its relationship to multiple regression and factor analysis’, Res. Nurs. Health, 21(3):271–281, (1998).
• [4] J. F. Hair, G. T. M. Hult, C. M. Ringle, M. Sarstedt, N. P. Danks, and S. Ray, ‘An Introduction to Structural Equation Modeling’, in Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R, in Classroom Companion: Business. , Cham: Springer International Publishing, pp. 1–29., (2021).
• [5] G. Cepeda-Carrion, J.-G. Cegarra-Navarro, and V. Cillo, ‘Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management’, JKM, 23(1):67–89,
• [6] A. Moosavi and R. bt Juhari, ‘SPSS vs. AMOS: A Comparative Approach to Analyzing Moderators in Behavioral Research’, IJSSHR, 7(8):6159–6164, Aug. (2024).
• [7] B. K. Sarker, D. K. Sarker, S. R. Shaha, D. Saha, and S. Sarker, ‘Why Apply SPSS, SmartPLS and AMOS: An Essential Quantitative Data Analysis Tool for Business and Social Science Research Investigations’, IJRISS, vol. VIII, no. IX, pp. 2688–2699, (2024).
• [8] M. Sarstedt, C. M. Ringle, and J. F. Hair, ‘Partial Least Squares Structural Equation Modeling’, in Handbook of Market Research, C. Homburg, M. Klarmann, and A. Vomberg, Eds., Cham: Springer International Publishing, pp. 1–40., (2017).
• [9] H. Wold, ‘Path Models with Latent Variables: The NIPALS Approach’, in Quantitative Sociology, Elsevier, pp. 307–357., (1975).
• [10] M. Sarstedt, ‘Der Knacks and a Silver Bullet’, in The Great Facilitator, B. J. Babin and M. Sarstedt, Eds., Cham: Springer International Publishing, pp. 155–164., (2019).
• [11] J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A primer on partial least squares structural equation modeling (PLS-SEM), Second edition. Los Angeles London New Delhi Singapore Washington DC Melbourne: SAGE, (2017).
• [12] Alnıpak, Serdar, and Yavuz Toraman. "‘‘I am ChatGPT. would you accept me to assist you in logistics management?’’ An Empirical Study From Türkiye." Logforum 21.3, 11., (2025).
• [13] J. Ma, P. Wang, B. Li, T. Wang, X. S. Pang, and D. Wang, ‘Exploring User Adoption of ChatGPT: A Technology Acceptance Model Perspective’, International Journal of Human–Computer Interaction, 41(2):1431–1445, (2025).
• [14] J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, ‘A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, august 31, 1955’, AI Magazine, 27(4):12–12, (2006).
• [15] K. Uludag, ‘The Use of AI-Supported Chatbot in Psychology’:, in Advances in Medical Technologies and Clinical Practice, K. Uludag and N. Ahmad, Eds., IGI Global, pp. 1–20., (2024).
• [16] M. Cascella, J. Montomoli, V. Bellini, and E. Bignami, ‘Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios’, J Med Syst, 47(1):33, (2023).
• [17] S. Akter, S. Fosso Wamba, and S. Dewan, ‘Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality’, Production Planning & Control, 28(11-12):1011–1021, (2017).
• [18] J. Hair, C. L. Hollingsworth, A. B. Randolph, and A. Y. L. Chong, ‘An updated and expanded assessment of PLS-SEM in information systems research’, IMDS, 117(3):442–458, (2017).
• [19] D. S. Kumar and K. Purani, ‘Model specification issues in PLS-SEM: Illustrating linear and non-linear models in hospitality services context’, JHTT, 9(3):338–353, (2018).
• [20] M. Rönkkö and J. Evermann, ‘A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling’, Organizational Research Methods, 16(3):425–448, (2013).
• [21] R. Calderon Jr., G. Kim, C. Ratsameemonthon, and S. Pupanead, ‘Assessing the Adaptation of a Thai Version of the Ryff Scales of Psychological Well-Being: A PLS-SEM Approach’, PSYCH, 11(7):1037–1053, (2020).
• [22] E. W. L. Cheng, S. K. W. Chu, and C. S. M. Ma, ‘Students’ intentions to use PBWorks: a factor-based PLS-SEM approach’, ILS, 120(7/8):489–504, Jul. (2019).
• [23] E. Erwin, Y. K. M. Suade, and N. Alam, ‘Social Media Micro-enterprise: Utilizing Social Media Influencers, Marketing Contents and Viral Marketing Campaigns to Increase Customer Engagement’, in Proceedings of the International Conference of Economics, Business, and Entrepreneur (ICEBE 2022), vol. 241, Nairobi, Yuliansyah, H. Jimad, R. Perdana, G. E. Putrawan, and T. Y. Septiawan, Eds., in Advances in Economics, Business and Management Research, vol. 241. , Paris: Atlantis Press SARL, pp. 578–593., (2023).
• [24] G. T. M. Hult, J. F. Hair, D. Proksch, M. Sarstedt, A. Pinkwart, and C. M. Ringle, ‘Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling’, Journal of International Marketing, 26(3):1–21, Sep. (2018).
• [25] J. Riou, H. Guyon, and B. Falissard, ‘An introduction to the partial least squares approach to structural equation modelling: a method for exploratory psychiatric research’, Int J Methods Psych Res, 25(3): 220–231, (2016).
• [26] J. Henseler, C. M. Ringle, and R. R. Sinkovics, ‘The use of partial least squares path modeling in international marketing’, in Advances in International Marketing, vol. 20, R. R. Sinkovics and P. N. Ghauri, Eds., Emerald Group Publishing Limited, 277–319., (2009).
• [27] K. Keskinbora and F. Güven, ‘Artificial Intelligence and Ophthalmology’, tjo, 50(1):37–43, (2020).
• [28] Y. K. Dwivedi et al., ‘Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy’, International Journal of Information Management, vol. 57, p. 101994, (2021).
• [29] H. Gil De Zúñiga, M. Goyanes, and T. Durotoye, ‘A Scholarly Definition of Artificial Intelligence (AI): Advancing AI as a Conceptual Framework in Communication Research’, Political Communication, 41(2):317–334, (2024),
• [30] B. G. Tabachnick and L. S. Fidell, Using multivariate statistics, 6. ed., International ed. in Always learning. Boston Munich: Pearson, (2013).
• [31] KOÇ USTALI, Nesrin, et al. "Küresel Risk Yönetim İndeksi Değerlendirmesi: Gri Tabanlı Topsis Yöntemi Uygulaması." Journal of Polytechnic 27(5), (2024).
• [32] Toraman Y. Space Logistics and Risks: A Study on Spacecraft. Politeknik Dergisi., 28(2):573-584., (2025).
• [33] De Winter, Joost CF, Samuel D. Gosling, and Jeff Potter. "Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data." Psychological methods 21.3, 273., (2016).
• [34] Benesty, Jacob, et al. "Pearson correlation coefficient." Noise reduction in speech processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1-4., (2009).
• [35] Willmott, Cort J., and Kenji Matsuura. "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance." Climate research 30.1, 79-82., (2005).
• [36] Chai, Tianfeng, and Roland R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature." Geoscientific model development 7.3 1247-1250., (2014).