Structural Equation Modeling: Steps and Analysis

Öz

This study aims to explore the potential of Structural Equation Modeling (SEM) as an advanced methodological tool and to provide a practical guide to assist researchers in its empirical application. Guided by three research questions (i.e., identifying the sequential steps in SEM application, strategies to ensure model reliability and validity, and the challenges of applying SEM in educational contexts) the study adopts a comprehensive research method to integrate methodological rigor with practical considerations. The findings highlight that SEM is a multi-stage process, encompassing model specification, identification, data preparation, parameter estimation, model fit assessment, modification, result interpretation, and validation. Ensuring reliability and validity requires both statistical evaluation, using indicators such as Composite Reliability, Cronbach’s Alpha, Average Variance Extracted, and factor loadings, and alignment with theoretical and contextual considerations. The study further identifies critical challenges in educational research, including data quality and representativeness, operationalization of abstract constructs, model complexity, technical expertise, and the interpretation and communication of results. SEM proves to be a powerful tool for examining complex relationships in education, but its effectiveness depends on the integration of rigorous statistical procedures, theoretical grounding, and sensitivity to the educational context, reinforcing the importance of reflective and methodologically informed research practices.

Anahtar Kelimeler

• Research Methods
• Educational Research
• Methodologies in Education
• Quantitative Research Analysis

Yapısal Eşitlik Modellemesi: Adımlar ve Analiz

Öz

Bu çalışma, Yapısal Eşitlik Modellemesi'nin (SEM) gelişmiş bir metodolojik araç olarak potansiyelini araştırmayı ve araştırmacılara ampirik uygulamada yardımcı olacak pratik bir kılavuz sunmayı amaçlamaktadır. Üç araştırma sorusu (SEM uygulamasındaki sıralı adımların belirlenmesi, modelin güvenilirliğini ve geçerliliğini sağlamak için stratejiler ve eğitim bağlamında SEM'in uygulanmasındaki zorluklar) rehberliğinde, çalışma metodolojik titizliği pratik hususlarla bütünleştirmek için kapsamlı bir araştırma yöntemi benimsemiştir. Bulgular, SEM'in model spesifikasyonu, tanımlama, veri hazırlama, parametre tahmini, model uyum değerlendirmesi, modifikasyon, sonuç yorumlama ve doğrulamayı kapsayan çok aşamalı bir süreç olduğunu vurgulamaktadır. Güvenilirlik ve geçerliliği sağlamak için, Bileşik Güvenilirlik, Cronbach's Alpha, Ortalama Çıkarılan Varyans ve faktör yüklemeleri gibi göstergeler kullanılarak istatistiksel değerlendirme yapılması ve teorik ve bağlamsal hususlarla uyum sağlanması gerekmektedir. Çalışma ayrıca, veri kalitesi ve temsil gücü, soyut kavramların işlevselleştirilmesi, model karmaşıklığı, teknik uzmanlık ve sonuçların yorumlanması ve iletilmesi gibi eğitim araştırmalarındaki kritik zorlukları da belirlemektedir. SEM, eğitimdeki karmaşık ilişkileri incelemek için güçlü bir araç olduğunu kanıtlamaktadır, ancak etkinliği, titiz istatistiksel prosedürlerin, teorik temellerin ve eğitim bağlamına duyarlılığın entegrasyonuna bağlıdır ve bu da yansıtıcı ve metodolojik olarak bilgilendirilmiş araştırma uygulamalarının önemini pekiştirmektedir.

Anahtar Kelimeler

• Araştırma Yöntemleri
• Eğitim Araştırmaları
• Eğitimde Metodolojiler
• Nicel Araştırma Analizi

Kaynakça

1. Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168. https://doi.org/10.1016/j.im.2019.05.003
2. Cheung, G., Cooper-Thomas, H., Lau, R., & Wang, L. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 41, 745-783. https://doi.org/10.1007/s10490-023-09871-y
3. Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
4. Davvetas, V., Diamantopoulos, A., Zaefarian, G., & Sichtmann, C. (2020). Ten basic questions about structural equations modeling you should know the answers to – But perhaps you don't. Industrial Marketing Management, 90, 252-263. https://doi.org/10.1016/j.indmarman.2020.07.016
5. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). An Introduction to Structural Equation Modeling. In: Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Classroom Companion: Business. Springer. https://doi.org/10.1007/978-3-030-80519-7_1
6. Hair, J. F., Sarstedt, M., Ringle, C. M., Sharma, P., & Liengaard, B. (2024). Going beyond the untold facts in PLS–SEM and moving forward. European Journal of Marketing, 58(13), 81–106. https://doi.org/10.1108/EJM-08-2023-0645
7. HajiAbedi, S., Eslami, H., Afshani, S. A., & Pourkarimi, J. (2020). Applying Structural Equations Modeling (SEM) in Designing an Educational Effectiveness Model (Case Study: Governance Organization Employees). Agricultural Marketing and Commercialization, 4(1), 121-136.
8. Khairi, M. I., Susanti, D., & Sukono, S. (2021). Study on Structural Equation Modeling for Analyzing Data. International Journal of Ethno-Sciences and Education Research (IJEER), 1(3), 52-60. https://doi.org/10.46336/ijeer.v1i3.295

9. Lim, Y. W., Darmesah, G., Pang, N. T. P., & Ho, C. M. (2023). A bibliometric analysis of the structural equation modeling in mathematics education. Eurasia Journal of Mathematics, Science and Technology Education, 19(12), em2365. https://doi.org/10.29333/ejmste/13838
10. Lyu, J., Shepherd, D., & Lee, K. (2023). The impact of entrepreneurship pedagogy on nascent student entrepreneurship: an entrepreneurial process perspective. Studies in Higher Education, 49(1), 62–83. https://doi.org/10.1080/03075079.2023.2220722
11. Magno, F., Cassia, F., & Ringle, C. M. (2024). A brief review of partial least squares structural equation modeling (PLS-SEM) use in quality management studies. The TQM Journal, 36(5), 1242–1251. https://doi.org/10.1108/TQM-06-2022-0197
12. McCrae, R., Kurtz, J., Yamagata, S., & Terracciano, A. (2010). Internal Consistency, Retest Reliability, and their Implications for Personality Scale Validity. Pers Soc Psychol Rev., 15(1), 28–50. https://doi.org/10.1177/1088868310366253
13. Mueller, R. O., & Hancock, G. R. (2019). Structural equation modeling. In G. R. Hancock, L. M. Stapleton, & R. O. Mueller (Eds.), The reviewer’s guide to quantitative methods in the social sciences (2nd ed., pp. 445–456). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781315755649-33
14. Shi, D., & Maydeu-Olivares, A. (2019). The Effect of Estimation Methods on SEM Fit Indices. Educ Psychol Meas, 80(3), 421–445. https://doi.org/10.1177/0013164419885164
15. Stein, C., Morris, N., Hall, N., & Nock, N. (2017). Structural Equation Modeling. Methods Mol Biol, 1666, 557-580. https://doi.org/10.1007/978-1-4939-7274-6_28
16. Sun, Y., & Liu, L. (2023). Structural equation modeling of university students’ academic resilience academic well-being, personality and educational attainment in online classes with Tencent Meeting application in China: investigating the role of student engagement. BMC Psychology, 11(347), 1-18. https://doi.org/10.1186/s40359-023-01366-1
17. Taherdoost, H. (2022). What are Different Research Approaches? Comprehensive Review of Qualitative, Quantitative, and Mixed Method Research, Their Applications, Types, and Limitations. Journal of Management Science & Engineering Research, 5(1), 53–63. https://doi.org/10.30564/jmser.v5i1.4538
18. Tarka, P. (2018). An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Quality & Quantity, 52, 313–354. https://doi.org/10.1007/s11135-017-0469-8
19. Thiem, J., Preetz, R., & Haberstroh, S. (2023). How research-based learning affects students’ self-rated research competences: evidence from a longitudinal study across disciplines. Studies in Higher Education, 48(7), 1039–1051. https://doi.org/10.1080/03075079.2023.2181326
20. Timans, R., Wouters, P., & Heilbron, J. (2019). Mixed methods research: what it is and what it could be. Theory and Society, 48, 193-216. https://doi.org/10.1007/s11186-019-09345-5
21. Tomarken, A. J., & Waller, N. G. (2005). Structural equation modeling: strengths, limitations, and misconceptions. Annu Rev Clin Psychol, 1, 31-65. https://doi.org/10.1146/annurev.clinpsy.1.102803.144239
22. Ullman, J. B., & Bentler, P. M. (2013). Structural equation modeling. In J. A. Schinka, W. F. Velicer, & I. B. Weiner (Eds.), Handbook of psychology: Research methods in psychology (2nd ed., pp. 661–690). John Wiley & Sons, Inc.
23. Wang, J., Hefetz, A., & Liberman, G. (2017). Applying structural equation modelling in educational research / La aplicación del modelo de ecuación estructural en las investigaciones educativas. Culture and Education: Cultura y Educación, 29(3), 563-618. https://doi.org/10.1080/11356405.2017.1367907
24. Warneke, K., Gronwald, T., Wallot, S., Magno, A., Hillebrecht, M., & Wirth, K. (2025). Discussion on the validity of commonly used reliability indices in sports medicine and exercise science: a critical review with data simulations. European Journal of Applied Physiology, 125, 1511-1526. https://doi.org/10.1007/s00421-025-05720-6
25. Williams, L. J., Vandenberg, R. J., & Edwards, J. R. (2009). 12 Structural Equation Modeling in Management Research: A Guide for Improved Analysis. The Academy of Management Annals, 3(1), 543–604. https://doi.org/10.1080/19416520903065683
26. Yin, H., & Huang, S. (2021). Applying structural equation modelling to research on teaching and teacher education: Looking back and forward. Teaching and Teacher Education, 107, 103438. https://doi.org/10.1016/j.tate.2021.103438