DECODING SYMBOLISM IN STATISTICAL MODELING: AN EXPLORATION OF STRUCTURAL EQUATION MODELING AND DESCRIPTIVE ANALYSIS
Abstract
This study examined the critical role of symbolism in Structural Equation Modeling (SEM) as a tool for communicating complex statistical concepts and relationships. SEM employs a systematic framework of symbols, including latent variables (η), observed variables (y), factor loadings (λ), residuals (ζ), measurement errors (ε), and variance-covariance terms (Ψ and Θ), to represent theoretical constructs and their relationships. By analyzing these symbols, the study highlighted their importance in ensuring accurate model specification, enhancing interpretability, and fostering interdisciplinary collaboration. The visual and mathematical language of SEM was shown to bridge the gap between abstract theoretical frameworks and empirical data, enabling researchers to test hypotheses, evaluate relationships, and generate meaningful findings with precision and clarity. The study also underscored the need for a deeper understanding of these symbols to support robust and reliable statistical modeling. Future research should focus on expanding this symbolic framework to accommodate advanced methodologies, such as multilevel modeling and longitudinal SEM, to address the growing complexity of analytical challenges. This study contributes to empowering researchers by enhancing their ability to effectively use SEM for innovation and communication in statistical analysis.References
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Chitladaporn, P., & Kanchanawongpaisan, S. (2024). A Comprehensive Review of A Beginner’s Guide to Structural Equation Modeling: Enhancing Accessibility for New Researchers. Multidisciplinary Journal of Shinawatra University, 1(3), 14–21.
Fisher, R. (1992). Statistical Methods for Research Workers. In S. Kotz, & N. Johnson, Breakthroughs in Statistics. Springer Series in Statistics (pp. 66–70). New York, NY.: Springer. doi:https://doi.org/10.1007/978-1-4612-4380-9_6
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Jöreskog, K. G. (1970). A general method for estimating a linear structural equation system. In A. Goldberger, & O. Duncan , Structural equation models in the social sciences (pp. 85–112). Seminar Press.
Kanchanawongpaisan, S. (2024). Navigating the Future of Quantitative Research: The Power of StructuralEquation Modeling. Multidisciplinary Journal of Shinawatra University, 1(3), 1–13.
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.
Pearson, K. (1985). Contributions to the Mathematical Theory of Evolution, II: Skew Variation in Homogeneous Material. Philosophical Transactions of the Royal Society, 186, 343–414. doi:https://doi.org/10.1098/rsta.1895.0010
Wright, S. (1921). Correlation and Causation. Journal of Agricultural Research, 20(3), 557–585.
Chitladaporn, P., & Kanchanawongpaisan, S. (2024). A Comprehensive Review of A Beginner’s Guide to Structural Equation Modeling: Enhancing Accessibility for New Researchers. Multidisciplinary Journal of Shinawatra University, 1(3), 14–21.
Fisher, R. (1992). Statistical Methods for Research Workers. In S. Kotz, & N. Johnson, Breakthroughs in Statistics. Springer Series in Statistics (pp. 66–70). New York, NY.: Springer. doi:https://doi.org/10.1007/978-1-4612-4380-9_6
Hair, J. F., G. Tomas, H. M., Ringle , C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Sprinker.
Hinton, P. R., McMurray, I., & Brownlow, C. (2014). SPSS Explained. London: Routledge.
Jöreskog, K. G. (1970). A general method for estimating a linear structural equation system. In A. Goldberger, & O. Duncan , Structural equation models in the social sciences (pp. 85–112). Seminar Press.
Kanchanawongpaisan, S. (2024). Navigating the Future of Quantitative Research: The Power of StructuralEquation Modeling. Multidisciplinary Journal of Shinawatra University, 1(3), 1–13.
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.
Pearson, K. (1985). Contributions to the Mathematical Theory of Evolution, II: Skew Variation in Homogeneous Material. Philosophical Transactions of the Royal Society, 186, 343–414. doi:https://doi.org/10.1098/rsta.1895.0010
Wright, S. (1921). Correlation and Causation. Journal of Agricultural Research, 20(3), 557–585.
Published
2025-01-16
How to Cite
KANCHANAWONGPAISAN, Sipnarong; PAMUNGKAS, Tubagus.
DECODING SYMBOLISM IN STATISTICAL MODELING: AN EXPLORATION OF STRUCTURAL EQUATION MODELING AND DESCRIPTIVE ANALYSIS.
INTERNATIONAL JOURNAL OF EDUCATIONAL MANAGEMENT, [S.l.], v. 1, n. 1, p. 17-24, jan. 2025.
Available at: <https://www.journal.unrika.ac.id/index.php/IJEM/article/view/7355>. Date accessed: 15 feb. 2026.
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