Exploring the Application of Structural Equation Modeling in Testing Theoretical Relationships Among Latent Variables

Posted: Sep 25, 2025

• Grace Campbell
• Jack Taylor
• Mia Clark

Abstract

This research presents a comprehensive investigation into the application of Structural Equation Modeling (SEM) for testing complex theoretical relationships among latent variables across diverse disciplines. While SEM has traditionally been employed in social sciences, this study demonstrates its novel application in computational fields and interdisciplinary research contexts. We develop an innovative methodological framework that integrates machine learning techniques with traditional SEM approaches, creating a hybrid analytical tool capable of handling complex, high-dimensional datasets. Our methodology addresses several limitations of conventional SEM, including assumptions of linearity and normality, through the incorporation of non-parametric estimation techniques and robust standard error calculations. The research examines theoretical relationships among latent constructs in three distinct domains: technology adoption behaviors, computational creativity assessment, and environmental sustainability indicators. Results reveal previously undetected mediation and moderation effects, demonstrating SEM's capacity to uncover complex causal pathways that traditional analytical methods might overlook. The study contributes original insights into model specification techniques, measurement invariance testing across heterogeneous populations, and the integration of qualitative data within quantitative SEM frameworks. Our findings challenge conventional wisdom regarding variable relationships in several theoretical models and propose alternative conceptual frameworks that better account for observed data patterns. This research advances methodological sophistication in latent variable modeling while providing practical guidance for researchers seeking to apply SEM in novel contexts beyond its traditional domains.