Structural Equation Modeling: Fundamentals and Practical Applications

Researchers and data analysts often need to model complex, real-world systems where key variables cannot be directly measured. Structural Equation Modeling (SEM) provides the powerful multivariate framework necessary to analyze these intricate, multi-layered relationships simultaneously. This written course guides you through the fundamental theory and practical steps of SEM. You will transition from basic regression concepts to constructing, evaluating, and interpreting sophisticated path models and confirmatory factor analyses using modern statistical syntax. What you'll learn: - Understand the foundational concepts of latent variables, measurement errors, and path analysis. - Build and specify confirmatory factor analysis models to validate measurement instruments. - Estimate structural equation models using modern statistical software syntax. - Evaluate model fit using key indices and implement scientifically sound model modifications. - Apply SEM techniques to analyze multi-group data and complex direct or indirect mediation effects. - Practice troubleshooting common estimation issues, such as non-normal data and missing values. The course begins with essential terminology and the conceptual foundations of covariance-based SEM. You will then progress through step-by-step written explanations of model specification, estimation, evaluation, and reporting, accompanied by practical syntax examples. This course is designed for beginners to multivariate modeling, including students, researchers, and data analysts in the social sciences, psychology, education, and business. A basic understanding of correlation and multiple regression is recommended. Start reading today to unlock deeper insights from your multivariate data.