Structural Equation Modeling and Path Analysis Foundations

Research in fields like psychology, education, and management often involves complex concepts that cannot be measured directly, such as job satisfaction or brand loyalty. Structural Equation Modeling (SEM) provides the essential statistical framework to analyze these hidden variables and their interconnected relationships simultaneously. This course transforms your understanding of data analysis by moving beyond simple regression into the world of multivariate modeling, allowing you to test entire theoretical frameworks in a single analysis. What you'll learn: - Understand the core concepts of latent constructs and measurement error in statistical modeling. - Apply path analysis to identify direct, indirect, and total effects between variables. - Perform confirmatory factor analysis (CFA) to ensure your measurement scales are reliable and valid. - Interpret modern model fit indices like CFI, TLI, and RMSEA to evaluate your research findings. - Execute structural models using LISREL and understand the logic of covariance-based estimation. - Explore advanced applications including multi-group analysis and longitudinal data patterns. - Master the reporting standards for SEM results in academic and professional research. The course begins with clear definitions of key terminology and the statistical logic behind SEM before walking through the practical steps of model specification, identification, and estimation. You will learn through detailed written explanations and step-by-step analysis of research scenarios. This course is designed for beginners in multivariate statistics who have a basic grasp of correlation and regression. It is ideal for students and researchers looking to elevate their data analysis skills. Enhance your research capabilities with sophisticated structural modeling techniques.