PhD Course: DRL036 Covariance-Based Structural Equation Modeling: Theory and Applications

In today’s data-rich environment, researchers and professionals are increasingly expected to analyse complex relationships among variables—far beyond what traditional statistical techniques can handle. Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA) offer powerful tools to generate deep insights into latent constructs, measurement quality, and theoretical model testing. 

This hands-on, medium-advanced course is designed for PhD students in business administration and related fields. Focusing on covariance structures, this course equips participants to go beyond surface-level correlations and explore the underlying mechanisms driving human behaviour, market dynamics, and organisational performance. 

Through a structured five-day intensive format, participants will gain a solid foundation in matrix algebra, likelihood theory, and multivariate normality—before diving into applications like longitudinal modelling, mediation and moderation analysis, and simulation-based evaluation. You will learn the “how” and the “why” behind model selection, fit assessment, and diagnostic tools. The course literature includes cutting-edge research and applied examples demonstrating how SEM is used in top-tier publications.

More details about the course.

Published Sep. 27, 2024 11:09 AM - Last modified May 15, 2025 11:19 AM