319414 Introduction to Structural Equation Modeling (V) (SoSe 2025)

The course "Introduction to Structural Equation Modeling" provides a comprehensive introduction to the fundamentals of structural equation modeling (SEM), beginning with the motivation for using SEM for complex data analysis. Students begin with a recap of basic statistical concepts to ensure a solid foundation. The course then moves on to path analysis, a preliminary stage of SEM that helps to understand causal relationships between variables. Students will then learn about model specification, which involves defining the model structure and hypotheses. The course covers model identification, focusing on the conditions necessary to obtain unique parameter estimates. In the model estimation section, different estimation techniques and software tools are introduced. Next, model evaluation is discussed, where students learn to evaluate model fit and diagnose potential problems of their model. Finally, the course covers advanced topics in structural equation modeling, providing insight into more complex models and applications. This structured approach ensures a deep and practical understanding of SEM.

Literaturangaben

Bollen, K. (1989) Structural Equations with Latent Variables, Wiley (New York)
Kline, R. B. (2016) Principles and Practice of Structural Equation Modeling, Guilford Press (New York)
Henseler, J. (2021) Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables, Guilford Press (New York)
Jöreskog K.G. (1969) A General Approach to Confirmatory Maximum Likelihood Factor Analysis, Psychometrika, 34(2): 183 - 202
Rosseel, Y. (2012) lavaan: An R Package for Structural Equation Modeling, Journal of Statistical Software, 48(2): 1 - 36