Structural equation modeling (SEM) is a widely applied method in various disciplines, including business, economics, psychology, sociology, and medicine. Its populary is arguably due to ists ability to model and assess theories comprising theoretical concepts and to account for various forms of measurement errors. Traditionally, SEM has been primarily used to model theoretical concepts as latent variables, i.e., variables that explain the variance-covariance structure of their related variables. In recent years, the composite, i.e., a linear combination of more elementary variables that transmits all information between the variables that make up the composite and all other variables in the model, has gained increasing attention as a second type of theoretical construct in SEM. However, due to the types of relations in a composite model, the integration of composites in a structural equation model is not straightforwards. This presentation will introduce the composite model and discuss various model specifications for incorporating composites into a structural equation model. Finally, the presentation will discuss the main approaches to estimating composite models in SEM.

Vortrag von Dr. Tamara Schamberger,

Data Science Group, Faculty of Business Administration and Economics Bielefeld University

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