Additional module information: Introduction to Structural Equation Modeling  Schließen

Content:
During the seminars, the topics covered in the lectures are deepened and discussed.

Learning objectives:
In this module the students acquire the following knowledge and skills:

• Gain basic knowledge of structural equation modeling (SEM) framework and path analysis 
• Learn how to develop, evaluate, refine, solve, and interpret structural equation models
• Master basic skills to analyze data with SEM in the software R
• Gain basic knowledge of piecewise SEM and how it differs from the classical SEM
• Master basic skills to implement in the SEM count, binary, proportion, and categorical response variables, as well as nested data with the mixed effect models using piecewise SEM approach in the software R
• Gain basic understanding of causal relations, bottom-up and top-down control, and how to calculate direct and indirect effects in ecological and biological systems (e.g., communities, food webs, ecosystems)
• Independently apply SEM for different data types
• Present statistical methods and results in oral and written form to a specialist audience

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Grace (2006) Structural Equation Modeling and Natural Systems. Cambridge Univ. Press.

Shipley, B. (2016). Cause and correlation in biology: A user's guide to path analysis, structural equations and causal inference with R. Cambridge university press.

Lefcheck (2021) Piecewise Structural Equation Modeling in Ecological Research: https://jslefche.github.io/sem_book

Shipley, B. (2009). Confirmatory path analysis in a generalized multilevel context. Ecology, 90(2), 363-368. Schließen