Additional module information: Introduction to Structrual Equation Modeling close

Content:
Lecture:
The lectures provide an introduction to structural equation modeling (SEM) using linear and generalized linear models and (generalized) mixed effect models, and give basics of analyzing data using these methods in the statistical software R. The lectures are accompanied by applied examples and cover the following topics:

• Essentials of structural equation modeling (SEM) and path analysis, understanding of cause-effect relations in ecological systems
• Similarities and differences between SEM and traditional statistical methods (regression, ANOVA, ANCOVA)
• Overview of the SEM modelling process
• Latent and composite variables in SEM
• SEM specification and estimation using software R
• Evaluation of SEM models
• Analysis of indirect effects in SEM to test mediating mechanisms
• Basics of grouped data and introduction to mixed effect models using software R
• Piecewise structural equation modeling (piecewise SEM) as an alternative SEM method for the analysis of count data, proportion data and nested data with mixed effect models
• Description of the methods and presentation of results.

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

• Gain basic knowledge of structural equation modeling (SEM) framework
• 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. close