Zusätzliche Modulinfos: Introduction to Structrual Equation Modeling Schließen

Vorkenntnisse in R und mit linearen Modellen, wie Regression, ANOVA und ANCOVA sind notwendig. Bitte am Rechner arbeiten, auf einem Tablet lässt sich R schlecht installieren! Die Vorlesungen werden am Vortag jedes Kurstages zur Verfügung gestellt. Die Studierenden sollen sich die Vorlesungen vor dem entsprechenden Seminar anschauen. Schließen

Inhalte:
Vorlesung: The lectures provide introduction to structural equation modeling (SEM) 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), understanding of causeeffect 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
• Introductory overview of piecewise structural equation modeling (piecewise SEM) as an alternative SEM method for the analysis of nested data
• Description of the methods and presentation of results.

Qualifikationsziele:
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 knowledge of nested data and mixed effect models
• Gain basic knowledge of piecewise SEM and how it differs from the classical SEM
• Master basic skills to analyse nested data with the mixed effect models and piecewise SEM using the software R
• Gain basic understanding of causal relations, bottom-up and top-down control, and direct and indirect effects in ecological and biological systems (e.g., communities, food webs, ecosystems)
• Independently apply SEM and mixed effect models
• Present statistical methods and results in oral and written form to a specialist audience.

Schließen

Grace (2006) Structural Equation Modeling and Natural Systems. Cambridge Univ. Press.
Zuur et al. (2009) Mixed Effects Models and Extensions in Ecology in R. Springer
Crawley, M.J. (2012). The R book. John Wiley & Sons. Schließen