Course

Structural Equation Modelling

Dates 2 - 6 March 2026
Location Wageningen University Campus
ECTS 1.5

Structural Equation Modelling (SEM) is a methodology that falls within the broader topic of “Methods for Causal Investigations”. There is a great deal of activity in this broader field and many are now referring to a “causal revolution” based on recent developments. There is a substantial expansion in conceptual and terminological refinements required to overcome the historical oversimplifications that have obscured the central role of scientific expert knowledge in building causal understanding. 

This course will empower attendees to maximize and defend their scientific interpretations based on their expert knowledge as informed by quantitative results. A new procedure, “causal knowledge analysis” will be introduced as a way to document expert knowledge and set the stage for SEM analyses. Once that is accomplished, structural equation modeling techniques will be presented as a methodology for representing and evaluating hypotheses involving networks of relationships. The material presented in this course represent a beginning to the fourth generation of SEM – using SEM for causal inquiry.

In the course, initial focus will be on defining and characterizing the properties of existing causal knowledge relevant to students’ research projects or interests. That will be followed by the introduction of traditional techniques for SEM analyses using the R program. Examples in the course will feature studies of ecosystems and their components. Activities will focus on helping participants in their research projects and those with data are encouraged to bring for in-class analyses.

Each day begins at 08:30 and ends at around 16:30 however, this schedule will be modified, depending on how well we have progressed through each topic. 

Day 1
  • Intro to a general paradigm for building causal knowledge
  • Intro to causal knowledge analysis (CKA) and mechanistic reasoning
  • Class exercises with CKA
Day 2
  • Defining study objectives
  • Matching information and data needs with objectives
  • Model specification options
  • Model assumptions interrogation
Day 3
  • Statistical causal inference ignoring existing causal knowledge
  • Techniques for statistical causal inference
  • Integrated causal analysis and characterization of mechanisms
Day 4
  • Establishing and evaluating model implications
  • Sensitivity tests
  • External validity and transportability
  • Defining future information needs for deepening causal knowledge
Day 5
  • Working groups
  • Class presentations and discussions
  • Recap for how to present your findings

Dr. Vahe Avagyan & Dr. Pariya Behrouzi 

 

Target GroupThe course is aimed at PhD candidates, postdocs, and other academics
Group SizeMax. 30 participants
Course duration5 days
Prior knowledgeTraining in some field of science. Basic knowledge of probability and statistics. Basic knowledge of R is recommended (e.g. installing packages, reading data-files, linear regression).
Recommended LiteratureA pdf of course materials will be provided in advance 
LocationWageningen Campus. Forum building B0106 (Monday, Wednesday, Thursday & Friday) and B0214 (Tuesday)

 

 

FEE1 

PE&RC/EPS/WIMEK/WASS/VLAG/WIAS/RSEE PhD candidates with approved TSP and WU EngD candidates

€250,-

PE&RC postdocs and staff

€500,-

All other academic participants

€540,-

Non-academic participants

€1040,-

1 The course fee includes lunch, course materials, coffee/tea, and water.. 
 

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