Structural equation modelling

In the academic year 2023-2024 this course will take place offline.

Session 1
March 6 (Wednesday) 2024
13:30-16:30
Mandeville building (campus map), room T19-01

Session 2
March 13 (Wednesday) 2024
13:30-16:30
Mandeville building (campus map), room T19-01

Session 3
March 20 (Wednesday) 2024
13:30-16:30
Mandeville building (campus map), room T19-01

Session 4
March 27 (Wednesday) 2024
13:30-16:30
Mandeville building (campus map), room T19-01

Structural equation models (SEM) are used to test a theory or hypothesis about how multiple constructs are related to one another. A construct can be a cause of an outcome or the outcome itself. With SEM it’s possible to assess constructs that can be observed and measured directly, or constructs that cannot be observed and measured directly and are therefore ‘latent’.

For example, you can use SEM to test whether the relationship between higher body weight (observed variable) and more depressive symptoms (latent variable, assessed by several indicators of depression) is mediated by a more negative self-concept (latent variable, assessed by several indicators of self-concept).

The course will explain both the measurement part of the model (i.e. confirmatory factor analysis, linking indicators to latent variables) and the structural part of the model (also linking latent variables amongst each other). We will also discuss how SEM can be used to test hypotheses about mediation and moderation, and about change when longitudinal data are available.

We will discuss both the theory and practice of SEM. Participants will learn how to fit a structural equation model to the data in the software program R (package lavaan). The meetings will consist of mini-lectures and the opportunity to practice with structural equation modelling in R using both exercise data and your own data.

To attend this course properly participants should ideally have basic knowledge of the program R. If you do not have such knowledge yet, you can first follow our course on Data Analysis with R. If you doubt whether you have sufficient knowledge about R, please contact the lecturer, Marleen de Moor.

After this course participants will:

• Understand for what purposes SEM can be used;
• Understand the role of confirmatory factory analysis in SEM;
• Know how to use SEM for mediation and moderation;
• Know how to use SEM for longitudinal data;
• Understand how SEM can be applied in R.

Session 1: Introduction to Structural Equation Modelling

• Read Chapter 1 (p.1-15):  "Fundamentals of structural equation modeling" Raykov, T. & Marcoulides, G.A. (2000). A First Course in Structural Equation Modeling. Mahwah: Lawrence Erlbaum Associates, Inc., Publishers. Available online (PDF)
• Read Chapter 3 (p. 63-66):  "Path analysis" Raykov, T. & Marcoulides, G.A. (2000). A First Course in Structural Equation Modeling. Mahwah: Lawrence Erlbaum Associates, Inc., Publishers. Available online (PDF)
• Download and install the free and open source programs R and Rstudio
• Bring your laptop to class.

Session 2:  Measurement Models: Confirmatory Factor Analysis

• Read Chapter 4 (p. 94-98):  "Confirmatory Factor Analysis" Raykov, T. & Marcoulides, G.A. (2000). A First Course in Structural Equation Modeling. Mahwah: Lawrence Erlbaum Associates, Inc., Publishers. Available online (PDF)
• Bring your laptop to class.

Session 3: Structural Models: Mediation and Moderation

• Read Chapter 5 (p. 121-124):  "Structural Regression Models" Raykov, T. & Marcoulides, G.A. (2000). A First Course in Structural Equation Modeling. Mahwah: Lawrence Erlbaum Associates, Inc., Publishers. Available online (PDF)
• Prepare questions on your own research.
• Bring your laptop to class.

Session 4: Structural Equation Models for Longitudinal Data

• Read Chapter 6 (p. 147-153):  "Latent Change Models" Raykov, T. & Marcoulides, G.A. (2000). A First Course in Structural Equation Modeling. Mahwah: Lawrence Erlbaum Associates, Inc., Publishers. Available online (PDF)
• Before class, send in question about your own research. You will receive personal feedback during class.
• Bring your laptop to class.