Structural equation modelling

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Introduction

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.
 

Course information

ECTS: 2.5 
Number of sessions: 4
Hours per session: 3

Key Facts & Figures

Type
Course
Instruction language
English
Mode of instruction
Offline

What will you achieve?

  • After this course you will understand for what purposes SEM can be used.
  • After this course you will understand the role of confirmatory factory analysis in SEM.
  • After this course you will know how to use SEM for mediation and moderation.
  • After this course you will know how to use SEM for longitudinal data.
  • After this course you will understand how SEM can be applied in R.

Start dates

Session 1
March 5 (Wednesday) 2025
13:30-16:30 hrs
Mandeville building (campus map), room T19-01

Session 2
March 12 (Wednesday) 2025
13:30-16:30 hrs
Mandeville building (campus map), room T19-01

Session 3
March 19 (Wednesday) 2025
13:30-16:30 hrs
Mandeville building (campus map), room T19-01

Session 4
March 26 (Wednesday) 2025
13:30-16:30 hrs
Mandeville building (campus map), room T19-01

Aims and working method

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.

Entry level

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.

Session descriptions

  • 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.

  • 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.

  • 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.

  • 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.

Instructor

  • Marleen de Moor
    Marleen de Moor is an Associate Professor at the EUR Department of Psychology, Education and Child Studies, where she gives courses in research methodology and statistics. In her research she develops and applies advanced data analysis techniques such as Multilevel analysis, Structural Equation Modelling, Factor Analysis and Time Series Analysis.
    Email address

Contact

Facts & Figures

Fee
  • free for PhD candidates of the Graduate School
  • €575,- for non-members
  • consult our enrolment policy for more information
Tax
Not applicable
Offered by
Erasmus Graduate School of Social Sciences and the Humanities
Course type
Course
Instruction language
English
Mode of instruction
Offline

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