Scientific Coordination
Alisa Remizova
Administrative Coordination
Noemi Hartung
Please wait...
Introduction to Structural Equation Modeling
About
Location:
Online via Zoom
Online via Zoom
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 330 €
Academics: 495 €
Commercial: 990 €
Keywords
latent variable analysis, Structural Equation Modeling, multiple regression, group comparisons, measurement invariance
Additional links
Lecturer(s): Timo Gnambs
Course description
Structural equation modeling (SEM) is a multivariate statistical technique used to analyze relationships among observed and unobserved (latent) variables. It allows researchers to test and evaluate how well a theoretical model fits observed data by modeling both direct and indirect effects between multiple variables. SEM encompasses various statistical methods like regression, factor analysis, and path analysis as special cases.
The workshop introduces basic concepts of SEM with applications from psychology and education. The first part provides a conceptual overview of SEM with an emphasis on the modeling of latent variables and introduces the statistical foundation of SEM. The second part demonstrates how to estimate multivariable SEMs with empirical data using the R package lavaan (Rossell, 2012). The third part focuses on several advanced techniques such as multi-group analysis, analysis of measurement invariance, and moderation/mediation analysis. Participants will be able to apply all topics to empirical data in various practical exercises.
Target group
You will find the course useful if:
- you want to evaluate the factor structure of multi-item measurement instruments (e.g., psychological tests, individual value inventories or attitude assessments)
- you want to examine true score effects, adjusted for measurement error
- you want to address complex, multivariate research questions involving latent variables
- you want to investigate moderation or mediation hypotheses involving latent variables
- you want to address multivariate research questions involving latent variable comparisons between groups (e.g., countries, gender or cultural groups).
Learning objectives
By the end of the course, you will:
- have an overview of the statistical basics of SEM
- be able to specify, estimate, and interpret measurement models for latent variables
- understand how to model, estimate, and interpret various SEMs such as mediation and multi-group models
- know how to specify and interpret SEMs in lavaan
Prerequisites
Software and Hardware Requirements
You will require the open source software R (https://cran.r-project.org/), preferably with a graphical user interface such as RStudio (https://posit.co/download/rstudio-desktop/), and the R package lavaan (https://cran.r-project.org/web/packages/lavaan/). Please install these prior to the workshop.
