Module 7: Structural Equation Modelling II

Dates - Venue - Description - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teacher - Course material - Fees - Enrol


2 full days in April 2018: Wednesday April 11 and Thursday April 12, 2018, from 9 am to 12 pm and from 1 pm to 4 pm.
Please note: the deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Recognition) is March 9, 2018.


Faculty of Social and Behavioural Sciences, Site Dunant, Dunantlaan 2, Ghent.


Hierarchically clustered (multilevel or nested) data are common in most scientific fields, including the medical, biological and social sciences. For example, individuals may be nested within geographical areas, institutions, or companies, the canonical example being students nested within schools. Multilevel data also arise in longitudinal studies where one or several outcomes are measured on several occasions. Another feature of multilevel data is that variables can be measured at any level. For example, we may have collected measures of student outcomes and student characteristics, but we may also have collected variables at the school level.

This course starts with a refresher of multilevel modeling (MLM). We will discuss key concepts of MLM, introduce the linear mixed model, and provide several examples of univariate multilevel regression analysis. All analyses will be done in R, using a variety of packages (nlme, lme4, lavaan). Next, we will discuss the relationship between classic (single-level) regression, multilevel regression, and structural equation modeling (SEM). We will do this both from a theoretical point of view as well as from a software point of view. We will show how and under which conditions (classic, non-multilevel) SEM software can produce identical results as dedicated multilevel (or mixed modeling) software.

On the second day, we will introduce the multilevel SEM framework. We will start from a regression perspective, and gradually proceed from a simple regression analysis, to a two-level regression analysis, towards more complicated (regression) models, exploiting the full power of the multilevel SEM framework. Special attention will be given to multilevel mediation models, and the difference between the latent and manifest covariate approach to represent observed exogenous covariates at the between level. Next, we will take a latent-variable (CFA) perspective, and discuss various examples of multilevel CFA, and eventually multilevel SEM involving latent variables and regressions among latent variables. Here, special attention will be given to the interpretation of the latent variables at both the within and between level, together with a typology of possible approaches. Along the way, we will discuss many practical issues including the role of centering, the treatment of missing and/or non-normal data, and how to deal with categorical data. Finally, we will discuss some alternative approaches to handle clustering in the data in a SEM framework, including the design-based (survey) approach, and the 'wide format' approach.

The main software used in this course is the open-source R package `lavaan' (see

Target audience

This course targets everyone who has had some exposure to either multilevel modeling and/or structural equation modeling, and who wants to deepen their understanding of both the theoretical and practical connection between the two frameworks. The course also targets everyone who wants to better understand the new multilevel SEM framework available in lavaan.


Participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued to participants with a university degree at the bachelor level or an equivalent degree.
Please note: For UGent PhD students it is no longer necessary to succeed in this exam to be able to incorporate the course in the DTP.

Incorporation in DTP and reimbursement from DS for UGent PhD students

As a UGent PhD student, this course can only be included in your Doctoral Training Program (DTP) if followed together with Module 6 'Structural Equation Modelling I'. To get a reimbursement of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Recognition) for this course is March 9, 2018.

Course prerequisites

Participants should have a solid understanding of regression analysis and basic statistics (hypothesis testing, p-values, etc.). At least some minimal knowledge of multilevel modeling and/or structural equation modeling is recommended. Because lavaan is an R package, some experience with R (reading in a dataset, fitting a regression model) is recommended, but not required.


Prof. dr. Yves Rosseel obtained his PhD from Ghent University, Belgium. He is now an associate professor at the Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University. His research interests include computational statistics, connectivity and causality, and structural equation modeling. He is the author of lavaan, an R package for structural equation modeling.

Course material

Copies of lecture notes.


A different price applies, depending on your main type of employment.

EmploymentModule 7Exam
Industry/Private sector1 600 30
Non-profit, government, university outside AUGent2 270 30
(Doctoral)student outside AUGent2 210 30

1 If three or more employees from the same company enrol simultaneously for this course a reduction of 10% on the module price is taken into account.

2 AUGent staff and AUGent doctoral students who pay through use of an SAP internal order/invoice can participate at these special rates.

Enrol for this course