M7 - Identifying Latent Data Structures: Structural Equation Modelling I + II

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

Type of course

This is an on campus course, with blended learning options.
Update: Part I will be online only due to the peak in omikron infections.


Part I: Two days in February 2022: Monday February 7 and Tuesday February 8, 2022, from 9 am to 12 pm and from 1 pm to 4 pm.
Part II: Two days in May 2022: Monday May 23 and Tuesday May 24, 2022, from 9 am to 12 pm and from 1 pm to 4 pm.
Please note: UGent PhD students can only incorporate this course if they participate in both parts. The deadline for UGent PhD students who want a refund need to open a dossier on the DS website (Application for Recognition) is January 7, 2022.


Part I: Online

Part II: Faculty of Psychology and Educational Sciences, Campus Dunant, PC-lokaal 1.2 - Dunant 2, 9000 Gent.


Part I

Structural equation modeling (SEM) is a general statistical modeling technique to study the relationships among observed variables. It spans a wide range of multivariate methods including path analysis, mediation analysis, confirmatory factor analysis, growth curve modeling, and many more. Many applications of SEM can be found in the social, economic, behavioral and health sciences, but the technology is increasingly used in disciplines like biology, neuroscience and operation research. SEM is often used to test theories or hypotheses that can be represented by a path diagram. In a path diagram, observed variables are depicted by boxes, while latent variables (hypothetical constructs measured by multiple indicators) are depicted by circles. Hypothesized (possibly causal) effects among these variables are represented by single-headed arrows. If you had ever found yourself drawing a path diagram in order to get a better overview of the complex interrelations among some key variables in your data, this course is for you.

The first day of the course provides an introduction to the theory and application of structural equation modeling. On the second day, we discuss several special topics that are often needed by applied users (handling missing data, nonnormal data, categorical data, longitudinal data, etc.). Hands-on sessions are included in order to ensure that all participants are able to perform the analyses using SEM software. The software used in this course is the open-source R package `lavaan' (see http://lavaan.org).

Part II

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 http://lavaan.org).

    Target audience

    Part I

    This course targets everyone with an interest in testing theories or models that involve relationships between both observed and latent variables. The audience for this course can include both novices with little or no previous experience with SEM, as well as existing users who wish to refresh or update their theoretical and practical understanding of structural equation modeling.

    Part II

    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 who participate in both modules 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.

    The exam consists of a take home project assignment. Students are required to write a report by a set deadline.

    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 both parts (I + II) are combined. 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 January 7, 2022.

    Please note: For UGent PhD students it is no longer necessary to participate/succeed in this exam to be able to incorporate the course in the DTP.

    Course prerequisites

    Part I

    Participants should have a solid understanding of regression analysis and basic statistics (hypothesis testing, p-values, etc.). Some knowledge of exploratory factor analysis (or PCA) is recommended, but not required. Because lavaan is an R package, some experience with R (reading in a dataset, fitting a regression model) is recommended, but not required.

    Part II

    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.


    Foto Yves RosseelProf. 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.

    Employment Module 7, part I Module 7, part II Exam
    Industry/Private sector1 720 720 30
    Non-profit, government, higher education staff2 540 540 30
    (Doctoral) students, retired, unemployed2 245 245 30

    1 If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second registration.

    2 UGent staff and UGent doctoral students who pay through use of an SAP or internal transfer can participate at these special prices.

    Enrol for this course