Master’s students Methodology and Statistics, Master’s students Psychology, and Research Master Students.
Knowledge of and experience with the statistical programming environment R, by having attended the course “Programming in R”. Students who have not attended that course should spend a few hours, before the course starts, by studying and working through the examples and exercises of the first chapter of Beaujeans book. In addition, a short and quick introduction to R can be found on http://data.princeton.edu/R/default.html, which may further help students in familiarizing themselves with R.
Structural equation modeling (SEM) is a general term used to describe a family of statistical methods, designed to test a theoretical model. In SEM, the structure of the means and (co)variances of a set of observed variables is described with a set of simultaneous regression equations, allowing the user to test the theoretical model. It is assumed that the structure of means and (co)variances of observed variables in a model arises from, or can be explained by, the interrelations among a set of latent and/or observed variables. Both observed and latent variables in a SEM may be continuous and/or categorical. Some common SEMs are path analysis, confirmatory factor analysis and latent growth modeling. In this course, students will acquire a theoretical basis for understanding SEM and will learn to apply it in empirical research.
In the course, we will be using the R-package ‘lavaan’ for SEM. Please bring your laptop to the course with R installed.
This course focuses on translating substantial theories into SEMs. It offers students an introduction to the estimation of structural equations. Students acquire basic skills in using R and the lavaan package for SEM: they are taught how to perform SEM analyses and how to interpret the results. Applications presented and used in the course will come from a wide range of disciplines in psychology. Students will acquire basic knowledge of :
Structural and measurement parts of SEMs;
Identification of the scale of unmeasured (latent) variables; and
Estimation and calculation of path coefficients.
Students will acquire basic skills in:
Fitting basic and hierarchical CFA models;
Fitting SEM models with multiple groups or measurement occasions; and
Testing moderation and mediation with SEMs.
For the timetables of your lectures, work groups and exams, please select your study programme in:
Students need to enroll for lectures and work group sessions.
Master’s course registration
Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date. Students who are not registered will not be permitted to take the examination.
Registering for exams
Mode of instruction
7 or 8 sessions of 4 hours, consisting of a 2 hour lecture and a 2 hour practical (including breaks), where students work on assignments. Every week, students will be supplied with take-home exercises which will be discussed in class.
One structured assignment, and one unstructured assignment. The unstructured assignment consists of performing and reporting on a SEM analysis, on a dataset of a student’s own choosing, or supplied by the lecturer. The final grade will be the average of the grades for the two assignments.
The Faculty of Social and Behavioural Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud.
Beaujean, A. A. (2014). Latent variable modeling using R: A step by step guide. New York, NY: Routledge/Taylor and Francis.
Additional course material to be announced on Blackboard.
Dr. Marjolein Fokkema