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Latent Variable Models


Entry Requirements

Only open to Master’s and Research Master’s students from Psychology.

Students who have not attended the “Introduction in R and Statistical Computing” course should spend a few hours, before the course starts, by: 1) Studying and working through the exam.


Psychological characteristics cannot be measured directly. They are latent variables, which can only be measured indirectly through, for example, tests or questionnaires. Responses to test or questionnaire items are then used as a measure, or indicator, of the psychological characteristic (construct) of interest. With latent variable models (LVMs) we can assess how well these latent variables are measured, how they change over time and/or how they are associated with other variables. LVMs are therefore an important tool for testing models and hypotheses in psychological research, for assessing the quality of psychological tests, or for interpreting the results of psychological tests.

Two widely known frameworks for latent variable modeling are Structural Equation Modeling (SEM) and Item Response Theory (IRT). This course offers a theoretical and practical introduction to SEM and IRT. A range of models and applications will be discussed, including path analysis, confirmatory factor analysis, IRT modeling, hierarchical latent variable models, measurement invariance (‘differential item functioning’) and latent growth curve modeling.

Course objectives

This course focuses on translating substantial theories into latent variable models. Students acquire basic skills in using R and the lavaan package for SEM: they are taught how to perform latent variable analyses and how to interpret the results. Applications from psychology will be used to illustrate the methods.

Students will acquire basic knowledge of :

  • Distinction between structural and measurement parts of a SEM

  • Identification of the scale of latent variables in LVMs

  • Differences and similarities between IRT, CFA and classical test theory

  • Estimation, calculation and interpretation of path coefficients

Students will acquire basic skills in:

  • Fitting and interpreting path models

  • Fitting and interpreting IRT models

  • Fitting and interpreting basic and hierarchical CFA models

  • Fitting and interpreting models with multiple groups or measurement occasions


For the timetables of your lectures, work groups and exams, please select your study programme in: Psychology timetables




Students need to enroll for lectures and work group sessions. Master’s course registration


Students are not automatically enrolled for the examination. They can register via uSis from 100 to 10 calendar dates before the date. Students who are not registered will not be permitted to take the examination. Registering for exams

Mode of instruction

There will be seven four-hour lab sessions, in which theory (lectures) will be combined with practice (computer assignments). Attendance at at least 5 lab sessions is required. We will be using the R and the R-package ‘lavaan’ (short for LAtent VAriable ANalysis), so bring your laptop to the course with R installed.

Assessment method

Two structured written assignments (each 25%), and one unstructured written assignment (50%). The structured assignments consist of LVM analyses that have to be performed and interpreted, according to instructions. The unstructured assignment consists of designing, performing, reporting and interpreting an advanced LVM analysis on a dataset of a student’s own choosing, or supplied by the lecturer.

Reading list

  • 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.

Contact information

Dr. Marjolein Fokkema