Open to Master’s and Research Master’s students from Psychology only.
Students who have not attended the course “Introduction in R and Statistical Computing” should spend a few hours, before the course starts, by: 1) Studying and working through the examples and exercises of the first chapter of Beaujean’s book. 2) Going through the examples in the introduction to R on http://data.princeton.edu/R/default.html.
Psychological characteristics cannot be measured directly, like temperature or distances. They are latent variables, which can only be measured indirectly through, for example, items of tests or questionnaires. These item responses are used as a measure, or indicator, of the psychological characteristic (construct) of interest. Using latent variable models, we can assess how well these latent variables are measured, how they change over time, and/or how they are associated with other (latent or directly observed) variables.
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 models. Several latent variable modeling applications will be discussed, including path analysis, confirmatory factor analysis, IRT modeling, measurement invariance (‘differential item functioning’) and latent growth curve modeling.
This course provides students with basic skills in and knowledge about LVMs, which are indispensable for researchers in psychological and statistical sciences. The course provides students with skills and knowledge about advanced techniques for quantifying reliability and validity of psychological measures, which are indispensable for any career involving psychological assessments (e.g., clinical psychology, human resource management).
This course focuses on translating substantial theories into latent variable models. Students acquire basic skills in using R and package lavaan (short for latent variable analysis), with which 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 and skills in:
• Translating substantial theories into SEMs and LVMs
• The distinction between structural and measurement parts of a SEM
• Identification of the scale of latent variables in SEM and IRT
• Evaluating the fit of SEMs and LVMs
• Fitting and interpreting CFA and IRT models
• Fitting and interpreting LVMs with multiple groups and multiple time points
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 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
This course consists of:
7 4-hour lab sessions.
Lectures will be combined with computer assignments in these sessions. They will cover both latent variable modeling theory, as well as practical applications and computing in R. We will be using the R-package lavaan.
*Please bring your laptop to the course with R and lavaan installed!
- Two structured assignments (each 25%);
- One unstructured assignment (50%).
The unstructured assignment consists of performing and reporting on an advanced analysis involving latent variable(s), on a dataset of a student’s own choosing, or supplied by the lecturer.
• Required: 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