Only open to Master’s students in Psychology with specialisation Methodology and Statistics in Psychology and Research Master’s students from Psychology.
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.
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.
Three widely known frameworks for latent variable modeling are Structural Equation Modeling (SEM), Item Response Theory (IRT) and Latent Class Analysis. This course offers a theoretical and practical introduction to SEM, IRT and LCA models. Several latent variable modeling applications will be discussed, including path analysis, confirmatory factor analysis, IRT modeling, measurement invariance (‘differential item functioning’) and latent class models.
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 for performing latent variable analyses and on interpreting the results of LVMs. Applications from psychology will be used to illustrate the methods.
At the end of the course, the student can:
make the distinction between structural and measurement parts of a LVM.
identify the scale of latent variables in SEM, IRT and LCA.
can evaluate the model fit of SEMs and LVMs.
can fit and interpret CFA, IRT and LCA models and report results in a written form.
can fit and interpret LVMs with multiple groups and investigate the presence of DIF.
can translate substantial theories into SEMs and LVMs and fit the appropriate LVM models using R.
For the timetable of this course please refer to MyTimetable
Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register up to 5 days prior to the start of the course.
You must register for each exam in My Studymap at least 10 days before the exam date. You cannot take an exam without a valid registration in My Studymap. Carefully read all information about the procedures and deadlines for registering for courses and exams.
Exchange students and external guest students will be informed by the education administration about the current registration procedure.
Mode of instruction
This course consists of:
7 4-hours sessions and take home preparatory assignments.
Lectures will be combined with computer assignments in the preparatory assignments and the live sessions. Each of the 7 meetings is broken down into combining 2x45 minutes of lectures and 2x45 minutes of computer assignments and breaks in between. 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 and poLCA.
*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.
The Institute of Psychology follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. All students are required to take and pass the Scientific Integrity Test with a score of 100% in order to learn about the practice of integrity in scientific writing. Students are given access to the quiz via a module on Brightspace. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of this fraud policy.
Required: Beaujean, A.A. (2014). Latent variable modeling using R: A step by step guide. New York, NY: Routledge/Taylor and Francis.
Linzer, Drew A. and Jeffrey Lewis. 2011. "poLCA: an R Package for Polytomous Variable Latent Class Analysis." Journal of Statistical Software. 42(10): 1-29. http://www.jstatsoft.org/v42/i10
Vermunt, J.K. (2010). Latent class models. In: P. Peterson, E. Baker, B. McGaw, (eds.), International Encyclopedia of Education, Volume 7, 238-244. Oxford: Elsevier. (pdf)
Magidson, J., and Vermunt, J.K., ( 2004) Latent class models. D. Kaplan (ed.), The Sage Handbook of Quantitative Methodology for the Social Sciences, Chapter 10, 175-198. Thousand Oaks: Sage Publications. ( pdf: updated version from 2016)
Additional course material to be announced on Brightspace.
Dr. Zsuzsa Bakk email@example.com