Entry Requirements
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.
Description
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).
Course objectives
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 LVM
Identification of the scale of latent variables in SEM, IRT and LCA
Evaluating the fit of SEMs and LVMs
Fitting and interpreting CFA, IRT and LCA models
Fitting and interpreting LVMs with multiple groups and DIF
Timetables
For the timetable of this course please refer to MyTimetable
Registration
NOTE As of the academic year 2021-2022, you must register for all courses in uSis.
You do this twice a year: once for the courses you want to take in semester 1 and once for the courses you want to take in semester 2.
Registration for courses in the first semester is possible from early August. Registration for courses in the first semester is possible from December. The exact date on which the registration starts will be published on the website of the Student Service Center (SSC)
By registering for a course you are also automatically registered for the Brightspace module. Anyone who is not registered for a course therefore does not have access to the Brightspace module and cannot participate in the first sit of the exam of that course.
Also read the complete registration procedure
Mode of instruction
This course consists of:
7 4-hour Q&A live sessions and take home preparatory assignments.
Lectures will be combined with computer assignments in the preparatory assignments and live 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!
Assessment method
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.
Reading list
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.
Contact information
Dr. Zsuzsa Bakk z.bakk@fsw.leidenuniv.nl