Latent Variable Models (Basic)
This course focuses on translating substantial theories into LVMs. Participants acquire basic skills in fitting LVM models using the R package lavaan (short for LAtent VAriable ANalysis).
- Target group
- PhD candidate
- Marjolein Fokkema (Assistant Professor) Marian Hickendorff (Assistant Professor) Julian Karch (Assistant Professor) Mathilde Verdam (Assistant Professor)
Lectures will be combined with computer assignments. The lectures will cover LVM theory as well as practical applications. We will focus on how to perform LVM analyses and how to interpret the results. Applications from psychology will be used to illustrate the methods.
- Study material
- 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.
Most psychological characteristics cannot be measured directly, unlike directly measureable variables such as temperature or distance. 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 variables.
Two widely known frameworks for latent variable modeling are Structural Equation Modeling (SEM) and Item Response Theory (IRT). Traditionally, IRT models were often used for analysis of dichotomous and (ordered) categorical item responses. SEM models were traditionally developed for analyses of continuous variables, and include techniques such as path analysis, confirmatory factor analysis and latent growth modeling.
This course offers a theoretical and practical introduction to SEM and IRT models. Several LVM model types will be discussed, including:
- path analysis,
- confirmatory factor analysis,
- IRT and ordered categorical item response models,
- measurement invariance (a.k.a. differential item functioning), and
- basic latent growth curve models.
Basic proficiency in R is a requirement: participants are advised to have taken the course ‘Introduction to R’, or should otherwise make sure that their R programming skills are at a similar level. Please bring your laptop to the course with R and lavaan installed.