Advanced Latent Variable Modeling
Most psychological characteristics cannot be measured directly. They are latent variables, which can only be measured indirectly through, for example, items of tests or questionnaires.
- Target group
- PhD candidate
- Marjolein Fokkema (Assistant Professor) Marian Hickendorff (Assistant Professor) Julian Karch (Assistant Professor) Mathilde Verdam (Assistant Professor)
Enrollment deadline is 8 June 2019.
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.
The course Basic Latent Variable Modeling provides a theoretical and practical introduction to SEM and IRT models, including path analysis, confirmatory factor analysis, IRT and ordered categorical item response models, measurement invariance and basic latent growth curve models.
The current course Advanced Latent Variable Modeling covers more advanced LVM topics, like (non-linear) growth modeling, categorical latent variables (a.k.a. latent class analysis), hierarchical factor models (e.g., second-order or bifactor models) and advanced longitudinal latent variable models, such as cross-lagged panel models. Additional LVM topics may be covered on request by the participants.
Please, complete this survey (duration 1 minute)
Basic proficiency in R and latent variable models is a requirement: participants are advised to have taken the course ‘Introduction to R’ as well as ‘Basic Latent Variable Models’, or should otherwise make sure that their R skills and knowledge of LVM analyses are at a similar level.
Please bring your laptop to the course with R installed.
Mode of instruction
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.
- 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.