Learning Objectives:
On successful completion of this course, students will be able to...
- explain the differences and similarities between the main types of latent variable models, as well as between latent variable models and network models
- perform a latent class analysis (LCA), which involves interpreting model selection statistics and model parameters, classifying individuals into latent classes, and relating latent classes to covariates
- apply item response theory (IRT) models, which involves interpreting goodness-of-fit statistics and model parameters, as well as determining the adequacy of the instrument for the practical purpose envisaged
- apply basic network methodology, which involves network encoding using adjacency matrices, estimating Gaussian Graphical Models, and interpreting network structures in terms of their key characteristics
- reproduce basic input for and output from software for latent class, IRT, and network modeling by hand
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This course provides an introduction to latent variable measurement and psychometric networks in the psychological and social sciences. The models to be discussed are latent class, latent profile, and latent trait (or item-response theory) models, and among others the Gaussian Graphical network model. These models are routinely applied in various fields of the social sciences. We will focus on the conceptual foundations of the models, discuss their basic form and their generalizations or special cases, and practice different applications to real data sets. The relevant software in this course includes SPSS, R, and LatentGOLD.
Type of instructions
Videos, (Q&A) Lectures, and Computer labs.
Attendance at the lab sessions is strongly advised.
To pass the exam, you should be able to interpret output from software and perform computations that we will practice during the lab sessions.
Type of exams
This course will have an open-questions exam. A student passes the course if the final grade is 5.5. or higher.
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