Network Psychometrics: Foundations, Theory, and Cross-sectional Data Analysis (Classroom and Practical Training)
About This Course
After completing this workshop, attendees will be able to:
- Understand the theoretical foundations behind network psychometrics
- Carry out basic programming in R: create a matrix, load and inspect data, computer a variance-covariance matrix, visualize and analyze networks using the qgraph package for R
- Explaining the differences between social network analysis and network psychometrics
- Compute network metrics common in the field
- Draw an implied Markov Random Field (MRF) given a certain causal structure
- Be familiar with Gaussian Graphical Models (GGMs), Ising models, and Mixed Graphical Models
- Interpret MRFs in different ways (causal, predictive)
- Estimate unconstrained MRFs from cross-sectional data
- Understand the differences between pruning, model selection, and regularization
- Estimate MRFs using different estimation algorithms and understand which algorithm should be preferred for which setting
- Apply permutation tests to check differences between two groups
- Apply bootstrapping to assess the stability of network model parameters
What You'll Learn
Entry Requirements
There are no formal entry requirements. It would be beneficial if
participants had some familiarity with R. However, even participants
unfamiliar with this statistical software will be able to effectively
participate in the course, as we will dedicate time to learn the basics of
programming in R.