PSYCHOLOGY | Workshop I: Network Psychometrics: Foundations, Theory, and Cross-sectional Data Analysis
Note: this workshop is one of two that aim to provide expert guidance for researchers and practitioners interested in learning the basics of network modeling for social science, and count toward the Professional Certificate (PC) in Network Psychometrics for Behavioral and Social Scientists.
Course Synopsis
This workshop will focus on both theoretical foundations of network psychometrics and modeling techniques developed from this perspective. It will provide the programming background and skills necessary to carry out independent data analyses, as well as the central concepts in the estimation of psychometric network models. The workshop will consist of plenary lectures (2h), followed by seminar-style computer practicals (1.5h).
Learning Outcomes
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
- Explain 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
For more information or to apply, click here.