The workshop starts with a conceptual introduction on why items in psychological data tend to co-occur, and what this implies about the constructs such as mental disorders, cognitive abilities, personality, and attitudes. This is followed by an introduction to social and psychological network models; an overview of the network literature in psychopathology (the field where network psychometric models have been used most over the last years); and a summary of important topics (centrality, comorbidity, early warning signals).
The main group of statistical models we learn are network models in cross-sectional data. We will use the free statistical environment R to learn the basics about (1) network estimation, (2) network inference, and (3) network accuracy and replicability. We will do so in both lectures and practicals. Please bring your laptops, make sure to have RStudio installed and running; a basic understanding of R is suggested.
The last day of the workshop will cover advanced topics and methods, such as network comparisons, modeling of different types of variables, and considerations about causality. We will largely work with freely available data I provide, but if you have your own data you would like to investigate, feel free to bring it along. Note that consecutive sections in the workshop build on each other.
Day 1, Tuesday
- Introduction to social network analysis
- Practical: Using R to plot networks and to estimate graph-theoretical measures like centrality
- Introduction to network psychometrics and network theory
Day 2, Wednesday
- Markov Random Fields: Gaussian Graphical Model, Ising Model, and regularization
- Practical: Estimating Markov Random Fields in R
- Advanced topics I
Day 3, Thursday
- Stability, robustness, and replicability of Markov Random Fields
- Practical: Estimating model stability in R, and comparing network models
- Advanced topics II
- If time: working with your own data!