Inferential Network Analysis introduces statistical methods for analysing networks, which are sets of nodes and their connecting ties. Networks model phenomena like policy networks, lobbying coalitions, governance systems, international relations, financial deals, and policy diffusion. These systems evolve in complex ways, and researchers aim to understand tie formation and behaviour adoption within networks. Key questions include: 1) How to model datasets with non-independent observations? 2) How to explain connections or node characteristics using covariates and theories of data endogeneity? 3) How to simulate processes to predict network evolution?
The course introduces statistical models for explaining and predicting tie formation using node characteristics, ties, and the network. It covers the exponential random graph model (ERGM), its specification, estimation, and implementation in R, as well as extensions for temporal and valued relations. Alternatives like latent space models (LSM), quadratic assignment procedure (QAP), stochastic actor-oriented model (SAOM), latent-order logistic (LOLOG) model, relational event model (REM), and network autocorrelation models (NAM) are also discussed. All models are covered theoretically and practically using R, with daily assignments.
Participants will confidently choose, implement, and interpret models, assess fit, simulate networks, and diagnose issues. Familiarity with logistic regression and R is recommended. Participants should ideally bring laptops.
- Instructor: Philip Leifeld