Learning Outcomes:
At the end of this course, students will be able to:
--- Use and interpret interaction effects in the linear model;
--- Apply generalizations of the linear model to limited dependent variables (categorical and count data) and interpret the results;
--- Explain and (where possible) test assumptions of causal inference techniques and use these with real data;
--- Select, run, and interpret a fairly complex regression model to answer a practical research question.
Indicative Module Content:
- Interaction effects in the linear model
- Logit models for binary and polytomous dependent variables
- Simulation
- Count data models
- Causal inference from observational data
- Multilevel data modeling