The course will cover the entire empirical research workflow:
- Philosophy of empirical science,
- stages of empirical research,
- types of studies (experimental, quasi-experimental, cross-sectional, cohort, case-control, meta-studies),
- planning empirical research (translating a practical question into an actionable statistical question, identifying relevant variables, proxy variables and operationalization),
- sampling (simple random sampling, cluster sampling, stratified sampling, non-probability sampling methods),
- human-centric research instruments (self-reporting, observer-reporting, designing questionnaires, reliability and validity),
- standard data analysis application and interpretation (maximum likelihood, Bayesian, and null-hypothesis statistical testing, up to general mixed-effects models),
- communication (reporting, static and interactive visualization, eliciting problems from and communicating results to non-experts).
The course is a combination of lectures explaining the fundamental principles and labs where students apply these principles in real-world scenarios (selecting the best machine learning model, evaluating the quality of a university course, grading students, measuring athletes, providing the statistical methodology for an empirical research paper).
