This page is a short overview of what we cover. For the official week-by-week schedule, see Syllabus.

Topic blocks (high level)

1. Weeks 1–2: Foundations & reproducibility
   • review of inference logic and classical tests
   • effect sizes and confidence intervals (“new statistics”)
   • reproducible workflows (Quarto, project structure)
   • simulation fundamentals
2. Weeks 3–4: GLM revisited
   • regression as the universal engine
   • ANOVA as regression
   • categorical predictors, coding/contrasts, interactions
3. Weeks 5–6: Diagnostics & robustness
   • assumptions, residual checks, influence (Cook’s distance)
   • transformations (log, Box-Cox) and bootstrapping
4. Weeks 7–8: Generalized linear models
   • link functions
   • logistic regression and interpretation
   • Poisson regression for counts (and overdispersion basics)
5. Weeks 9–12: Mixed-effects models
   • random intercepts/slopes, nested vs crossed designs
   • practical workflow and convergence issues
   • GLMM for accuracy + reporting + emmeans
6. Week 13: Dimension reduction & SEM
   • PCA/EFA → CFA and basic SEM (lavaan)
7. Week 14: Meta-analysis & synthesis
   • effect sizes from papers, forest plots, publication bias, QRPs
8. Week 15: Final project workshop
   • student presentations

Readings

Readings are listed on each week page (see the Readings item in the relevant week section). PDFs provided by the course are in Materials for Participants.