Course map
Alignment with Research Project Design
The teaching package follows the quantitative R components of the course while keeping the workload suitable for beginners.
| Lab | Course focus | Repository lessons | Main dataset |
|---|---|---|---|
| Lab 1 | Introduction to R | First steps | Employee survey |
| Lab 2 | Data cleaning, descriptive statistics, t-tests | Clean and describe; correlations and t-tests | Employee survey; training experiment |
| Lab 3 | Reliability, EFA, t-test, chi-square | Reliability and scales; EFA and chi-square | Employee survey |
| Lab 4 | Marketing-mix regression and diagnostics | Regression and diagnostics | Marketing mix |
| Lab 5 | Moderation, mediation, replication | Moderation and mediation; replication and reporting | Employee survey; replication sample |
| Optional extension | Logistic regression and managerial interpretation | Logistic regression | Hotel upgrades |
Recommended lab structure
A 90-minute session can follow this sequence:
| Time | Activity |
|---|---|
| 0–10 min | Research question and method choice |
| 10–25 min | Instructor live coding |
| 25–45 min | Guided student coding |
| 45–60 min | Interpretation discussion |
| 60–78 min | Short independent exercise |
| 78–90 min | Debrief, common errors, takeaway |
What is intentionally excluded
To protect beginner cognitive load, the core pathway does not teach:
- loops and functional programming;
- writing custom R packages;
- advanced Git commands;
- machine learning;
- multilevel or longitudinal models;
- structural equation modelling;
- advanced causal inference;
- highly customized graphics.
These can be learned later after students can confidently manage a basic research workflow.