Course map

Designed as a practical companion

The material supports Research & Evidence: Data, Methods & Design and related management-research courses. It is intentionally usable as a short bootcamp: students can complete the core sequence without prior programming experience and use the extensions when their project requires them.

Recommended pathway

Stage	Main question	Material	Typical output
1. Start	How do I work in R without getting lost?	Modules 1–2	Imported, inspected, described data
2. Compare	Are variables related or groups different?	Module 3	Correlation/t-test with CI and effect size
3. Measure	How do survey items become constructs?	Modules 4–5	Scale evidence; PCA/EFA/CFA interpretation
4. Model	Which factors are associated with an outcome?	Module 6	Regression model and diagnostics
5. Extend	Does the design involve conditional, binary, nested, repeated, or scenario questions?	Modules 7–12	Method matched to the research structure

A realistic 90-minute lab

Time	Activity
0–10 min	Management problem and statistical intuition
10–25 min	Instructor live coding
25–45 min	Guided student coding
45–60 min	Read and interpret output
60–78 min	Small independent/pair task
78–90 min	Debrief: what can and cannot be concluded

Core versus extension

The core bootcamp is Modules 1–6.

The applied extensions are:

• moderation and mediation;
• logistic regression;
• replication and reporting;
• multilevel/nested data;
• scenario-based management analysis;
• chi-square and categorical association.

This distinction keeps the main learning path manageable while preserving a richer reference for student projects.

Intentionally not taught as core programming

The bootcamp does not require:

• loops or functional programming;
• custom functions beyond simple examples;
• package development;
• advanced Git;
• machine learning;
• structural equation modelling beyond an introductory CFA;
• advanced causal inference.

Students first learn to manage a defensible quantitative workflow.