Learning R for Management Research

A practical introduction to R and RStudio for quantitative management research

Learn the research workflow—not isolated commands

These materials help beginners turn a management research question into clean data, an appropriate statistical analysis, a careful interpretation, and a reproducible report.

Start with setup View the course map Open the GitHub repository

The learning pathway

1. Ask Translate a managerial problem into a researchable question.

2. Prepare Import, inspect, clean, and document the data.

3. Explore Use summaries and graphs to understand patterns.

4. Test Choose a method that fits the question and variables.

5. Interpret Explain magnitude, uncertainty, assumptions, and limitations.

6. Report Make the analysis transparent and reproducible.

What you will learn

By the end of the pathway, you should be able to:

  • navigate RStudio and work from an R project;
  • import and clean a CSV dataset;
  • produce descriptive statistics and clear visualizations;
  • conduct and interpret correlations and t-tests;
  • evaluate survey reliability and build multi-item scales;
  • run and interpret exploratory factor analysis and chi-square tests;
  • estimate linear and logistic regression models;
  • diagnose common regression problems;
  • test and visualize moderation;
  • conduct a simple mediation analysis with appropriate caution;
  • reproduce an analysis in a second dataset;
  • communicate results to academic and managerial audiences.

How each lesson works

Every lesson uses the same beginner-friendly structure:

  1. a concrete management question;
  2. the statistical idea in plain language;
  3. short, commented R code;
  4. interpretation of the output;
  5. a small practice task;
  6. common mistakes and a takeaway.

Teaching datasets

The core examples use four documented synthetic datasets:

  • an employee and organizational practices survey;
  • a randomized management-training exercise;
  • a weekly marketing-mix dataset;
  • a hotel-upgrade dataset for logistic regression.

A separate employee sample supports the final replication exercise.

Explore the datasets

Scope

This is an applied introduction, not a complete statistics textbook or a programming course. The materials focus on the methods used in introductory quantitative management research and on interpreting results responsibly.

Note

The examples are designed for learning. A real research project also requires theory, defensible measurement, an appropriate sampling strategy, research ethics, and a design that supports the claims being made.