[Course] Quantitative Methods in International Business Research

HEC Montréal - MATH60033A

Thierry Warin https://warin.ca/aboutme.html (HEC Montréal and CIRANO (Canada))https://www.hec.ca/en/profs/thierry.warin.html


This course approaches quantitative methods in business research from a Data Science perspective. We explain the intuition behind a method, we discuss how researchers apply that method to understand real life problems, and we get our hands dirty by replicating using the following Data Science pipeline: Markdown, R, Git, Zotero, etc.

The goal is to expose students to the latest way of thinking when it comes to using data. This course will equip students with the necessary foundations to be, for instance, a business analyst. It will not be sufficient in itself, but it will present the building blocks that are necessary to achieve this goal.

We will approach the domain questions with an evidence-based perspective. For instance, we will use data and models to answer some of the following questions: How to construct a variable that measures country corruption? What does global really measure? How to design and interpret a questionnaire that captures CEO’s psychological barriers to internationalize?

Quantitative methods help researchers and business people approach real business problems and understand them with rigour. The objective of this course is twofold. First, after this course students will be familiar with basic statistics language as well as some applications of advanced statistics in the field of business research, so that once they face their thesis/supervised projects they can identify which method is most appropriate to answer their research question. Second, by the end of the course students will have some basic notions of how to work within a Data Science pipeline. For this purpose, five labs are designed to help students perform simple data analyses using Markdown, R, etc.

The complementarities of this course with other M. Sc. courses - e.g., Internationalization of the firm, Stratégie concurrentielle de multinationals - are very strong, as it will allow students to better understand papers that use quantitative methods in other courses. Also, we believe this course will allow students to gain a better competitive position in the job market, as there is an increasing number of firms that demand quantitative methods skills.



First, make sure you read the important information and details [here]


During this period, students will work on our communication platform as well as our analytical platform (optional).

Session 1 Session 2 Session 3 Session 4 Session 5 Session 6
Monday, January Monday, January Monday, January Monday, February Monday, February Monday, February
8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM
Session 7 Session 8 Session 9 Session 10 Session 11 Session 12
Monday, February Monday, March Monday, March Monday, March Monday, March Monday, April
8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM 8:30 AM - 11:30 AM
Lab 1 Lab 2 Lab 3 Lab 4 Lab 5 Lab 6
Wednesday, January Wednesday, January Wednesday, January Wednesday, February Wednesday, February Wednesday, February
4:30 PM - 7:30 PM 4:30 PM - 7:30 PM 4:30 PM - 7:30 PM 4:30 PM - 7:30 PM 4:30 PM - 7:30 PM 4:30 PM - 7:30 PM

Mid-term exam: Monday, February, 8:30 AM - 11:30 AM

Final exam: Monday, April, 9:00 AM - 12:00 PM


[Deploy some information by clicking on the triangle-shaped pictogram]

Session 1. Course overview [Click here]

The first half of the class will give students an overview of the course. All parties will introduce themselves, and we will present the material and topics we will cover over the term and the evaluation criteria. The second half of the class will introduce key statistical concepts.

Session 2. Exploratory Data Analysis [Click here]

Fundamentals in statistics.

Session 3. Linear regression analysis in IB research 1/2 [Click here]

Correlation, multiple regression, model assumptions, collinearity.

Session 4. Linear regression analysis in IB research 2/2 [Click here]

Model selection based on AIC, BIC and adjusted R2, stepwise methods, generalized linear models such as logistic regression.

Session 5. Panel data analysis in IB research and other advanced considerations [Click here]

Specifities about cross-section time-series analyis.

Session 6. Mid-term exam [Click here]
Session 7. Logistic regression in IB research 1/2 [Click here]

Multinomial regression, cumulative logit regression, other generalized linear models.

Session 8. Logistic regression in IB research 2/2 [Click here]

Selection of the number of factors, factor rotation, creation of new variables, Cronbach’s alpha.

Session 9. Exploratory factor analysis [Click here]
Session 10. AI and ethics [Click here]

Data biases, model biases, human biases.

Session 11. Serious-game: Business analytics / Data Science simulation [Click here]
Session 12. Research project presentations [Click here]
Session 13. Final exam [Click here]


Lab 1. Introduction to R, Github and Markdown [Click here]

Lab #1

Lab 2. Data wrangling with R [Click here]

Lab #2

Lab 3. Data visualization [Click here]

Lab #3

Lab 4. Dashboards [Click here]

Lab #4

Lab 5. Databases [Click here]

Lab #5

Lab 6. Debugging and exam preparations [Click here]

Lab #6

Community values and Honor Code

In this course, you will have access to some of the tools we use on our data science platform in our lab http://lab.nuance-r.com. The use of these tools requires adherence to our Honour Code. This comes on top of the school’s regulations [here]

Community Values

It is essential to foster a supportive e-learning environment.

In our lab, we believe it is essential that all participants embody and uphold our community values in order to foster a supportive online learning environment where individuals can have open discussion, reflect on their thinking and learn from each other.

Our laboratory’s Honour Code

A code of honour, ethics and respect.

Our Honour Code complements the Community Values Statement and reflects the commitment of participants as members of the learning community to participate in, foster and sustain the learning model of our laboratory.


Before the session:

  1. Read this program carefully
  2. Add this website to your favorites: [syllabus]

That’s all !