Quantitative Methods in International Business Research

HEC Montréal - MATH60033A

Quantitative Methods in IB Research with R

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.


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

Course schedule

Session 1 Session 2 Session 3 Session 4 Session 5 Session 6
Monday, January 11 Monday, January 18 Monday, January 25 Monday, February 01 Monday, February 08 Monday, February 15
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 22 Monday, March 8 Monday, March 15 Monday, March 22 Monday, March 29 Monday, April 12
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 schedule

Lab 1 Lab 2 Lab 3 Lab 4 Lab 5
Wednesday, January 13 Wednesday, January 20 Wednesday, January 27 Wednesday, February 03 Wednesday, February 10
16:30 PM - 19:30 PM 16:30 PM - 19:30 PM 16:30 PM - 19:30 PM 16:30 PM - 19:30 PM 16:30 PM - 19:30 PM

Assessment schedule

Office hours

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.