Skip to Main Content

CBS PhD School logo

Computational Research Methods - 3.5 ECTS Postponed

Date and time

Monday 15 April 2024 at 09:00 to Monday 6 May 2024 at 12:00

Registration Deadline

Monday 8 April 2024 at 23:55


Kilen - room KL1.46 (first floor), Kilevej 14A, 2000 Frederiksberg Kilen - room KL1.46 (first floor)
Kilevej 14A
2000 Frederiksberg

Computational Research Methods - 3.5 ECTS Postponed

Course Coordinators: Michel Avital and Jason Burton, Department of Digitalization (DIGI)




Assistant Professor Jason Burton 

Department of Digitalization 



Aims and Objectives

This course is designed for doctoral students who are interested in applying computational research methods for social science research. The overarching objectives of the course are to (1) familiarize students with key concepts in the field of computational social science and (2) equip them with practical knowledge of computational research methods to apply to their own research interests. Special focus is given to the collection and analysis of digital trace data and agent-based modeling and simulation.


Learning Objectives

At the end of the course, students should be able to:

  • Critically discuss the emergence of computational social science as a field of research;
  • Reflect on the opportunities and challenges of applying computational research methods to their own domain of interest, and social science at large;
  • Collect digital trace data via APIs and web scraping;
  • Statistically analyze digital trace data and interpret results;
  • Build agent-based models of social systems and run simulations;
  • Develop and present a proof-of-concept study applying computational research methods to their own domain of interest.

Structure and Format

Three weekly full-day sessions plus one half-day session to wrap up the course. Each full-day session consists of lectures, group discussions, and hands-on exercises with the R programming language.



Course Project


The course project is designed to assess each participant's understanding of the topics covered in class. The course project requires each student to develop a research study that applies one or more of the computational research methods covered. The course project report should be up to ten pages and must include:

·       A well-defined and motivated research question (ideally oriented in their ongoing doctoral work);

·       Justified selection of one or more computational research methods;

·       An overview of potential results and research impact;

·       A proof-of-concept analysis (e.g., descriptive analysis of a newly collected digital trace dataset; a visualization of preliminary agent-based simulations).


On the last day of the course, students will deliver a short oral presentation of their project and receive feedback from the teacher and their peers.



A Pass/Fail grade will be based on the timely submission of a 10-page course project paper and the quality of the oral presentation in the last session. A retake exam, if necessary, will be administered about a month following the ordinary exam.


Prerequisite Statistical Software

This course will use the R programming language and the RStudio IDE. Before the first class session, students should download R and RStudio here: Students with no prior experience using are strongly encouraged to complete an introductory tutorial before starting the course (e.g., sections 1-9 of R for Data Science, and/or the first six of these Posit Primers).



This course is research-based, and the required readings primarily consist of academic articles. See the course plan below for assigned readings for each session. Students should complete the readings before each session and be prepared to discuss and answer questions pertaining to the reading material.



Pre-class preparation

54 Hours

Class sessions

21 Hours

Project and presentation preparation

23 Hours




98 Hours

1 ECTS = 28 hrs



Course Plan






15 April


Introduction to Computational Social Science


22 April


Analyzing digital trace data


29 April


Agent-based modeling and simulation


06 May


Integration and project presentations



Session 1: Introduction to Computational Social Science


Overview of CSS as a field: motivation, methods, applications, ethics.



·       Basic web scraping

·       Collecting data from APIs


Required reading:

·       Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., ... & Van Alstyne, M. (2009). Computational social science. Science323(5915), 721-723. [Link]

·       Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788. [Link]


Session 2: Analyzing digital trace data


Recognize key characteristics of digital trace data (e.g., big; nonreactive; incomplete; confounded) and the opportunities and challenges they pose to social science research. Understand different methodological use cases (e.g., prediction vs. explanation) and real-world applications.



·       Basic text analysis

·       Descriptive social network analysis


Required reading:

·       Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature595(7866), 189-196. [Link]

·       Tufekci, Z. (2014). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In Proceedings of the international AAAI conference on web and social media (Vol. 8, No. 1, pp. 505-514). [Link]


Session 3: Agent-based modeling and simulation


Understand what an agent-based model is and what makes a "good" model. Appreciate seminal models, survey real-world applications, and learn how to critique simulation results.



·       Program an agent-based model of opinion dynamics, run simulations, and interpret results.


Required reading:
  • Epstein, J. M. (1999). Agent‐based computational models and generative social science. Complexity4(5), 41-60. [Link]
  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America, 99(suppl. 3), 7280–7287. [Link]
  • Smaldino, P. E. (2017). Models are stupid, and we need more of them. In R. R. Vallacher, S. J. Read, & A. Nowak (Eds.), Computational social psychology (pp. 311–331). Routledge. [Link]


Session 4: Integration and project presentations


In this session, we will wrap up the course material and provide each student with an opportunity to deliver an oral presentation of their course project.



Registration deadline and conditions

The registration deadline is 1 March 2024. If you want to cancel your registration on the course it should be done prior to this mentioned date. By this date we determine whether we have enough registrations to run the course, or who should be offered a seat if we have received too many registrations.

If there are more seats available on the course we leave the registration open by setting a new regsitration deadline in order to fill remaining seats. Once you have received our acceptance/welcome letter to join the course, your registration is binding and we do not refund your course fee. The binding registration date will be the registration deadline mentioned above.

Payment methods
Make sure you choose the correct method of payment upon finalizing your registration:
CBS students:
Choose payment method CBS PhD students and the course fee will be deducted from your PhD course budget.
Students from other Danish universities: 
Choose payment method Danish Electronic Invoice (EAN). Fill in your EAN number, attention and possible purchase (project) order number.
Do you not pay by EAN number please choose Invoice to pay via electronic bank payment (+71). 
Students from foreign universities:
Choose payment method Payment Card. Are you not able to pay by credit card please choose Invoice International to pay via bank transfer. 

Event Location

Click to view the event location on Google Maps >

Organizer Contact Information

CBS PhD School
Nina Iversen

Phone: +45 3815 2475

Organizer Contact Information

CBS PhD School
Nina Iversen

Phone: +45 3815 2475