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Quantitative Research Skills


Admission requirements

To participate in Quantitative Research skills, students need to have completed at least 48 EC of the first year MSc courses.
For this course, we assume a basic knowledge and understanding of statistical methods and theory. Such knowledge and understanding is assumed to be captured by the general prerequisite of the MSc Governance of Sustainability programme of 8EC and quantitative skills. An elaboration of the requirements regarding prior (statistical) knowledge is provided in an on-line video on Brightspace. We expect all (prospective) students to have watched and acted upon the video prior to the course. It is also assumed that all students have (R and) Rstudio installed on their private devices prior to the course.


Humanity is currently experiencing the data revolution. This revolution represents a unique opportunity to make evidence-based decisions, with strengthened accountability and maximal transparency. The reality of the data revolution, and its embrace by our major governing bodies, means that future generations will need to understand more about data, statistics, and transparent reproducible research than ever before.

This course teaches the basic skills needed to harness the deluge of big data currently underway in virtually all scientific disciplines. The course consists of three components that align with the skills requirements of a modern data scientist: 1) expertise in statistics, 2) programming and 3) data visualization. These three components are geared towards understanding the possibilities and limits of data, handling big and small datasets while building transparent and reproducible analyses, and finally communicating data in an unbiased way. Based on this vision, this course will introduce key concepts in statistics, programming and visualization that are commonly used across the natural and social sciences. Furthermore,

Students will learn the statistics needed for the design, execution and evaluation of data analysis – all core components of the scientific cycle - and thus essential for performing research. Students will also be able to evaluate the statistical claims that support many actions for sustainability - an essential skill for a change maker. The latter demands not only a comprehensive understanding of the key concepts and assumptions but also experience in performing such analyses in order to critically evaluate the merits of the chosen analysis.

Today, scientists from the health, biological and social sciences have become increasingly concerned with the reproducibility of research. Crucial in this framework are reproducible science workflows that enable transparent data analyses. To be prepared for the future, students should be able to evaluate and implement these workflows.

Finally, the old idiom of “a picture is worth a thousand words” rings as true as ever. The course teaches the basics of effective communication of data by teaching the basics skills of visualization for the 21st century.

We will use real-world examples to highlight the greatest triumphs and failures of data science. We will integrate these real-world examples with theory and applied exercises that build in complexity starting from core concepts as the central limit theorem and progressing towards linear regression (ANOVAs, ANCOVAs), multiple regression and multivariate analysis. Special attention is paid to meta-analysis, as this method – in addition to the statistical methods identified above – is often used in studies on socio-ecological systems.

Course objectives

After completing this course, students will be able to:
1. Explain the fundamental statistical concepts, including its assumptions, needed for the execution of statistical analysis and for evaluating statistical claims
2. Select the best suitable statistical design for a given research question and study design
3. Run and interpret statistical analysis for a selected suite of statistical methods
4. Critically verify and judge statistical claims in literature and recognize and address common statistical pitfalls.
5. Use a modern statistical programming language and implement reproducible workflows.
6. Understand the basics of effective data visualization


You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).

For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

We will have daily interactive sessions followed by computer labs where students work in pairs. Within each of the interactive sessions, we will deal with questions and comments on pre-recorded videos. These videos target one or a few statistical concepts. We will also ask for active participation of the students to bring in their own examples suitable to the topic discussed that day. Moreover, we will discuss the homework from the previous day as well as introduce new homework.

Homework consists of hands-on assignments that students can work on in computer labs under supervision. They either analyze claims from (short) scientific papers and/or execute their own analysis and provide the appropriate interpretation, plus watching and digesting the information from videos. All analyses are done in RStudio for which essential codes for the assignments are provided in the assignment texts.

Assessment method

Assessment is based on the grades of three dedicated assignments. Each assignment weighs 1/3 of the final grade and is done in groups (4 students, 1st assignment), in pairs (2nd assignment) and individually (3rd assignment). Assignments consist of an analysis of claims from literature and/or executions of statistical analysis and its interpretation.

Reading list

BrightSpace University Leiden will be used for communications and distributing study material.


MSc Governance of Sustainability students can register for the course and exam via uSis. Other students need to contact the study advisors of the programme via

From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.


prof.dr. Peter van Bodegom

Dr. ir. Marco Visser


Weekly on Mondays from 9.15 to 17.00 h. And on Wednesday 28 September from 15.15 - 19.00h