Admission requirements
This course is obligatory for students of the MSc Governance of Sustainability.
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 are assumed to be captured by the general prerequisite of the MSc Governance of Sustainability programme of 8EC 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.
If you have no prior experience in R then we strongly advise you to complete an online course before the start of QRS in September such as those provided by Code Academy, udemy, or Rstudio (links provided on Brightspace).
Description
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 limitations 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.
Statistics
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.
Programming
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.
Visualization
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.
Content
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
Timetable
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
We will have interactive sessions followed by computer labs where students may work in pairs. Within each of the interactive sessions, we will explain new concepts and tools, deal with questions and comments on pre-recorded videos on the course "video wall". We ask for active participation of the students, which may include bringing in their own examples suitable to the topic discussed that session. Moreover, we will discuss the homework from the previous session as well as introduce new homework.
Homework consists of hands-on assignments done in Rmarkdown that students can work on in computer labs under supervision. Assignments consist of executing their own analysis and provide the appropriate interpretation, or analyzing claims from (short) scientific papers – in addition to watching and digesting the information provided. All analyses are assumed to be done in RStudio for which essential codes for the assignments are provided in the assignment texts.
Course Load
The course load is calculated to include 60 hours of lectures, and 79 hours computer labs and guided on-campus study. Students are expected to complete 29 hours of self-study which include reading suggested articles and watching the course “video wall”. A weekly breakdown of the course load and planning is available on Brightspace.
Assessment method
Assessment is based on three individual assignments completed in Rmarkdown, each contributing to 30% of the final grade. These assignments involve executing statistical analyses and interpretations on real-world questions with distinct datasets or evaluating claims from the literature.
Each assignment is evaluated using a specific rubric, and the accumulated scores determine the grades. Lecturers inspect the graded assignments and provide feedback to each student privately via email. Additionally, the assignments are discussed collectively during lectures, offering a broader understanding.
Beyond the assignments, there's a 10% grade allocated for participation. This participation grade is determined by students completing online quizzes prior to the commencement of assignment weeks. There are three assessment points throughout the semester for this participation component.
To successfully pass, students must achieve an average grade of at least 5.5 across all assignments and the participation component. It's important to note that there is an opportunity for students to resit any or all of the graded assignments in the semester following the course.
Reading list
BrightSpace University Leiden will be used for communications and distributing study material. A teams channel will be available for homework help.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Prof.dr. Peter van Bodegom and dr. Marco Visser
Remarks
Other students than MSc Governance of Sustainability that are interested in following this course need to contact the study advisors of the programme via studyadvisor-gofs@cml.leidenuniv.nl
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.