POL3055 : Who and why votes for far right? Using data for political analysis
- Offered for Year: 2025/26
- Module Leader(s): Dr Sebastian Popa
- Owning School: Geography, Politics & Sociology
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 0.0 |
| European Credit Transfer System | |
Aims
Quantitative methods and data science are used across all areas of Politics and International Relations as one of the most common tools for bringing evidence and testing hypotheses. By focusing on the recent rise of far-right parties as a case study this module aims to provide students with key skills in quantitive methods and data science (i.e. coding using R). It will do so starting from a complete beginner level with no previous statistics or coding experience required. Building on recent studies examining the rise of far-right students will develop the skills to conduct an original quantitative analysis exploring some factors responsible for the success of far-right parties across different countries. Thus, students will not only gain an understanding of what lies behind the success of the far-right but will also develop useful practical and transferable data analysis skills.
More broadly the module is intended to enhance the quality of independent guided research, including in the project and dissertation modules, through ‘hands-on’ practical experience embedded within concrete examples. Beyond the academic applicability of quantitative methods and data science, the students will also gain enhanced transferable skills popular among potential employers.
Outline Of Syllabus
Topics covered may include but are not limited to the following:
* Explaining the success of far-right parties
* Univariate analysis of data
* Data visualization (winning arguments with numbers)
* Statistical inference and hypothesis testing
* Bivariate analysis of data
* Linear and logistic regressions and their assumptions.
* Theoretical and substantive interpretation of statistical analyses
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Structured Guided Learning | Lecture materials | 11 | 1:00 | 11:00 | Pre-recorded lecture materials |
| Guided Independent Study | Assessment preparation and completion | 1 | 79:00 | 79:00 | N/A |
| Scheduled Learning And Teaching Activities | Practical | 11 | 2:00 | 22:00 | PIP Computer labs |
| Structured Guided Learning | Structured research and reading activities | 11 | 8:00 | 88:00 | Guided coding tasks based on lecture content and reading in preparation for the computer labs |
| Total | 200:00 |
Teaching Rationale And Relationship
Pre-recorded lecture materials will impart basic information and background to undertake the practical skills sessions. They will introduce key concepts of quantitative research, place them within the context of epistemological debates and questions of research design and provide illustrations of applications to selected areas of quantitative study in politics research. Given the complexity of the material students might want to re-watch the lecture material multiple times, thus having access to quality bite-size recorded materials is preferable to PiP lectures.
In the practical sessions (PC lab sessions), students will have the opportunity to develop their proficiency in the use of statistical software. They will be based on the topics introduced in the lectures. During these sessions, students will have the chance to learn how to use statistical software to address and answer the research question that they will explore in their assessments.
Furthermore, they will provide the opportunity to discuss the application of quantitative methods, through reading of the work of established scholars and discussion of the students' own work from the computer labs.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Written exercise | 1 | M | 40 | Research assignment, written exercise - first half. 1500 words. |
| Written exercise | 1 | A | 60 | Research Assignment, Written exercise – Second half. 2000 words |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written Examination | 1 | M | Research assignment preparation, 500 word |
Assessment Rationale And Relationship
The assessment has been broken into two parts, due to be submitted at different times in the term so that students have the opportunity to develop their skills and receive formative feedback. Each assessment component will consist of a series of guided tasks asking students to use the techniques taught to empirically investigate a research question. The students will have the chance to work on their proposals during the Practical sessions.
In the formative assessment, students should identify the data used to test their hypotheses as well as the variables they will use early in the semester. This will ensure that they can work on their own projects during the practical sessions.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- POL3055's Timetable