(MSc) Master of Science
Quantitative Political Science
Current
University of Essex
University of Essex
Government
Colchester Campus
Masters
Part-time
None
MSC L20924
15/06/2023
Details
Professional accreditation
None
Admission criteria
A 2.2 degree in Political Science, International Relations, International Studies, American Studies, United States Politics, Economics, Finance or Statistics.
Essential:
Applicants must have completed an undergraduate statistics module up to the level of OLS/linear regression.
IELTS (International English Language Testing System) code
IELTS 6.5 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
Additional Notes
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Course qualifiers
A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The
specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of
Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is
optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).
None
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Additional notes
None
External examiners
External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment.
External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course.
They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards.
External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.
Programme aims
The aims of MSc Quantitative Political Science are:
- To enable students to formulate and test theories about politics by collecting and using data.
- To provide students with advanced training in a range of statistical methods with which to analyse a broad scope of political institutions, behaviour, policies, and relations that structure our world today.
- To introduce students to quantitative methods suitable for this purpose and offer a selection of substantive modules and a dissertation module in which this methodological knowledge can be applied.
Learning outcomes and learning, teaching and assessment methods
On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:
A: Knowledge and understanding
A1: Advanced knowledge of statistical methods for analysing political behaviour and institutions
A2: Understanding of how to formulate data-verifiable theories about political behaviour and institutions
A3: Critical awareness of how to test existing theories through the use of data and also of how data can be misused in political argument
A4: Understanding of how to distinguish reliable from unreliable sources of data
A5: Knowledge of how to provide data-based support for an original conjecture about political behaviour and institutions
Learning methods
A1-A4 Lectures, participation in and presentations to seminars. Instruction and guidance on the use of relevant software. Oran and written feedback on assignments.
A1 & A2 specifically in GV953 Advanced Quantitative Methods.
A3 & A5 specifically in GV987 MA Dissertation with supervision from member of staff.
For all learning outcomes, instructors will pay attention to sensitivities regarding gender, race, cultural heritage and religion and other possible signifiers of group identity. Delivery methods will be adjusted for students with special learning difficulties based on individual needs. Robust feedback mechanisms through personal contact between module supervisors and students as well as module representatives will ensure inclusivity needs will be dynamically identified as they develop.
Assessment methods
A1-A5 Through written assignments, essays and extended projects.
For all assessment methods, instructors will pay attention to sensitivities regarding gender, race, cultural heritage and religion and other possible signifiers of group identity. Assessment methods will be adjusted for students with special learning difficulties based on individual needs. Robust feedback mechanisms through personal contact between module supervisors and students as well as module representatives will ensure inclusivity needs will be dynamically identified as they develop.
B: Intellectual and cognitive skills
B1: To develop independent thinking
B2: To collect evidence in the form of data
B3: To analyse data-based evidence
B4: To code software programmes in order to analyse data
B5: To carry our independent data-driven research
Learning methods
B1-B6 Participation in and presentations to seminars, individual guidance on researching and writing essays.
B2&B6 Supervision of dissertation preparation and oral and written feedback on drafts of the dissertation.
Assessment methods
B1-B5 Written assignments and essays
B6 Through the preparation of dissertation
For all assessment methods, instructors will pay attention to sensitivities regarding gender, race, cultural heritage and religion and other possible signifiers of group identity. Assessment methods will be adjusted for students with special learning difficulties based on individual needs. Robust feedback mechanisms through personal contact between module supervisors and students as well as module representatives will ensure inclusivity needs will be dynamically identified as they develop.
C: Practical skills
C1: To read and explain complex data
C2: To use software in order to present data
C3: To use libraries and IT to access data, information & scholarly resources
C4: To present data according to accepted scholarly conventions
C5: To orally summarise complex data
Learning methods
C1-5 Participation in seminars, individual guidance for essays, supervision of dissertations, oral and written feedback on class presentations and essays.
C5 Participation in seminars and individual dissertation supervisory meetings.
For all learning outcomes, instructors will pay attention to sensitivities regarding gender, race, cultural heritage and religion and other possible signifiers of group identity. Delivery methods will be adjusted for students with special learning difficulties based on individual needs. Robust feedback mechanisms through personal contact between module supervisors and student as well as module representatives will ensure inclusivity needs will be dynamically identified as they develop.
Assessment methods
C1-5 Written assignments and essays, supervised dissertation.
C5 Especially in seminars and dissertation supervisions.
C4 Especially in essays and dissertation.
For all assessment methods, instructors will pay attention to sensitivities regarding gender, race, cultural heritage and religion and other possible signifiers of group identity. Assessment methods will be adjusted for students with special learning difficulties based on individual needs. Robust feedback mechanisms through personal contact between module supervisors and students as well as module representatives will ensure inclusivity needs will be dynamically identified as they develop.
D: Key skills
D1: Clear, focused, relevant and effective presentation of data in English
D2: Access & organise data from a variety of electronic sources and present data in a visually compelling form
D3: Apply statistical methods.
D4: To identify how data sources can be used to test theories
D5: Working with others in preparation for seminars and group work
D6: Positive response to feedback and criticism, ability to work independently
Learning methods
D1-6 Participation in and presentations to seminars, written assignments and essays, dissertation.
D5 Specifically in group-based in-class work.
D6 Specifically though the dissertation supervisions.
For all learning outcomes, instructors will pay attention to sensitivities regarding gender, race, cultural heritage and religion and other possible signifiers of group identity. Delivery methods will be adjusted for students with special learning difficulties based on individual needs. Robust feedback mechanisms through personal contact between module supervisors and student as well as module representatives will ensure inclusivity needs will be dynamically identified as they develop.
Assessment methods
D1-6 Written assignment and essays, in-class presentations, dissertation.
For all assessment methods, instructors will pay attention to sensitivities regarding gender, race, cultural heritage and religion and other possible signifiers of group identity. Assessment methods will be adjusted for students with special learning difficulties based on individual needs. Robust feedback mechanisms through personal contact between module supervisors and students as well as module representatives will ensure inclusivity needs will be dynamically identified as they develop.