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POL50070

Academic Year 2024/2025

Quantitative Methods I (CORE) (POL50070)

Subject:
Politics
College:
Social Sciences & Law
School:
Politics & Int Relations
Level:
5 (Doctoral)
Credits:
10
Module Coordinator:
Dr Yoo Sun Jung
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Exclusively for SPIRe PhD students, this foundational module initiates the quantitative methods sequence. It covers vital statistical analyses, from data visualization to multiple regression. Emphasizing the linear regression model, students learn applied statistical analysis with real social science data.

Practical skills in R programming for political science research are imparted. Addressing assumptions, estimation, and inference in linear regression, students enhance analytical robustness.

About this Module

Learning Outcomes:

Upon course completion, students will be well-prepared to:

- Master fundamental quantitative methods, from basic analysis to advanced regression, effectively conveying numerical data.
- Evaluate published research critically, applying linear models skillfully.
- Translate concepts from class into programming skills in R and LaTeX, aligning with industry and academia norms.

Indicative Module Content:

The curriculum will cover these key areas:
• Exploring and manipulating data
• Linear regression analysis
• Logit and Probit models
• Model specification and diagnostics
• Dummy variable and interactions

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

200
Lectures

15
Computer Aided Lab

12
Total

227

Approaches to Teaching and Learning:
Each week's sessions comprise lectures and labs. The lectures cover the fundamental aspects of statistical inference, and will make use of small group exercises to encourage students to work directly with example material. The homework assignments are strategically designed to progressively culminate in a comprehensive regression analysis, effectively applying the technical concepts covered in the course.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Throughout the Trimester, students will be tasked with completing three to four homework assignments centered on various data analysis tools. Week 4, Week 8, Week 11 Graded No
40
 No
Exam (In-person): Midterm examination Week 7 Alternative linear conversion grade scale 40% No
30
 No
Exam (In-person): Final examination Week 12 Alternative linear conversion grade scale 40% No
30
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Feedback will be provided within 20 days from submission, as per university guidelines.

– Kosuke Imai. 2017. Quantitative Social Science: An Introduction. Princeton: Princeton University Press.
– Kellstedt, Paul, and Guy Whitten. 2018. The Fundamentals of Political Science Research, 3rd Edition.
- Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables, Volume 7.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Practical Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 16:00 - 17:50
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