GV953-7-SP-CO:
Advanced Quantitative Methods

The details
2024/25
Government
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 13 January 2025
Friday 21 March 2025
15
22 April 2024

 

Requisites for this module
(none)
(none)
GV903
GV900

 

(none)

Key module for

MRESL25024 International Relations,
MRESL20624 Political Economy,
MRESL20024 Political Science,
MSC L16512 Quantitative International Development,
MSC L20912 Quantitative Political Science,
MSC L209EB Quantitative Political Science,
MPOLL234 Politics and International Relations,
MPOLL235 Politics and International Relations (Including Placement Year),
MPOLL236 Politics and International Relations (Including Year Abroad)

Module description

This module presents advanced quantitative methods for political science. The course is intended as an overview of the different methods ranging from experimental designs to quasi-experimental designs. A brief introduction to the basics of Data Science and Machine Learning techniques will also be provided. Students will develop the skills to critique methods used in recent academic work and to begin to apply these methods in their own research.


The course will start by introducing causality in social sciences, potential outcomes framework, experiments, selection on observables/matching, difference in differences, and regression discontinuity design. This course goes into more detail on these topics but also briefly introduces the basics of Data Science and Machine Learning techniques.

Module aims

The aims of this module are:



  • For students to have a deeper understanding of advanced quantitative methods in political science by introducing causality in social sciences, potential outcomes framework, experiments, selection on observables/matching, difference in differences, and regression discontinuity design.

  • To provide an understanding of the basics of Data Science and Machine Learning techniques.

  • To familiarise students with the interpretation and presentation of empirical evidence in political science.

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. Choose and implement an appropriate statistical model commensurate with their theory and appropriate for a given dataset and research question, and be able to improve upon, statistical analyses and their interpretations in leading political science journals

  2. Demonstrate practical experience with conducting high-quality quantitative political science research as well as with the implementation of advanced regression models master the mathematics and statistical theory underlying potential outcomes framework, experiments, selection on observables/matching, difference in differences, regression discontinuity design, and similar methods.

  3. Understand the assumptions underlying a variety of statistical models and be able to diagnose violations of these assumptions and present and interpret statistical results effectively.

Module information

The topics covered are extensive, and we expect this course to be an introduction for students to then explore the different topics/applications on their own. 


Students on this course are required to have taken GV903 and to be familiar with probability and statistics (OLS, hypothesis testing).


The module will be particularly useful for students who aim to pursue careers in academia or in research-intensive environments, for example think tanks, research-related government posts, data science, or survey analytics.


Indicative contents:



  • Introduction

  • Introduction to Data Management and Collection 

  • Introduction to Machine Learning

  • Potential Outcomes Framework

  • Experiments

  • Selection on Observables

  • Multiple Regression

  • Matching

  • Regression Discontinuity Designs

  • Fuzzy Regression Discontinuity Design

  • Panel Data

  • Differences-In-Differences using cross-section Differences-In-Differences using panel data

Learning and teaching methods

This module will be delivered via:

  • One 2-hour lecture and class each week.

Bibliography*

(none)

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment 1    50% 
Coursework   Assignment 2    50% 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Nelson Ruiz, email: nelson.ruiz@essex.ac.uk.
Nelson Ruiz
Please contact govpgquery@essex.ac.uk

 

Availability
No
No
No

External examiner

Dr Kyriaki Nanou
Durham University
Associate Professor in European politics
Resources
Available via Moodle
No lecture recording information available for this module.

 

Further information
Government

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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