Quantitative Methods

The details
Colchester Campus
Postgraduate: Level 7
Thursday 03 October 2024
Friday 13 December 2024
13 April 2023


Requisites for this module



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 quantitative methods essential to test hypotheses in political science. After introducing the statistical computing environment R and associated document preparation software tools as well as bivariate hypothesis testing, linear regression using ordinary least squares estimation is covered in depth as the workhorse model for statistical inference in political science. The second half of the module will cover extensions for temporal and multilevel data and introduce methods for causal inference.

All models and methods are approached substantively, mathematically, and computationally (using R), with applications to political science research questions. Throughout the module, students will also familiarise themselves with the interpretation and presentation of empirical evidence in political science. 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.

Module aims

The module will enable students to:

  • understand and apply the logic of hypothesis testing in a variety of political science contexts.

  • understand and interpret statistical analyses in published political science research.

  • master the mathematics behind ordinary least squares and related regression models.

  • translate theories into empirical models.

  • conduct their own basic regression analyses using empirical datasets, both manually and with software, commensurate with analyses published in political science journals.

  • assess the goodness of fit of empirical models.

  • estimate causal effects of treatment variables on outcomes.

  • effectively present quantitative results using R and modern document formats embedded in statistical software.

Module learning outcomes

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

  1. formulate theories in ways that are amenable to single and multiple hypothesis testing and be able to diagnose violations of basic assumptions.

  2. understand, and be able to improve upon, statistical analyses and their interpretations in political science journals.

  3. have practical experience with conducting high-quality quantitative political science research as well as with the implementation of basic regression models, both using ready-made functions/packages in R and manually/from scratch.

  4. master the mathematics and statistical theory underlying hypothesis testing, ordinary least squares, time series analysis, panel and multilevel models, and causal inference techniques.

  5. know how to handle complex data structures and implement appropriate models, including temporal and hierarchical dependence.

  6. confidently apply causal inference techniques to estimate treatment effects.

  7. present statistical results effectively.

Module information

Indicative contents:

  1. Introduction to Quantitative Methods and R

  2. Hypothesis testing

  3. Linear regression I: Linear regression with one/two regressors

  4. Linear regression II: Ordinary least squares with multiple regressors

  5. Linear regression III: Assumptions of linear regression

  6. Methods for panel and multilevel data

  7. Time series analysis

  8. Causal inference I: Experimental design

  9. Causal inference II: Difference in difference and regression discontinuity

  10. Causal inference III: Matching, instrumental variables, and synthetic controls

Learning and teaching methods

The module will be delivered via:

1hr lecture + 1hr class (PC lab)



Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Data Analysis and interpretation exercise 1    30% 
Coursework   Data analysis and interpretation exercise 2    35% 
Coursework   Data analysis and interpretation exercise 3    35% 

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%


Coursework Exam
100% 0%
Module supervisor and teaching staff



External examiner

No external examiner information available for this module.
Available via Moodle
No lecture recording information available for this module.


Further information

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

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