Quantitative Methods

The details
Colchester Campus
Full Year
Postgraduate: Level 7
Thursday 05 October 2023
Friday 28 June 2024
17 February 2022


Requisites for this module



Key module for

MA L24512 United States Politics

Module description

This module presents quantitative methods essential to test hypotheses.

The first part of the course focuses on hypothesis testing, hypothesis testing using least squares, and some classic violations of the Gauss-Markov conditions.
We will cover cross-sectional and longitudinal models for continuous dependent variables. This first part will also cover the basics of programming, data management, and data visualisation in the statistical computing environment R as well as the preparation of documents with statistical contents using LaTeX and knitr, but the main focus of the module is on statistical theory.

The second part of the module focuses on more advanced models ubiquitous in political science based on maximum likelihood estimation and other estimation techniques, starting with the generalised linear model and its various outcome distributions (models for binary, ordered, categorical, count, and event history data) and ending with advanced topics like inferential network analysis and topics in causal inference.

This second part will again focus mainly on statistical theory but also cover many political science applications and their implementation using R.

The models and methods are approached substantively, mathematically, and computationally. 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, maximum likelihood estimation, generalised linear models, and relation regression models and estimation techniques.
- translate theories into empirical models.
- conduct their own basic and advanced regression analyses using empirical datasets, both manually and with software, commensurate with analyses published in leading political science journals.
- assess the goodness of fit of empirical models.
- understand which statistical model to employ in a given situation and to what extent the assumptions of each candidate model are met.
- effectively present quantitative results using R, LaTeX, and knitr.

Module learning outcomes

After completing this module, students will...

- formulate theories in ways that are amenable to multivariate hypothesis testing and be able to choose an appropriate statistical model commensurate with their theory.
- understand, and be able to improve upon, statistical analyses and their interpretations in leading political science journals.
- have practical experience with conducting high-quality quantitative political science research as well as with the implementation of basic and advanced regression models, both using ready-made functions/packages in R and manually/from scratch.
- master the mathematics and statistical theory underlying hypothesis testing, ordinary least squares, maximum likelihood estimation, generalised linear models, time series analysis, panel and multilevel models, event-history analysis, and similar techniques.
- know how to handle complex data structures and implement appropriate models, including temporal, spatial, and hierarchical dependence.
- understand the assumptions underlying a variety of statistical models and be able to diagnose violations of these assumptions.
- be able to present statistical results effectively.

Module information

No additional information available.

Learning and teaching methods

This module will be taught over 2 hours per week


The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment 1    25% 
Coursework   Assignment 2    25% 
Coursework   Assignment 3    25% 
Coursework   Assignment 4    25% 

Additional coursework information

The seminar will be held face-to-face (or online if required for safety reasons).

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
Prof Philip Leifeld, email: philip.leifeld@essex.ac.uk.
Professor Philip Leifeld
Module Supervisor Professor Philip Leifeld philip.leifeld@essex.ac.uk or Module Administrator, Jamie Seakens (govpgquery@essex.ac.uk)



External examiner

Dr Damien Bol
King's College London
Senior Lecturer
Available via Moodle
Of 80 hours, 40 (50%) hours available to students:
40 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

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