Advanced Quantitative Methods

The details
Colchester Campus
Postgraduate: Level 7
Monday 15 January 2024
Friday 22 March 2024
04 May 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

Module description

This module presents advanced quantitative methods for political science based on maximum likelihood estimation (MLE), with a particular focus on the generalised linear model (GLM). After introducing the principles of MLE, models for different kinds of outcome distributions, such as binary, ordinal, categorical, count, and event history data, are considered. The module also introduces some advanced methods beyond the GLM.

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:

  • master the mathematics behind maximum likelihood estimation, generalised linear models, and related regression models and estimation techniques.

  • translate theories into empirical models.

  • conduct their own 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 and modern document formats embedded in statistical software.

Module learning outcomes

By the end of this module, students will:

  1. be able to choose and implement an appropriate statistical model commensurate with their theory and appropriate for a given dataset and research question.

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

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

  4. master the mathematics and statistical theory underlying maximum likelihood estimation, generalised linear models, event-history analysis, and similar techniques.

  5. understand the assumptions underlying a variety of statistical models and be able to diagnose violations of these assumptions.

  6. be able to present and interpret statistical results effectively.

Module information

Indicative contents:

  1. Introduction and software

  2. Distributions

  3. Maximum likelihood estimation

  4. Models for binary data

  5. The generalised linear model; non-parametric approaches

  6. Models for ordinal data

  7. Models for categorical data

  8. Models for counts and proportions

  9. Duration models

  10. Missing data and imputation

Learning and teaching methods

1hr lecture + 1hr class (PC lab)


This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment 1  19/02/2024  50% 
Coursework   Assignment 2  18/03/2024  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%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Prof Philip Leifeld, email: philip.leifeld@essex.ac.uk.



External examiner

No external examiner information available for this module.
Available via Moodle
Of 18 hours, 18 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.


Further information

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