Political Analysis: Introduction to OLS

The details
Colchester Campus
Undergraduate: Level 5
Thursday 03 October 2019
Saturday 14 December 2019
04 December 2019


Requisites for this module


GV205, GV300, GV508, SC208, SC385

Key module for

DIPLL20009 Politics,
BA LL12 Economics and Politics,
BA LL13 Economics and Politics (Including Placement Year),
BA LL1F Economics and Politics (Including Year Abroad),
BSC LL2F Economics and Politics,
BSC LL3F Economics and Politics (Including Year Abroad),
BSC LL4F Economics and Politics (Including Placement Year),
BA L900 International Development,
BA L901 International Development (Including Year Abroad),
BA L902 International Development (Including Placement Year),
BA L250 International Relations (Including Foundation Year),
BA L258 International Relations,
BA L259 International Relations (Including Year Abroad),
BA L260 International Relations (Including Placement Year),
BA L150 Political Economics,
BA L151 Political Economics (Including Year Abroad),
BA L152 Political Economics (Including Placement Year),
BA L200 Politics,
BA L201 Politics (Including Year Abroad),
BA L202 Politics (Including Foundation Year),
BA L203 Politics (Including Placement Year),
BA L219 Politics with Human Rights (Including Placement Year),
BA L2M9 Politics with Human Rights,
BA LFM9 Politics with Human Rights (Including Year Abroad),
BA L225 Politics and International Relations,
BA L226 Politics and International Relations (Including Year Abroad),
BA L227 Politics and International Relations (Including Placement Year),
BSC L222 Politics and International Relations,
BSC L223 Politics and International Relations (Including Year Abroad),
BSC L224 Politics and International Relations (Including Placement Year)

Module description

This module introduces students to the use of quantitative methods in political research. It builds on modules like GV110 and GV112, which are about finding relevant research designs and questions. This module is about how to answer such questions using statistical data. It will make it easier to understand the material in other modules, pave the way towards a quantitative Capstone project if that's what you opt for, help you in future postgraduate study, improve your job prospects, and make you a more informed consumer of statistics in the media and politics.

We begin with the simplest statistical ideas and methods (which will already be familiar to some students). The module then builds towards OLS regression which is the single most commonly used statistical method in political and other research. The good news is that, as the work becomes more challenging, the relevance to real research in political science will become more apparent. The module does not presuppose a strong maths background and indeed involves much less maths than you might expect. There will be some, but most is straightforward, and that's all you'll need to achieve the key learning objectives of the module. The central concepts in the module are not numbers but 'variables', which tend to be everyday things like people's beliefs, countries' GDPs, or political parties' vote shares. The aim of this module is to understand the relationship between these everyday things, and in practice it is logical rather than mathematical thinking that will be tested.

GV207 also gives students various types of practical experience. Most notably, students will become practised users of Stata, probably the most commonly used software in quantitative political science. Students are assumed to have no prior experience with Stata and will be given a full introduction to its use. You will also be introduced to a range of sources from which to access quantitative data or related information.

Module aims

Students will require, use and develop the following key skills:
• Transfer of ideas: students will be helped to follow and assess quantitative research in other modules – parties and elections, comparative politics, IR, and so on;
• Use of statistical programs to analyse data: students will learn in lab sessions how to use the statistical program Stata to analyse quantitative data;
• Improving independent learning and performance: Students will learn how they might think about and to address their own research topics in a quantitative framework;
• Communication and interaction: Both the lectures and lab sessions involve not only questions from the lecturer but also group discussions;
• Writing: Students learn how to report on and discuss results of quantitative analyses.

Module learning outcomes

By the end of the module students should achieve the following learning outcomes – that is, an ability to:
• understand what a quantitative Capstone project will look like and how to do one
• understand the right statistical method for particular research questions and variables;
• use various statistical methods, from means to multivariate regression models;
• analyse quantitative data with Stata;
• build their own datasets, and to download and use existing datasets;
• read, understand, and evaluate quantitative analyses published in the leading journals;
• embark on the more advanced quantitative training available to students at Essex.

Module information

Each week there will be a two-hour session (including a break) on Wednesdays.

Learning and teaching methods

Each week there will be a two-hour session (including a break) on Wednesdays.


This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Assignment 1   20/11/2019  50% 
Coursework   Assignment 2  18/12/2019  50% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Ms Lasma Kokina, email: lasma.kokina@essex.ac.uk.
Ms Lasma Kokina
Module Supervisor: Lasma Kokina - email: lk19903@essex.ac.uk - Module Administrator: Lewis Olley - govquery@essex.ac.uk



External examiner

Dr Mohammed Rodwan Abouharb
University College London
Available via Moodle
Of 99 hours, 9 (9.1%) hours available to students:
76 hours not recorded due to service coverage or fault;
14 hours not recorded due to opt-out by lecturer(s).


Further information

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