2024/25 Taught Postgraduate Module Catalogue

PIED5737M Elections and Voters

30 creditsClass Size: 30

Module manager: Professor Jocelyn Evans
Email: J.A.J.Evans@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

In this module, we explore the different theories used in psephology (the study of voting behaviour) and their application in modelling individual voter choice, including the principal sociological, psychological and economic models of voting. Taking into account contextual influences on voting – for example, campaigns, electoral systems and party systems – we look to construct the so-called ‘full model’ of voting, applicable to any competitive electoral race. We focus particularly on contemporary examples of voting, including ‘unique’ examples, such as Brexit and Trump, as well as other archetypal post-war elections. By the end of the module, you will be able to build simple but powerful explanatory models of voting, and understand how to apply these using a range of electoral and socio-economic data.

Objectives

The aim of this module is to provide an advanced introduction to the main theories and models used in psephology (the study of voting). In order to achieve this it aims to: (1) Consider the competing explanations of how voters make their choices in elections, looking at the individual and contextual influences which may have a bearing on their party support. In particular, it focuses on how voters process information and prioritise ideological and pragmatic considerations in their decision whether and how to vote; (2) Explain and discuss contemporary voting developments (polarization, disengagement, populism) and to examine these through the lens of traditional voting theories which still offer strong analytical value for modern electoral phenomena; (3) To provide an opportunity for students to engage with the modelling of voting behaviour (i.e. the application of theory), and demonstrate how rigorous empirical analysis of voting is necessary to understanding voters’ motivations, and thereby election outcomes.

Learning outcomes
On successful completion of the module students will be able to:
1. Recognise and critically discuss the principal theories of voting behaviour including the mechanical effects of electoral laws, and voters’ interactions with these.
2. Conceptualise simple empirical tests to voting data to demonstrate causal relationships in voting; and apply electoral theories to country-cases and comparatively
3. Construct a range of testable hypotheses as part of the ‘full model’ of voting.
4. Explain the findings of more sophisticated statistical models of voting found in the secondary literature.

Skills Learning Outcomes
On successful completion of the module students will be able to:
1. Identify and analyse relevant empirical studies to construct and defend informed arguments.
2. Assemble and analyse data, including making judgements about its relevancy and robustness.
3. Run this primary analysis.

Syllabus

Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module

Teaching methods

Delivery type	Number	Length hours	Student hours
Workshop	11	2.00	22.00
Lecture	11	1.00	11.00
Seminar	11	1.00	11.00
Independent online learning hours			11.00
Private study hours			245.00
Total Contact hours			44.00
Total hours (100hr per 10 credits)			300.00

Opportunities for Formative Feedback

Students will submit an early, non-assessed version of their research proposal of up to 2000 words for a mid-term review. They will then use the feedback to develop their proposal into a final 4000-word version which will be assessed and submitted at the end of term. A mid-term Mutiple Choice Question exercise will allow students to explore and consolidate their knowledge of key concepts and arguments from the basic voting theories before moving on to more applied models.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course MCQ	25 questions	10.00
Assignment	Coursework	90.00
Total percentage (Assessment Coursework)		100.00

The assessment should include the specification of an appropriate statistical modelling technique based upon a) the approaches studied in the workshop; b) an existing secondary dataset upon which the model may be tested.

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:19:21

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