Your dissertation is a piece of original and supervised research on a topic of personal interest to you, and relating to some aspect of your field of study. 

The dissertation should include both critical engagement with the literature, and a significant element of original research and independent analysis. In carrying out this piece of work, you will identify a topic and a question; make use of arguments, concepts, evidence and methods of analysis taught in your programme’s component modules; and clearly and rigorously write up your argument and conclusions.

The dissertation module - POLS0102 - exists to support you on this process. It covers topics such as: 

· how to plan and organise your research, working to a deadline;

· how to write at a specified length and for a specific purpose and audience;

· how to assess and evaluate critically the contributions made by existing research or policy analysis (where relevant) in the chosen field;

· how to make use of the concepts and methods taught in your programme, in the analysis of a concrete issue or problem;

· how to approach and conduct original research;

· how to communicate clearly.

This module aims to build skills in applied statistics using a variety of methods, including regression, spatial analysis and quantitative text analysis. It...

This module aims to build skills in applied statistics using a variety of methods, including regression, spatial analysis and quantitative text analysis. It starts with an introduction to multiple regression, advanced survey methods and missing data before going to look at a host of spatial analysis methods. In term 2, students will begin with an introduction to the quantitative analysis of text, including methods of document classification and web scraping. They will then be introduced to a wider range of regression techniques, including models for binary dependent variables, panel data, multilevel models, and poststratification. The module caters for students from diverse disciplinary backgrounds. Students will become proficient users of statistical software (R) and be able to identify, analyse and interpret regression output. Throughout there is an emphasis on the use, interpretation and understanding of results.