GV2ATA-Analysing Social Data: Techniques and Applications

Module Provider: Geography and Environmental Science
Number of credits: 20 [10 ECTS credits]
Level:5
Terms in which taught: Autumn / Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Prof Steve Musson
Email: s.musson@reading.ac.uk

Type of module:

Summary module description:

This module will explore the analysis of social data, using quantitative and qualitative. We will use social data to persuade, argue and illustrate our understanding. During the module, you will become a better informed, more confident and critical user of social data.ÌýÌý

The first section of the module deals with quantitative (i.e. numerical) approaches. We will develop technical analysis skills using Excel and put these into practice with a large dataset such as the UK Census. The emphasis will be on applying simple analytical techniques to secondary data sources and no great level of mathematical ability is assumed.Ìý

The second section of the module deals with qualitative approaches. We will develop a different set of analytical techniques and better understand how we can interpret textual documents. The emphasis will again be on using secondary data and we will put these techniques into practice using a large dataset such as the Mass Observation Archive. If possible, we will visit a public record archive to better understand these data sources.Ìý

Students have often found these techniques useful in dissertations, other research projects, and in future employment. As such, this module can be the gateway for further research and professional development.Ìý

Aims:

1. To encourage students to understand social data as a socio-political product and to enable them to reflect on the epistemological and methodological implications of this perspective;Ìý


2. To empower students to become critical users of social data, with particular reference to the relative strengths and weaknesses of a range of data sources;Ìý


3. To develop students' confidence in finding and using social data for research purposes, including the development of a range of analytical and visualisation techniques that allow them to understand the possibilities of different types of social data.ÌýÌý


4. To enable students to develop data analysis techniques relevant to a wide range of sources.Ìý


5. To apply these skills to a range of qualitative and quantitative data sources to answer research questions.ÌýÌý

Assessable learning outcomes:

By the end of this module, students will be able to:ÌýÌý

1. Identify different sources of social data and think critically about their potential utilityÌýÌý


2. Demonstrate their ability to manipulate social data, conduct appropriate analysis and display their results in an appropriate wayÌýÌý


3. Use social data to make a compelling and evidenced argument ÌýÌý


4. Reflect on their use of socialdata in a way that demonstrates a critical understanding of socio-political processes of data productionÌý

Additional outcomes:

Students will become more confident users of social data and develop a range of transferable skills, in sourcing, manipulating, analysing, visualising and reporting social data. These skills will be invaluable in subsequent academic modules (especially the undergraduate dissertation) and are highly sought after by prospective graduate employers. The ability to think critically about data and to argue in an evidenced way are important life skills and this module gives students an opportunity to develop their abilities in this respect.Ìý

Outline content:

This module begins with seminars that introduce students to key features of social data, research applications and critical interpretation of its role in the creation of knowledge. Students will encounter, manipulate and analyse a range of social data. This will initially take the form of teaching data sets, but students will later be expected to obtain their own data in an informed and critical manner. Towards the end of Autumn Term, students will work on a small data analysis task, in which they will be expected to demonstrate their ability as a critical user of social data. In the Spring Term, students will be introduced to qualitative