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 social data, including some of the main analytical techniques used to understand and interpret such sources. We will use a major social data archive collection to put these techniques into practice and, if possible, visit a public record collection to work with qualitative social data first hand.Ìý

Brief description of teaching and learning methods:

This module will be delivered through a mixture of lectures and practical classes. A companion website will support learning with supplementary material including sample datasets and worked through exercises. A field visit is planned for the Spring Term.Ìý

Contact hours:

Ìý	Autumn	Spring	Summer
Demonstration	20	14
External visits		6
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (directed)	20	20
Ìý Ìý Advance preparation for classes	30	30
Ìý Ìý Preparation of practical report	25	25
Ìý Ìý Reflection	5	5
Ìý	Ìý	Ìý	Ìý
Total hours by term	100	100	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Report	100

Summative assessment- Examinations:

N/A

Summative assessment- Coursework and in-class tests:

Autumn Term Report (Quantitative Data Analysis Exercise) 2500 words. Submitted in Autumn Term Week 10 (50%).Ìý

Spring Term Report (Qualitative Data Analysis Exercise) 2500 words. Submitted in Spring Term Week 11 (50%).Ìý

Formative assessment methods:

Data analysis class exercises, which allow students to check their learning. The answers and support in developing data analysis skills will be provided through companion materials on the Blackboard site.Ìý

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

40% overallÌý

Reassessment arrangements:

Via alternative assessmentÌý

Additional Costs (specified where applicable):

1) Required text books:Ìý

2) Specialist equipment or materials:Ìý

3) Specialist clothing, footwear or headgear:Ìý

4) Printing and binding:Ìý

5) Computers and devices with a particular specification:Ìý

6) Travel, accommodation and subsistence:Ìý£10 (day subsistence for field class)Ìý

Last updated: 30 March 2023