SOCIAL DATA ANALYTICS - 2021/2
Module code: SOCM064
In light of the Covid-19 pandemic, and in a departure from previous academic years and previously published information, the University has had to change the delivery (and in some cases the content) of its programmes, together with certain University services and facilities for the academic year 2020/21.
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Quantitative data analysis is one of the key methodological approaches available to social researchers, enabling them to identify end explain important patterns in the social structure of society.
In this module students will learn how quantitative research approaches can be used to describe and explore the social world. We will begin by considering how to ask questions with quantitative data, before outlining the fundamental statistical principles required for making robust and generalizable claims. We will then go on to consider theoretical, methodological and practical issues, which have an impact on quantitative research designs. Students will also learn how to collect and analyse quantitative data, covering issues of sampling and descriptive statistics, as well as being introduced to general regression approaches.
In addition to the formal lectures there will be hands on practical workshops where students will be introduced to R (www.r-project.org), the worlds leading statistical software package. Emphasis throughout the module is on intuitive understanding and practical considerations, rather than rigorous derivation.
BRUNTON-SMITH Ian (Sociology)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
JACs code: G300
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
This module introduces students to quantitative approaches to social science, and provides hands on experience with statistical software. Indicative content includes:
- Asking quantitative research questions
- Quantitative data collection and sampling
- Measuring the social world
- Fundamentals of statistical inference
- Quantifying association
- Introducing regression approaches
Hands on practical workshops will cover:
- Data management
- Coding and recoding
- Descriptive statistics and exploratory data analysis
- Graphics for display and analysis
- Correlation and regression
|Assessment type||Unit of assessment||Weighting|
|Coursework||Data analysis descriptive exercise (1500 words)||30|
|Coursework||Data analysis regression exercise (2500 words)||70|
The assessment strategy is designed to provide students with the opportunity to demonstrate their understanding of the basic principles of quantitative research methods, including both statistical concepts and the use of software to demonstrate those concepts practically. Continuous formative assessment in class will allow students to demonstrate their appreciation of the potential of the evaluation, manipulation and interpretation of data, and also allow them to develop practical data analysis skills.
Thus, the summative assessment for this module consists of:
- One 1,500 word written exercise (30%)
- One 2,500 word practical exercise (70%)
Formative assessment and feedback
Students will receive formative feedback during practical classes and on feedback sheets provided on return of the first shorter exercise.
- Equip students with the tools necessary to start using quantitative data to answer important questions about the social
- Introduce students to the basics of data analysis for social research from first principles
- Introduce students to statistical software with which to analyse quantitative data
- Give students practical experience of analysing real world problems through secondary analysis of large government
data sets, such as the British Social Attitude Survey and the Crime Survey for England and Wales
|001||Understand the fundamentals of quantitative data collection and the main quantitative research techniques||KP|
|002||Be able to critically evaluate existing empirical research from the social sciences||C|
|003||Be able to carry out advanced data management tasks prior to analysis||KCP|
|004||Have a comprehensive understanding of descriptive statistics and how to apply them on their own data set or on other secondary data sources||KC|
|005||Be able to understand regression analysis as a tool for social research||K|
|006||Have a critical understanding of the logic behind, and the appropriate use of both bivariate and multivariate analysis||KC|
|007||Have the technical expertise to know how to conduct descriptive analysis using R||CPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Overall student workload
Independent Study Hours: 120
Lecture Hours: 16
Practical/Performance Hours: 14
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Enable students to explore important social questions using quantitative data, undertake descriptive and exploratory data analysis, and critically evaluate existing empirical studies. This will be achieved through a combination of lectures and hands on practical workshops using R.
The learning and teaching methods include:
- Ten 2 hour seminars combining formal lectures, practical workshops giving students hands on experience with R, group work, and open discussion
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list for SOCIAL DATA ANALYTICS : http://aspire.surrey.ac.uk/modules/socm064
Programmes this module appears in
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2021/2 academic year.