Module code: SOCM064

Module Overview

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 (, the worlds leading statistical software package. Emphasis throughout the module is on intuitive understanding and practical considerations, rather than rigorous derivation.

Module provider


Module Leader

BERLUSCONI Giulia (Sociology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 20

Independent Learning Hours: 105

Guided Learning: 15

Captured Content: 10

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

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 pattern

Assessment type Unit of assessment Weighting
Coursework Data analysis descriptive exercise (1500 words) 30
Coursework Data analysis regression exercise (2500 words) 70

Alternative Assessment


Assessment Strategy

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.

Module aims

  • 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

Learning outcomes

Attributes Developed
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

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

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
Upon accessing the reading list, please search for the module using the module code: SOCM064

Other information


Programmes this module appears in

Programme Semester Classification Qualifying conditions
Criminology (Corporate Crime and Corporate Responsibility) MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Criminology (Cybercrime and Cybersecurity) MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Criminology MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module

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 2023/4 academic year.