Module code: SOCM064

Module Overview

Quantitative data analysis is one of the key methodological approaches available to social researchers, enabling them to identify and 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. Students will learn how to ask questions with quantitative data, will be introduced to the fundamental statistical principles required for making robust and generalisable claims, and will consider theoretical, methodological, and practical issues which have an impact on quantitative research design. 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.


Emphasis throughout the module will be on intuitive understanding and practical considerations, rather than rigorous derivation, and on hands-on practical experience with R, the world’s leading statistical software package.

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: 22

Independent Learning Hours: 103

Guided Learning: 15

Captured Content: 10

Module Availability

Semester 1

Prerequisites / Co-requisites


Module content

Indicative topics include the following:


  • Asking quantitative research questions

  • Quantitative data collection and sampling

  • Secondary data analysis and the UK Data Service

  • Data management, including recoding and computing of new variables

  • Descriptive statistics, data visualisation, and exploratory data analysis

  • Fundamentals of statistical inference

  • Quantifying association and introduction to regression approaches

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Data analysis descriptive exercise 30
Coursework Data analysis regression exercise 70

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to allow students to demonstrate their understanding of the basic principles of quantitative research methods, including both key concepts and the use of software to demonstrate those concepts practically.


Thus, the summative assessment for this module consists of:


  • Assessment 1:  Data analysis descriptive exercise (coursework), 30% (addresses LO1, LO2, LO3 and LO4) – develop a research question, identify suitable secondary data and variables, and produce basic descriptive statistics and data visualisations.


  • Assessment 2: Data analysis regression exercise (coursework), 70% (addresses LO1, LO3, LO4 and LO5) – extend the analysis conducted in Assessment 1 by elaborating on the conceptual framework, building a regression model, interpreting the results, and presenting the findings.


Formative assessment and feedback


Continuous formative assessment in class will allow students to demonstrate their appreciation of the potential of statistical evaluation, manipulation, and interpretation of data, and will also allow them to develop transferable and practical skills in analysing data with statistical software.


Detailed guidance on how to complete the summative assessments will be given in class and on Surrey Learn, and students will be provided with opportunities to ask questions and receive feedback on their assignment plans.


Students will receive individual, written feedback on return of both assessed exercises.

Module aims

  • Introduce students to the basics of quantitative data analysis for social research (both descriptive and inferential
  • Equip students with the tools necessary to use quantitative data to answer questions about the social world
  • Develop students' understanding of data analysis procedures in R (and RStudio) and the ability to adapt them to new problems
  • Give students practical experience of analysing real world problems through secondary analysis of large data sets
  • Equip students to reflect upon the impact of data collection methods, concept operationalisation, and other contextual factors on the meaning of the findings generated by quantitative data analysis

Learning outcomes

Attributes Developed
001 Demonstrate knowledge and understanding of descriptive and inferential statistical techniques in quantitative research KCP
002 Develop research questions that can be answered quantitatively and identify relevant secondary data KCT
003 Adapt R code to analyse secondary quantitative data KPT
004 Identify the appropriate time to use specific descriptive and inferential statistical techniques, and interpret and present the results KCP
005 Show an understanding of the factors that may impact on the meaning of the findings generated by quantitative data analysis KPT

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 inferential data analysis, and critically evaluate existing empirical studies. This will be achieved through a combination of lectures and laboratory classes that include practical exercises on R (and RStudio).


The weekly session will combine a formal lecture style with practical workshops giving students hands on experiences with R, group work, and open discussion. The sessions will introduce students to key concepts and data analysis techniques; the workshops will allow students to engage in practical exercises, familiarise with the practical and technical issues using R to analyse quantitative data, and gain experience in quantitative data analysis.

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

The Department of Sociology is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module enhances students skills in Employability, Digital Capabilities, Global and Cultural Capabilities, and Resourcefulness and Resilience.


Digital capabilities: Students will develop an understanding of data analysis procedures in the software environment R. They will also learn how to access quantitative secondary data from the UK Data Service, which is the UK's largest collection of economic, population and social research data.


Employability: Knowledge of R (and RStudio) is proving to be extremely popular with employers, with a large number of social research organisations moving away from other statistics packages to focus on R. The practical skills that students will learn in the workshops will therefore be extremely valuable for their future careers.


Global and cultural capabilities: Students will learn how to use quantitative secondary data to explore a range of topics, including social inequalities surrounding income, gender, ethnicity as well as access to education, health care, and justice, thus expanding their knowledge about the differences and needs of other social groups.


Resourcefulness and resilience: Students will be required (with guidance) to independently develop a quantitative research project involving the analysis of secondary data and to competently interpret the results. This will involve the development of new skills and the building of confidence to successfully apply these in the module’s assignments.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Social Research Methods MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
Social Research MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module
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 2025/6 academic year.