QUANTITATIVE RESEARCH METHODS - 2024/5

Module Overview

This module builds upon the research methods training which students received in their first year to provide students with a more robust understanding of some of the main quantitative analysis approaches in the social sciences. Students will learn about multivariate quantitative analyses with the help of R, a software environment for statistical computing and graphics. They will investigate a range of topics in sociology and criminology by accessing suitable secondary data from the UK Data Service, analysing data using R, and interpreting and presenting the results of quantitative analyses.

Module provider

Sociology

Module Leader

BRUNTON-SMITH Ian (Sociology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

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

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

This module aims to:

• Enhance students’ knowledge of and skills in quantitative data analysis (both descriptive and inferential).


• 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.


• Enable students to demonstrate awareness of the logic behind, and the appropriate time to use regression analysis as a tool for social research.


• Develop students’ understanding of data analysis procedures in R (and RStudio) and the ability to adapt them to new problems.

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to allow students to demonstrate their understanding of both descriptive and inferential statistical techniques, 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:  Exercise 1 (coursework), 30% (addresses LO1, LO2 and LO4) – critically discuss survey data from a UK survey and analyse the responses to some of the questions asked in the same survey using descriptive statistics.

 

• Assessment 2: Exercise 2 (coursework), 70% (addresses LO1, LO2, LO3 and LO4) – analyse quantitative secondary data using both descriptive and inferential statistics, interpret the results, and present the main 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

• Enhance students¿ knowledge of and skills in quantitative data analysis (both descriptive and inferential)
• 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
• Enable students to demonstrate awareness of the logic behind, and the appropriate time to use regression analysis as a tool for social research
• Develop students¿ understanding of data analysis procedures in R (and RStudio) and the ability to adapt them to new problems

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to encourage students to develop a practical understanding of when and how to use particular multivariate statistical techniques to best research social processes using quantitative data. This will be achieved through lectures and laboratory classes that include practical exercises on R (and RStudio).

The weekly lectures will introduce students to key data management and analysis techniques, illustrated through examples using real-world data and R code.

The weekly laboratory classes 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 secondary 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

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: SOC2102

Other information

The School of Sociology is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

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 laboratory classes will therefore be extremely valuable for their future careers.

Digital capabilities: Students will strengthen their understanding of data analysis procedures in R, building upon the training they received in their first year. 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.

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 use regression analysis to explore the association between variables and to competently interpret the results. This will involve the development of new skills and the building of confidence to successfully apply these in Assignment 2.

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 2024/5 academic year.