GRT-40020 - Quantitative Research and Data Analysis
Lecture Time:
Level: Level 7
Credits: 15
Study Hours: 150
School Office:

Programme/Approved Electives for 2024/25


Available as a Free Standing Elective






Barred Combinations


Description for 2024/25

What is quantitative social research? What kinds of questions about the social world can we ask when using a quantitative research design? What challenges do quantitative researchers face in their work? How do we make social phenomena measurable? What makes quantitative social research produce reliable and valid results? What tools do quantitative researchers use, and what are their benefits? How do quantitative researchers report the outcomes of their research?
This introduction to quantitative research and data analysis builds on earlier work in the programme by developing and consolidating theoretical and practical knowledge of quantitative approaches to conducting social research. Students will discuss the opportunities offered by a quantitative approach to doing research, and develop familiarity with the theoretical underpinnings of quantitative social research. They will focus on the problematic of operationalisation, sampling in survey research, and the structured ways in which data is numerically organised. They will also develop understanding of introductory statistical procedures used in the analysis of quantitative data. Practical work will include questionnaire design, data analysis and the writing of a quantitative research design.
The module will provide the opportunity to develop important transferable skills, and students will gain familiarity with the SPSS software, which is widely used in the analysis of quantitative data sets.

The module aims to deliver a comprehensive introduction to the principles and practices of quantitative social science research. The module covers major themes in the theoretical appraisal of the methodological terrain of quantitative research methods, including the question of causality, the problematic of operationalisation, and theories of sampling. The module will engage students in a discussion of quantitative research design; the development of research instruments, such as questionnaires, and it will offer an introduction to the statistical analysis of quantitative data sets and the SPSS software.

Intended Learning Outcomes

Demonstrate knowledge of quantitative methods of design, data collection and analysis: 1
Develop critical awareness of the main benefits and limits of a quantitative approach to specific social issues: 1
Understand key quantitative concepts, such as: generalisability, hypothesis testing, variance, statistical significance, sampling, probability.: 1
Access large secondary datasets and be able to prepare them for analysis: 1
Apply simple techniques of data analysis to social science problems using SPSS: to include univariate and bivariate analysis (frequencies, central tendency, tabulation, cross-tabulation, control variables, confidence intervals, t-tests/ANOVA), with the option to add in more intermediate techniques (linear/logistic regression) if the group is able.: 1
Formulate a simple research question, design an appropriate approach, carry out the analysis, use appropriate visualisations and discussion in reporting the outcomes, including understanding how to report when analysis does not work as hoped.: 1
Analyse and interpret results/outcomes of data exploration: 1
Communicate data analysis outcomes professionally, with appropriate academic standards for reporting/visualising/labelling, and while providing suitable social science context/explanation: 1

Study hours

15 hours contact time in workshop format, focused on problem-based learning in an IT lab using SPSS, but also including mini-lecture and discussion elements.
30 hours asynchronous structured practical/worksheet completion
15 hours engagement with supplementary online materials
10 hours asynchronous group discussion/reflection
5 hours optional tutorial drop in time
15 hours - class preparation time to include pre-reading,
60 hours - assessment preparation time, to include focused & wider, reading; sourcing data; analysis; writing up; redrafting; proofing.

School Rules

Students should ideally have a prior qualification with some social science or broader numeracy element - this may be a GCSE Maths at grade 4 or above (or equivalent). Alternatively, this might be a social science numeracy/statistical element in their prior degree, or relevant work experience in presenting/handling numerate data.
Although this module is core for certain PGT courses, it needs to be clear to admissions tutors and to those students seeking (and their supervisors advising them) to take it as an option, that it requires the willingness to engage with some mathematical/statistical concepts and operations. The module does work with students with no experience/confidence in these areas; however there does need to be a willingness to engage.

Description of Module Assessment