Module manager: Dr Jose Pina-Sánchez
Email: J.PinaSanchez@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
A basic understanding of statistical inference, regression modelling, and R is required.
| SLSP3065 | Quantitative Social Research |
This module is not approved as a discovery module
This module will offer students the opportunity to enhance their data analytical skills obtained in the module “Quantitative Social Research”. Students will be introduced to a wide range of statistical models used in social science research, and to the most common assumptions invoked in such models. The focus of the module is eminently applied and based on teaching and learning activities that emphasise hands-on work with datasets on topics of interest to criminology, sociology, and social policy. This module would be an ideal choice for students wanting to undertake quantitative dissertations in social science degrees and/or students wanting to develop the key skills to conduct applied quantitative research in academic and industry settings.
This research-based module enables students to extend their quantitative skills-set to answer different types of research questions. Over the duration of the module students will:
- familiarise themselves with a variety of existing key datasets used in social science research, with an emphasis on data used to study the phenomenon of crime;
- enhance their data analysis skills using the statistical software R;
- learn the most widely used statistical models in the social sciences;
- recognise some of the key assumptions made in quantitative research and the modelling strategies available to comply with (and/or relax) those assumptions;
- learn to present quantitative findings visually and succinctly.
On completion of the module, students should be able to:
- Identify the most common assumptions invoked in quantitative research.
- Identify the appropriate statistical models to analyse different types of data and research questions.
- Identify optimal modelling strategies applicable to different forms of data.
- Use self-teaching materials available online and in R to learn about other statistical model beyond those covered in the module.
- Present effectively research findings using visual methods.
Statistical modelling
Data analytics
Introduction and R recap
Explanatory variables
Moderating and mediating effects
Non-linear effects
Data quality
Data reduction techniques
Hierarchical data
Longitudinal data
Time-to-event data
Time-series
Agent-based modelling
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Computer Class | 11 | 2 | 22 |
| Private study hours | 178 | ||
| Total Contact hours | 22 | ||
| Total hours (100hr per 10 credits) | 200 | ||
A formative assessment opportunity will be provided.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | 1 x 3,000-word project report | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team