Module and Programme Catalogue

Search site

Find information on

2021/22 Undergraduate Module Catalogue

LAW3287 Quantitative Social Research II: Advanced Statistical Modelling and Crime Data

20 creditsClass Size: 30

Module manager: Dr Jose Pina-Sánchez

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2021/22

Pre-requisite qualifications

A basic understanding of statistical inference, regression modelling, and R is required.


SLSP3065Quantitative Social Research

This module is not approved as a discovery module

Module summary

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.

Learning outcomes
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.

Skills outcomes
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
Agent-based modelling

Teaching methods

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Private study hours200.00
Total Contact hours0.00
Total hours (100hr per 10 credits)200.00

Private study

The School is committed to providing an excellent student education and experience. This will involve a variety of teaching methods and follow a blended learning model, including meaningful on-campus in-person teaching for all students. Further information regarding teaching delivery will follow.

Opportunities for Formative Feedback

Students will be asked to complete 11 take-home data exercises and generic feedback will be provided for each exercise, which will help students evaluate their progress.

Methods of assessment

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Assessment typeNotes% of formal assessment
Report1 x 3,000-word project report100.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 02/09/2021 17:02:40


Browse Other Catalogues

Errors, omissions, failed links etc should be notified to the Catalogue Team.PROD

© Copyright Leeds 2019