Modelling Crime and Society

The details
Sociology and Criminology
Colchester Campus
Full Year
Undergraduate: Level 6
Thursday 03 October 2024
Friday 27 June 2025
03 November 2023


Requisites for this module
GV207 or SC202 or SC208



Key module for

BSC L315 Sociology (Applied Quantitative Research),
BSC L316 Sociology (Applied Quantitative Research) (Including Year Abroad),
BSC L317 Sociology (Applied Quantitative Research) (Including Placement Year),
BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (Including foundation Year)

Module description

This module will develop students' understanding of quantitative analysis and impart the practical skills necessary for carrying out advanced statistical analysis of social data using modern statistical software and programming.

Module aims

The aims of this module are:

  • To develop students’ understanding of quantitative methods in general and how to build statistical models representing sociological and criminological processes and behaviours.

  • To teach students the practical skills necessary for carrying out advanced statistical analysis of sociological and criminological data using modern statistical software and programming.

Module learning outcomes

By the end of this module, students will be expected to be able to:

  1. Perform, critically interpret, and communicate results from analysis using OLS regression, including models with categorical predictors, interactions, and non-linear terms.

  2. Perform, critically interpret, and communicate results from analysis using logistic and multinomial logit models.

  3. Freely and flexibly use computational tools —R— to perform reproducible data analysis and communicate your results.

  4. Identify and deal with issues of p-hacking, reverse causality, omitted variable bias, measurement error.

  5. Evaluate the internal, external, and ecological validity of a research project.

  6. Use OSF to pre-register your work and collaborate with others.

  7. Critically analyse different criminological and sociological issues using relevant models for causal inference.

Module information

If you wish to take this module but have not taken the second year module 'Analysing Social Life' (SC202-5-AU), please contact the Module Convenor to see if you have the appropriate background in statistics. Please click on the link below to view the Introduction video to SC385 Models and Measurement in Quantitative Sociology

The first term of the module is focused on statistical models and begins with simple OLS regression and provides a framework for modelling strategy and variable selection. Students are then taken through extensions to the basic OLS model, with categorical predictors, interactions and non-linear terms. Next, we introduce models for categorical outcomes: binary logistic and multinomial logit. The term concludes with a discussion of practical topics in survey data analysis – how to deal with complex sample designs, weighting and non-response adjustments. The modelling framework outlined in this term builds the foundations for advanced quantitative social science methods.

The second term of the module introduces students to data science concepts, techniques, and the skills necessary to analyse a variety of criminological and sociological problems that will give students an insight into how modern social scientific work is conducted and give them the skills to gain an edge in a competitive job market. Students will engage in hands-on reproducible data analysis workflows using open-source and open-access tools, such as R, G*Power, and the Open Science Framework (OSF). No prior knowledge of programming is required.

Students will learn how to conduct rigorous and reproducible research using observational and experimental data. We will consider several forms of data collection (e.g., lab experiments, field experiments, surveys, web scraping), discuss the advantages and disadvantages of each approach, and learn how to design a project employing such methods. The content is organized around three fundamental topics crucial in social science: reproducibility, causal inference, and big data. Case studies from criminology and sociology will be used throughout the course, illustrating to students how to apply different data science techniques to practical cases in areas in which they are interested.

This module is part of the Q-Step pathway. Q-Step is an award which you can gain simply by enrolling on specific modules and will signal to employers your capability in quantitative research. Learn more about the Q-Step pathway and enhance your degree now.

Learning and teaching methods

The teaching consists of online and face to face lectures, a weekly discussion class where students read and comment on a paper on a criminological or sociological case study that uses quantitative methods, and a computer lab session where students will learn how to build statistical models and analyse the results, using social and criminological real-world datasets.

Most modules in Sociology are divided into lectures of around 50 minutes and a class of around 50 minutes. Some are taught as a 2hr seminar, and others via a 50-minute lecture and 2-hr lab. Lectures, classes, labs and seminars will be taught face-to-face. Please note that you should be spending up to eight hours per week undertaking your own private study (reading, preparing for classes or assignments, etc.) on each of your modules (e.g. 32 hours in total for four 30-credit modules). The lectures provide an overview of the substantive debates around the topic of the week, while the classes will give you the opportunity to reflect on your learning and actively engage with your peers to develop your understanding further. You are strongly encouraged to attend the classes as they provide an opportunity to talk with your class teacher and other students. The classes will be captured and available via Listen Again. However, if you want to gain the most you can from these classes it is very important that you attend and engage. Please note that the recording of classes is at the discretion of the teacher.


The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   In-class test    15% 
Coursework   Data Analysis Report 1    35% 
Coursework   Data Analysis Exercise     20% 
Coursework   Data Analysis Report 2    30% 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Magda Borkowska, email:
Dr Sergio Lo Iacono, email:
Professor Nick Allum & Dr Sergio Lo Iacono
Jane Harper, Undergraduate Administrator, Telephone: 01206 873052 E-mail:



External examiner

Dr Emily Gray
University of Warwick
Assistant Professor of Criminology
Available via Moodle
Of 13 hours, 9 (69.2%) hours available to students:
4 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.


Further information
Sociology and Criminology

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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