Introduction to Quantitative Analysis

The details
Colchester Campus
Postgraduate: Level 7
Thursday 05 October 2023
Friday 15 December 2023
26 May 2023


Requisites for this module



Key module for

MSC B99012 Health Research,
MA M90012 Criminology,
MSC MF9012 Organised Crime, Terrorism and Security,
MA L30112 Sociological Research Methods,
MSC L31012 Survey Methods for Social Research,
MSC L310MO Survey Methods for Social Research,
MSC L31112 Migration Studies,
MPHDML9048 Criminology,
PHD ML9048 Criminology,
MPHDB79748 Health Studies,
PHD B79748 Health Studies,
MSOCM999 Criminology,
MSOCMX98 Criminology (Including Placement Year),
MSOCMX99 Criminology (Including Year Abroad)

Module description

This module is a practical introduction to analysing quantitative data. Using a combination of lecture and computer lab based formats, the module is intended to provide participants with an understanding of the principles of quantitative data analysis and their practical application. The primary focus is on the application of statistical techniques for analysing survey data, although the methods covered are applicable to many other forms of quantitative data. As well as enabling participants to conduct investigations relevant to their own research, it will also equip them to be a critical user of other research.

Module aims

The aim of the course is to introduce students who have little experience of quantitative methods to basic and intermediate statistical concepts and procedures. The emphasis is on practical applications, not mathematics (but an amount of very elementary maths is, inevitably, required!). The teaching is carried out with a combination of lectures, discussion and computer lab sessions conducting large-scale data analysis using STATA.

Module learning outcomes

By the end of this module, you should be able to:

• Ask correct research questions, learn thinking analytically about answering the research questions
• Understand the logic of formulating testable hypotheses and test them using quantitative research methods
• Understand large-scale data description and inference
• Critically evaluate research articles that use statistics
• Understand the link between theory and statistical models
• Carry out elementary and intermediate statistical analysis using STATA

Module information

Autumn Term
Topic 1 - week 2 Social statistics, variables, measurement
Topic 2 - week 3 Description and inference
Topic 3 - week 4 Correlation and Ordinary Least Square (OLS) regression
Topic 4 - week 5 Multiple Ordinary Least Square (OLS) regression
Topic 5 - week 6 Interaction, dummy variables and interpretation OLS
Topic 6 - week 7 Causal pathways and model building
Topic 7 - week 8 Introduction to binary logit models
Topic 8 - week 9 Interpretation and presentation of multiple Logit Models
Topic 9 - week 10 Concepts, measurement and summated scales
Topic 10 - week 11 Revision and starting the final assignment

Learning and teaching methods

Most modules at postgraduate level in Sociology are taught as a 2hr seminar. Most classes, labs and seminars will be taught face-to-face (assuming social distancing allows this). There may also be some online activities – either timetabled as a live online session or available on Moodle in the form of pre-recorded videos. You will be expected to watch this material and engage with any suggested activities before your seminar/class each week. Please note that you should be spending up to ten hours per week undertaking your own private study (reading, preparing for classes or assignments, etc.) on each of your modules (e.g. 30 hours in total for three 20--credit modules). This module [SC504] will include a range of activities to help you and your teachers to check your understanding and progress. These are: intensive reading, especially in the beginning of the module and doing lab exercise on large scale data analysis by using the statistical data analysis software STATA. You are strongly encouraged to attend lab sessions as they teach you to learn to implement the social science methods and conducting data analysis and they provide an opportunity to talk with your class teacher and other students. The classes/seminars will be captured and available via Listen Again. However, if you want to gain the most you can from these seminars/classes, it is very important that you attend and engage. Please note that the recording of seminars/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   Report 1   27/10/2023  20% 
Coursework   Report 2  24/11/2023  20% 
Coursework   Final Report  15/12/2023  60% 

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 Giacomo Vagni, email:
Dr Giacomo Vagni



External examiner

Prof Benjamin Bradford
University College London
Available via Moodle
Of 30 hours, 22 (73.3%) hours available to students:
8 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

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