Analysing Data in the Real World - SOCI5012

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2022 to 2023
Combined Autumn and Spring Terms 6 30 (15) Jack Cunliffe checkmark-circle


This module aims to develop standard research skills into a quantitative research skillset that will enable the student to work with data, from working with different types of datasets/variables to analysing this data and presenting it in oral and written form.

Learning will be orientated towards:
• Learning ways to work with and manipulate datasets to make them ready for statistical analysis (i.e. to create tidy data)
• Critically understanding the limitations of simple (OLS) regression, with particular emphasis on endogeneity/confounding and causal heterogeneity;
• Learning a number of advanced methods for investigating the social world through quantitative research (e.g. associative and causal methods). For each method, students will first consider the rationale for the method (its strengths and limitations), and then use the method in hands-on statistical analysis sessions using appropriate statistical software (e.g. R);
• Learning how to communicate and present data and quantitative analysis (e.g. with various types of data visualisations)


Contact hours

Total contact hours: 66
Private study hours: 234
Total study hours: 300


Compulsory Stage 3 module for any bachelor degree programme that includes 'with Quantitative Research'

Method of assessment

Main assessment methods

Coursework - Seminar participation and related exercises - 20%
Coursework - personal report 1 (2000 words) - 20%
Coursework - Group presentation - 20%
Coursework - personal report 2 (3000 words) - 40%

Reassessment methods
100% coursework

Indicative reading

Angrist, J.D. and Pischke, J.S., (2014). Mastering 'metrics: the Path from Cause to Effect. Princeton, Princeton University Press.
Cook, T., & Campbell, D. (1979) Quasi-experimentation: Design and analysis issues for field settings. Chicago, Rand McNally College Publications.
Grolemund, G. & H. Wickham. 2017. R for Data Science.
Healy, K. 2018. Data Visualization: A practical introduction.
Imai, K. 2018. Quantitative Social Science: An Introduction.
Morgan SL (2nd edition 2015), Counterfactuals and Causal Inference: methods and principles for Social Research, New York, Cambridge University Press
Murnane, R.J. and Willett, J.B., (2010). Methods Matter: Improving Causal Inference in Educational and Social Science Research. Oxford University Press.
Robson, C and McCartan, K (2016), Real-World Research: a resource for users of social research methods in applied settings 4th edition., Chichester, Wiley.

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:

8.1 Have a proficient ability to use appropriate statistical software (e.g. R);
8.2 Have a critical understanding of the limitations of common analytical techniques;
8.3 Critically understand the strengths and limitations of advanced methods for investigating causality
8.4 Demonstrate careful data visualisation skills in communicating quantitative research;
8.5 Demonstrate an ability to thoroughly critique quantitative analytical claims made in public debates and in academic research;
8.6 Demonstrate an ability to present the rationale and results of advanced statistical methods using a range of methods to non-technical audiences;
8.7 Be able to manipulate and clean data

The intended generic learning outcomes.
On successfully completing the module students will be able to:

9.1 Demonstrate an ability to use statistical packages to use, analyse and present quantitative data;
9.2 Critically understand the strengths and weaknesses of advanced quantitative methods, and apply sound judgement in real-world scenarios;
9.3 Demonstrate proficiency in the use of one or various statistical software packages;
9.4 Organise information clearly and persuasively communicate research using a variety of methods;
9.5 Create visualisations and presentations of analysis;
9.6 Work in a group and to produce clear communication using a variety of methods of research results.


  1. Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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