Introduction to Quantitative Methods for Media and Communications

This information is for the 2020/21 session.

Teacher responsible

Prof Patrick Sturgis COL.8.05


This course is available on the MPhil/PhD in Data, Networks and Society, MPhil/PhD in Media and Communications, MSc in Gender, Media and Culture, MSc in Global Media and Communications (LSE and Fudan), MSc in Global Media and Communications (LSE and UCT), MSc in Global Media and Communications (LSE and USC), MSc in Media and Communications, MSc in Media and Communications (Data and Society), MSc in Media and Communications (Media and Communications Governance), MSc in Media and Communications (Research), MSc in Media, Communication and Development, MSc in Politics and Communication and MSc in Strategic Communications. This course is not available as an outside option.

Course content

An intensive introduction to quantitative data analysis in the social sciences, with illustrative examples and class exercises drawn from the field of Media and Communications. The course is intended for students with no previous experience of quantitative methods or statistics. It covers the foundations of descriptive statistics and statistical estimation and inference. At the end of the course students will have an understanding of how to carry out and interpret significance tests and be able to implement univariate and bivariate data analysis and simple multiple linear regression. The computer classes give 'hands-on' training in the application of statistical techniques to real social science research problems using the R computer package (no prior knowledge of R is necessary).


This course is delivered through a combination of classes and lectures in Michaelmas Term. This year, this teaching will be delivered through a combination of short online recorded films for the lectures and live classes, which will be delivered face-to-face where feasible, or online where not. Combined hours across lectures and classes will be equivalent to a minimum of 30 hours face-to-face teaching.

This course has a Reading Week in Week 6 of MT.

Formative coursework

Self-guided computer exercises implementing statistics covered in the lectures with weekly online homework on the material covered in the lectures and exercises.

Indicative reading

A course pack will be available for download online.

Additional reading: many introductory statistics books are available. But we particularly recommend Alan Agresti and Christine Franklin (2009) Statistics: The Art and Science of Learning from Data, and Alan Agresti and Barbara Finlay (2009, 4th edition) Statistical Methods for the Social Sciences.


Online assessment (100%).

Three-hour online assessment (100%) in the January exam period.

Important information in response to COVID-19

Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Methodology

Total students 2019/20: Unavailable

Average class size 2019/20: 2

Controlled access 2019/20: No

Value: Non-credit bearing

Guidelines for interpreting course guide information

Personal development skills

  • Application of numeracy skills
  • Specialist skills