杏吧论坛

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 (杏吧论坛 and Fudan), MSc in Global Media and Communications (杏吧论坛 and UCT), MSc in Global Media and Communications (杏吧论坛 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.

Students on the programmes listed above will be enrolled on this course when you register for MC4M1, MC4M2 or MC5M2. You must not register separately for MY464. It is not possible to take MY464 as a standalone course.

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.

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

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.

Exam (100%, duration: 2 hours) in the January exam period.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.