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Prof Jouni Kuha COL.8.04

This course is available on the MSc in Human Geography and Urban Studies (Research). This course is available as an outside option to students on other programmes where regulations permit.

MSc students in the Department of Psychological and Behavioural Science take this course as part of PB411.

This course is not controlled access. If you register for a place and meet the prerequisites, if any, you are likely to be given a place.

Participants should have studied introductory statistics or quantitative methods before, up to an introduction to descriptive statistics and basic statistical inference. Students with no previous studies in quantitative analysis should take instead Introduction to Quantitative Analysis (MY451). 

Because of the overlaps between these courses, it is not possible to take both this course and either of Introduction to Quantitative Analysis (MY451) or Applied Regression Analysis (MY452) as assessed courses. 

The course is intended for students with some (even if limited) previous experience of quantitative methods or statistics. Using examples from psychological research, it covers first a review of the foundations of descriptive statistics and statistical inference, in the context of the analysis of two-way contingency tables and comparisons of means between two groups. The main topic of the course is linear regression modelling and related methods, including scatterplots, correlation, simple and multiple linear regression, and analysis of variance and covariance. An introduction to binary logistic regression modelling is also included. The computer classes give hands-on training in the application of these statistical techniques. 

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.