ST416 Half Unit
This information is for the 2018/19 session.
Dr Miltiadis Mavrakakis
This course is available on the MPhil/PhD in Data, Networks and Society, MPhil/PhD in Media and Communications, MSc in Inequalities and Social Science, MSc in Social Research Methods, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.
A knowledge of probability and statistical theory, including linear regression and logistic regression.
A practical introduction to multilevel modelling with applications in social research. This course deals with the analysis of data from hierarchically structured populations (e.g. student nested within classes, individuals nested within households or geographical areas) and longitudinal data (e.g. repeated measurements of individuals in a panel survey). Multilevel (random-effects) extensions of standard statistical techniques, including multiple linear regression and logistic regression, will be considered. The course will have an applied emphasis with computer sessions using appropriate software (e.g. Stata).
20 hours of lectures and 10 hours of computer workshops in the LT.
Week 6 will be used as a reading week.
Coursework assigned fortnightly and returned to students via Moodle with comments/feedback before the computer lab sessions.
T Snijders & R Bosker Multilevel Analysis: an Introduction to Basic and Advanced Multilevel Modelling, Sage (2011, 2nd edition);
S Rabe-Hesketh & A Skrondal, Multilevel and Longitudinal Modeling using Stata, (Third Edition), Volume I: Continuous responses (plus Chapter 10 from Volume II, which is available free on the publisher's website). Stata Press (2012);
H Goldstein, Multilevel Statistical Models, Arnold (2003, 3rd edition);
S W Raudenbush & A S Bryk, Hierarchical Linear Models: Applications and Data Analysis Methods, Sage (2002).
Exam (100%, duration: 2 hours) in the summer exam period.
Student performance results
(2014/15 - 2016/17 combined)
|Classification||% of students|
Total students 2017/18: 29
Average class size 2017/18: 26
Controlled access 2017/18: No
Lecture capture used 2017/18: Yes (LT)
Value: Half Unit
Personal development skills
- Problem solving
- Application of numeracy skills
- Specialist skills