ST416      Half Unit
Multilevel Modelling

This information is for the 2016/17 session.

Teacher responsible

Prof Irini Moustaki

Availability

This course is available on the 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) and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

Pre-requisites

A knowledge of probability and statistical theory, including linear regression and logistic regression.

Course content

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).

Teaching

20 hours of lectures and 10 hours of computer workshops in the LT.

Week 6 will be used as a reading week.

Formative coursework

Coursework assigned fortnightly and returned to students via Moodle with comments/feedback before the computer lab sessions.

Indicative reading

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).

Assessment

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

Student performance results

(2012/13 - 2014/15 combined)

Classification % of students
Distinction 30
Merit 22
Pass 36
Fail 12

Key facts

Department: Statistics

Total students 2015/16: 16

Average class size 2015/16: 16

Controlled access 2015/16: No

Lecture capture used 2015/16: Yes (LT)

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Problem solving
  • Application of numeracy skills
  • Specialist skills

Course survey results

(2012/13 - 2014/15 combined)

1 = "best" score, 5 = "worst" score

The scores below are average responses.

Response rate: 78%

Question

Average
response

Reading list (Q2.1)

1.6

Materials (Q2.3)

1.5

Course satisfied (Q2.4)

1.7

Lectures (Q2.5)

1.7

Integration (Q2.6)

1.6

Contact (Q2.7)

1.6

Feedback (Q2.8)

1.7

Recommend (Q2.9)

Yes

67%

Maybe

33%

No

0%