ST314      Half Unit
Multilevel and Longitudinal Models

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Additional requisites:

A first course in statistics such as Elementary Statistical Theory (ST102), Elementary Statistical Theory I (ST109) or Quantitative Methods (Statistics) (ST107) and familiarity with multiple regression to the level of Applied Regression (ST211) or Statistical Models and Data Analysis (ST201).

Students who have no previous experience in R are required to take an online pre-sessional R course from the Digital Skills Lab before the start of the course. Please log into moodle.lse.ac.uk and self-enrol in the 'R for Statistics Pre-sessional Course’.

This course considers statistical methods for the analysis of data with a multilevel (clustered) structure with applications in social research.  Examples of multilevel structures include students nested within schools or universities, and individuals nested within households or geographical areas.  Multilevel structures also arise in longitudinal or panel studies where repeated measurements over time are taken on subjects (e.g. individuals or countries).  The course has an applied emphasis, and will consider practical issues such as the choice of an appropriate method of analysis for a given dataset and research question, descriptive analysis and modelling, model selection, and visualisation and interpretation of results. Students will gain hands-on experience of data analysis using R and datasets drawn from a range of social science disciplines.

The course will include the following topics:

• Introduction to multilevel data structures and research questions
• Simple model for a two-level structure; comparison of fixed effects and random effects approaches
• Random intercept models
• Handling level 2 endogeneity in random effects models
• Random slope models
• Higher-level explanatory variables: contextual, between-group and cross-level interaction effects
• Longitudinal data analysis
• Model selection
• Special topics, e.g. models for three-level structures and for non-hierarchical structures

10 hours of computer workshops and 20 hours of lectures in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Students are required to install R on their own laptops for use in the computer workshops.

Students will be expected to produce 4 problem sets in the WT.

Students will work on weekly, structured problems in the computer classes. Students will be expected to submit their answers to four problem sets as homework.  Individual feedback will be given on homework and solutions will be provided at the end of each week for each problem set.

T A B Snijders and R J Bosker, Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd edition, Sage (2011).

S W Raudenbush and A S Bryk, Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd edition, Sage (2002).

J Hox  Multilevel Analysis: Techniques and Applications. Quantitative Methodology Series, 2nd edition. Taylor & Francis (2010).

D M Bates, lme4: Mixed-Effects Modeling with R https://stat.ethz.ch/~maechler/MEMo-pages/lMMwR.pdf

Project (30%) in Winter Term Week 7

Project (70%) in Spring Term Week 1

The first project (30%) will be carried out in groups.   The second project (70%) will be carried out individually. For a random sample of students, follow-up interviews will be conducted for the individual project. The aim of these interviews is to check against the unauthorised use of GenAI and that the work submitted is the student's own.