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Introduction to Applied Multilevel Analysis - HEA00039M

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  • Department: Health Sciences
  • Module co-ordinator: Dr. Mona Kanaan
  • Credit value: 10 credits
  • Credit level: M
  • Academic year of delivery: 2020-21

Related modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching cycle
A Autumn Term 2020-21

Module aims

To equip students with the necessary skills and knowledge to allow analysis of multilevel data. By means of lectures and hands-on analysis of data from real studies using the statistical software package STATA. The student is guided through a range of statistical techniques that can be used based on the nature of the outcome when the data follow a multilevel structure.

Module learning outcomes

At the end of the module, the student should be able to:

  1. Identify when to use a multilevel regression approach versus ordinary regression approach.
  2. Identify different types of multilevel structures.
  3. Distinguish between levels and variables and between fixed and random effects.
  4. Distinguish between different outcomes and correspondingly carry out the appropriate multilevel regression.
  5. Demonstrate, by example, how to formulate a multilevel model and how to interpret the results obtained from fitting the model.
  6. Use multilevel modelling for the analysis of cluster randomised trials.
  7. Use a statistical package to carry out analyses.

Academic and graduate skills

Students will be able to carry out multilevel data analysis on a number of outcomes using a statistical package and critically read research papers that use multilevel analysis.

Module content

Prior to commencing the module, students are expected to demonstrate knowledge of ordinary linear, logistic, and survival regression methods and ability to carry these regressions.

 

Subject content

Revision of Generalised Linear Models

Topics to include multiple linear regression, logistic regression, Poisson regression, and Survival Analysis

 

What is Multilevel modelling?

Introduce a range of multilevel structures, e.g nesting and cross classification, with examples from real studies, introduce how to represent multilevel structures using subscripts, distinguish between levels and variables, and fixed and random effects.

 

Multilevel modelling for a continuous outcome

Introduce the random intercept and the random slope models pointing out why standard linear regression does not work in the case of multilevel structures together with the assumptions underlying these models and sample size issues.

 

Multilevel modelling for a binary outcome and count data

Extend the random intercept and the random slope models to the case of a binary response and count data focusing on how to interpret the models and introduce the latent variable approach in the case of binary outcome.

 

Multilevel modelling for time-to-event data

Introduce the discrete time approach to analyse time-to-event data and extend the model to the multilevel case.

 

Multilevel modelling in cluster randomised trials

A brief introduction to cluster randomised trials and how to analyse them using multilevel modelling also hinting to other methods used to analyse cluster randomised trials.

Assessment

Task Length % of module mark
Open Examination
Introduction to Applied Multilevel Analysis
7 hours 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Open Examination
Introduction to Applied Multilevel Analysis
7 hours 100

Module feedback

Students are provided with collective exam feedback relating to their cohort, within the timescale specified in the programme handbook.

Indicative reading

  • Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models: Analytical Methods for Social Research. New York, Cambridge University Press.
  • Goldstein, H. (2010). Multilevel Statistical Models. 4th edn. Singapore: Wiley Series in Probability and Statistics.
  • Klar, N. and Donner, A. (2000). Design and Analysis of Cluster Randomisation Trials in Health Research. Wiley-Blackwell.
  • Leyland, A.H. and Goldstein, H. (2001). Multilevel modelling of health statistics. Chichester: Wiley Series in Probability and Statistics.
  • Rabe-Hesketh, S. and Skrondal, A. (2008). Multilevel and Longitudinal Modeling Using Stata. 2nd edn. USA: Stata Press.



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.

Coronavirus (COVID-19): changes to courses

The 2020/21 academic year will start in September. We aim to deliver as much face-to-face teaching as we can, supported by high quality online alternatives where we must.

Find details of the measures we're planning to protect our community.

Course changes for new students