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A 2 day course: 19th - 20th February 2018

The aim of this module is to introduce how to analyse data that has a multi-level, hierarchical structure. The mathematical form of multilevel models is described. The models are developed first for continuous outcomes moving from linear regression to the random intercept model to the random coefficient model. Multilevel models are then shown for binary and other outcomes. Software implementation is described with the lme4 package in R. Some use of MLwiN is also made.

Important please note:

If the course is fully booked please do complete the registration, as then you will be placed on a waiting list. You will be allocated a place if more places become available or if people cancel.

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

Details of course fees.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.

Accommodation Details 

 

Date:
Monday, February 19, 2018
Time:
All Day Event
Location:
Postgraduate Statistics Centre
Presenter:
Dr Tom Palmer
Type:
Taught Session - For Externals, Staff & Students
Categories:
Multi-Level Models

Topics covered will include:

  • The intra class correlation coefficient.
  • Two level random intercept and random coefficient models with continuous outcomes.
  • Checking model assumptions and residual diagnostics.
  • Models with three or more levels.
  • Generalized multilevel models including two-level logistic regression models, multilevel ordinal logistic regression models, and multilevel Poisson regression models.
  • Worked examples are shown of fitting such models in statistical software (mainly in R, but also some in MLwiN).
  • Students will also gain insight into that there are different estimation algorithms available for multilevel models.

On successful completion students will be able to:

  • Comprehend the notation used to describe multilevel models
  • Demonstrate knowledge of multilevel models by formulating appropriate models to answer specific questions
  • Demonstrate and understand how to use statistical software to fit multilevel models and how to interpret the relevant output
  • Demonstrate how to perform model diagnostics for such models
  • Be able to interpret the results of fitting multilevel models.
Bibliography:
  • Bryk, A. S., and Raudenbush, S. W., (1992) Hierarchical Linear Models, Sage.
  • Goldstein, H., (2003) Multilevel Statistical Models. London, Edward Arnold
  • Holmes Finch W, Bolin JE, Kelley K. Multilevel Modeling Using R. Chapman & Hall. 2014
  • Hox, J., (2002) Multilevel Analysis: Techniques and Applications, Malwah, N.J: Lawrence Erlbourn Associates.
  • Longford, N. T., (1993) Random Coefficient Models. Oxford University Press.
  • Snijders, T. A. B., and Bosker, R. J., (1999) Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.
 

 



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