Further Topics in Statistical Modelling

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Employ Multilevel (Mixed effects) and Marginal models for the analysis of hierarchical and longitudinal data.
• Interpret the results of statistical analyses in non-technical language.
• Recognise the basic statistical theory underpinning the models presented in the course.
• Use appropriate statistical tools for assessing goodness of fit and choosing among competing modelling techniques.
• Contrast statistical modelling techniques widely used for predictive purposes.

• Main statistical models for Hierarchical/Longitudinal data
• Multilevel models: Random intercepts and Random slopes, use of contextual variables
• Multilevel and Marginal models for Longitudinal data
• Multilevel models for a binary response
• The predictive approach. Cross-validation and the Bootstrap
• Additional modelling techniques. E.g., non-parametric regression, penalised regression, GAMs, CART

Teaching and learning methods

Teaching will be delivered by a mixture of synchronous and asynchronous online methods, which may include lectures, quizzes, discussion boards, workshop activities, exercises, and videos. A range of resources will also be provided for further self-directed study. Face-to-face teaching opportunities will be explored depending on circumstances and feasbility.

Resources & Reading list

General Resources

Laboratory space and equipment required. Students require access to computer lab with R and MLwiN.

Textbooks

Wood, S.N. (2017). Generalized Additive Models: An Introduction with R. . CRC Press.

Goldstein, H. (2011). Multilevel Statistical Models. London: Wiley.

Diggle, P. J., Liang, K-Y. and Zeger, S. L. (1994, 2001). The Analysis of Longitudinal Data. Oxford: Clarendon Press.

James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An introduction to statistical learning with applications in R.. Springer .

Faraway, J.J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC Press.

Singer, Judith D., Willett, John B. (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence. New York: Oxford University Press.

Snijders, T.A.B. and Bosker, R.J. (1999, 2012). Multilevel Analysis. London: Sage.

Assessment strategy

Teaching will be delivered by a mixture of lectures and computer workshops in R, where you will have the opportunity to practice each of the topics covered.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External