This is a postgraduate advanced module on statistical modelling with an emphasis on how to apply different methods to real world problems. In particular, approaches to handle hierarchical data structure (i.e. multi-level data) are the main focus of the module. Theory is introduced to ensure understanding, and real cases study are presented to learn how to apply the theory and overcome the challenges of real data. Statistical software is used to perform statistical analysis.
Having successfully completed this module you will be able to:
oRandom intercepts and random slopes
oContextual variables
oCross-level interactions
oHeterogeneous variance structures
oBinary, counts, and continuous responses.
oEstimation
oTesting
oDiagnostic checking (specification issues and residual analysis)
oModel selection
Teaching is delivered through lectures, computer workshops, and tutorials.
| Type | Hours |
|---|---|
| Independent Study | 108 |
| Workshops | 10 |
| Tutorial | 10 |
| Lecture | 22 |
| Total study time | 150 |
Agresti, A. (2013). (2013). Categorical Data Analysis.. Wiley.
Bryk, A.S. and Raudenbush, S.W (1992, 2002). Hierarchical Linear Models: Applications and Data Analysis Methods.. Newbury Park, CA: Sage.
Singer, Judith D., Willett, John B (2003). Applied Longitudinal Data Analysis: Modelling Change andEvent Occurrence.. New York: Oxford University Press.
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
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.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Repeat type: Internal & External