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This module focuses on the application of statistical methods specially developed for epidemiological study data. Topics include the basic disease occurrence measures of prevalence and incidence with their role in surveillance including standardization, Mantel-Haenszel estimation of various effect measures including the risk ratio and risk difference for cohort studies and the odds ratio for case-control studies as well as Poisson and logistic regression to adjust for potential confounders simultaneously. The module also includes elements of time-to-event analysis including Kaplan-Meier estimation and Cox' proportional hazards model for confounder adjustment. Finally, basic concepts of statistical methods for meta-analysis will be introduced. The module includes a mixture of lectures and practical workshops using the software STATA.

This module focuses on the application of statistical methods specially developed for epidemiological study data. Topics include the basic disease occurrence measures of prevalence and incidence with their role in surveillance including standardisation, Mantel-Haenszel estimation of various effect measures including the risk ratio and risk difference for cohort studies and the odds ratio for case-control studies as well as Poisson and logistic regression to adjust for potential confounders simultaneously. The module also includes elements of time-to-event analysis including Kaplan-Meier estimation and Cox' proportional hazards model for confounder adjustment. Finally, basic concepts of statistical methods for meta-analysis will be introduced. The module includes a mixture of lectures and practical workshops using the software STATA.

This module is divided into two components that focus on cutting-edge statistical techniques.

The first half focuses on Structural Equation Models, covering Path Analysis, Confirmatory Factor Analysis, Structural Equation Modelling, Multigroup Models and Latent Mean Structures.

The second half focuses on Hierarchical (Multilevel/Mixed) Linear Models, which is appropriate for nested data (e.g., certain repeated-measures designs, students nested within schools, romantic couples, or individuals within groups).

In addition to the readings below, a Blackboard site will be maintained throughout the Semester, where lecture slides, additional readings, and datasets will be available.

Software manuals
Arbuckle, J. L. (2017). Amos 24: User’s guide. Chicago: SPSS.

SEM Textbooks
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Hove, UK: Routledge.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). London: Guilford.

Multilevel Textbooks:
Bickel, R. (2007). Multilevel analysis for applied research: It's just regression. London: Guilford Press.

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal modeling with IBM SPSS. New York: Routledge.

Hox, J. (2010). Multilevel analysis: Techniques and application. (2nd ed). Abingdon: Routledge.

This module is divided into two components that focus on cutting-edge statistical techniques.

The first half focuses on Structural Equation Models, covering Path Analysis, Confirmatory Factor Analysis, Structural Equation Modelling, Multigroup Models and Latent Mean Structures.

The second half focuses on Hierarchical (Multilevel/Mixed) Linear Models, which is appropriate for nested data (e.g., certain repeated-measures designs, students nested within schools, romantic couples, or individuals within groups).

In addition to the readings below, a Blackboard site will be maintained throughout the Semester, where lecture slides, additional readings, and datasets will be available.

Software manuals
Arbuckle, J. L. (2017). Amos 24: User’s guide. Chicago: SPSS.

SEM Textbooks
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Hove, UK: Routledge.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). London: Guilford.

Multilevel Textbooks:
Bickel, R. (2007). Multilevel analysis for applied research: It's just regression. London: Guilford Press.

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal modeling with IBM SPSS. New York: Routledge.

Hox, J. (2010). Multilevel analysis: Techniques and application. (2nd ed). Abingdon: Routledge.

This module is divided into two components that focus on cutting-edge statistical techniques.

The first half focuses on Structural Equation Models, covering Path Analysis, Confirmatory Factor Analysis, Structural Equation Modelling, Multigroup Models and Latent Mean Structures.

The second half focuses on Hierarchical (Multilevel/Mixed) Linear Models, which is appropriate for nested data (e.g., certain repeated-measures designs, students nested within schools, romantic couples, or individuals within groups).

In addition to the readings below, a Blackboard site will be maintained throughout the Semester, where lecture slides, additional readings, and datasets will be available.

Software manuals
Arbuckle, J. L. (2017). Amos 24: User’s guide. Chicago: SPSS.

SEM Textbooks
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Hove, UK: Routledge.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). London: Guilford.

Multilevel Textbooks:
Bickel, R. (2007). Multilevel analysis for applied research: It's just regression. London: Guilford Press.

Heck, R. H., Thomas, S. L., & Tabata, L. N. (2010). Multilevel and longitudinal modeling with IBM SPSS. New York: Routledge.

Hox, J. (2010). Multilevel analysis: Techniques and application. (2nd ed). Abingdon: Routledge.

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.

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.

This module introduces students to multilevel methods for analysing hierarchical structures in the context of cross-sectional and longitudinal data. It also provides students with an understanding of some key methods of hierarchical data analysis, how to apply these methods and how to interpret the results using suitable statistical software.

Modern civil engineers must design structures that are not only safe and durable, but also low-carbon and resource-efficient. In this module, you will learn how to design real structural components made from reinforced concrete, prestressed concrete, steel–concrete composite systems, and timber.
The module focuses on how different materials behave under load and how engineers ensure structures are strong, stiff, and fit for purpose throughout their service life. You will learn to design structural members subjected to bending, shear, axial load, and torsion, and to check serviceability issues such as cracking and deflection. Alongside structural safety, you will explore how design choices influence embodied carbon and how engineers can reduce environmental impact while maintaining performance.
By the end of the module, you will be able to carry out advanced design calculations, identify critical failure modes, and make informed, responsible engineering decisions. This module prepares you for advanced design modules, final-year projects, and professional engineering practice.

Modern civil engineers must design structures that are not only safe and durable, but also low-carbon and resource-efficient. In this module, you will learn how to design real structural components made from reinforced concrete, prestressed concrete, steel–concrete composite systems, and timber.
The module focuses on how different materials behave under load and how engineers ensure structures are strong, stiff, and fit for purpose throughout their service life. You will learn to design structural members subjected to bending, shear, axial load, and torsion, and to check serviceability issues such as cracking and deflection. Alongside structural safety, you will explore how design choices influence embodied carbon and how engineers can reduce environmental impact while maintaining performance.
By the end of the module, you will be able to carry out advanced design calculations, identify critical failure modes, and make informed, responsible engineering decisions. This module prepares you for advanced design modules, final-year projects, and professional engineering practice.