Module finder

Modern spectroscopic techniques underpin a wide range of chemical and biological research as well as serving as a valuable analytical tool. This module will introduce some of the key principles, tools and techniques that govern spectroscopic measurements and allow scientists of all disciplines to characterise chemical structure and composition, image biological samples and follow chemical reactions in intricate detail. The module will cover how these techniques can be used for both applied science relevant to biological imaging, as well as more fundamental science for measuring the motion of the atoms and electrons that drive chemical reactivity.

Modern spectroscopic techniques underpin a wide range of chemical and biological research as well as serving as a valuable analytical tool. This module will introduce some of the key principles, tools and techniques that govern spectroscopic measurements and allow scientists of all disciplines to characterise chemical structure and composition, image biological samples and follow chemical reactions in intricate detail. The module will cover how these techniques can be used for both applied science relevant to biological imaging, as well as more fundamental science for measuring the motion of the atoms and electrons that drive chemical reactivity.

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 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 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 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 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.