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The Advanced Skills Portfolio documents your development in and mastery of a range of subject-specific, transferable and generic skills during the first two years of the research phase of the IPhD Applied Linguistics/English Language Teaching programme. It is compiled as part of your preparation for your PhD Confirmation, building on a range of different learning activities and experiences undertaken during this doctoral programme. You will be invited to start planning your Advanced Skills Portfolio in your first year of the programme.

Through your ongoing preparation of the Advanced Skills Portfolio over parts two and three of the IPhD, you will reflect critically on your developing research expertise, professional training, personal development, and employability. This will help identify areas of your development to focus on in the latter stages of the doctoral journey, supporting you in the transition to a future career as an academic, a researcher, a teacher, or in other employment sectors. The written portfolio is formally assessed.

The human sciences are evolving fast to incorporate new forms of data and powerful new analysis tools.
Advances in machine learning have allowed huge improvements in our ability to predict individual characteristics and preferences, while our interactions with networked devices and online stores and services produce ‘digital breadcrumbs’ that can lead us to new insights about behaviour. The types of sources which social researchers now investigate include data on trends in search terms, online review databases, and many others.
At the same time, these methods and data sources generate important ethical issues that we must consider.
This module will provide students with crucial skills in data manipulation and visualisation, programming and the application of machine learning methods to social data. These skills have wide-ranging applications in research business, and the public sector.

The human sciences are evolving fast to incorporate new forms of data and powerful new analysis tools.
Advances in machine learning have allowed huge improvements in our ability to predict individual characteristics and preferences, while our interactions with networked devices and online stores and services produce ‘digital breadcrumbs’ that can lead us to new insights about behaviour. The types of sources which social researchers now investigate include data on trends in search terms, online review databases, and many others.
At the same time, these methods and data sources generate important ethical issues that we must consider.
This module will provide students with crucial skills in data manipulation and visualisation, programming and the application of machine learning methods to social data. These skills have wide-ranging applications in research business, and the public sector.

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.

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 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.