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This module will develop understanding of how to quantitatively analyse data in the social sciences, building on the foundations from the research methods modules in the first year. It will be focused around the common methods for data analysis that are needed within the social science and will prepare students for conducting their own research in their dissertations. The module will also satisfy the requirements of the British Psychological Association (BPA) for those who need this in their degree.

The module will use real-world examples to illustrate the methods, giving participants insight into different studies that will be of interest, with examples coming from all disciplinary areas involved, including Psychology. The methods will be taught through flipped learning, with instructional videos placed online, supplemented by lectures and tutorials were problem sets will be considered. A range of further examples, as worksheets and recorded lectures, will be placed online in order to create a comprehensive resource for the methods, with the online material further tailored to different disciplines.

The module will follow the BPA requirements, including the use of the APA style of referencing.

This module will develop understanding of how to quantitatively analyse data in the social sciences, building on the foundations from the research methods modules in the first year. It will be focused around the common methods for data analysis that are needed within the social science and will prepare students for conducting their own research in their dissertations. The module will also satisfy the requirements of the British Psychological Association (BPA) for those who need this in their degree.

The module will use real-world examples to illustrate the methods, giving participants insight into different studies that will be of interest, with examples coming from all disciplinary areas involved, including Psychology. The methods will be taught through flipped learning, with instructional videos placed online, supplemented by lectures and tutorials were problem sets will be considered. A range of further examples, as worksheets and recorded lectures, will be placed online in order to create a comprehensive resource for the methods, with the online material further tailored to different disciplines.

The module will follow the BPA requirements, including the use of the APA style of referencing.

This module is designed to provide you with a broad introduction to the diverse cultures and histories of Spain, Latin America and the Portuguese-speaking or 'Lusophone' world. By studying various types of primary and secondary sources, you will become familiar with a wide range of themes, events and ideas from those regions.

All materials will be available in English, allowing students with little or no knowledge of Spanish and Portuguese to engage with the module.

This module is designed to provide you with a broad introduction to the culture, history and language of Spain, Latin America and the Spanish speaking World. By studying various types of primary and secondary sources, you will become familiar with a wide range of themes, events and ideas from the regions.

All materials will be available in English, allowing students with little or no knowledge of the Spanish language to engage with the module.

This module is designed to provide you with a broad introduction to the culture, history and language of Spain, Latin America and the Spanish speaking World. By studying various types of primary and secondary sources, you will become familiar with a wide range of themes, events and ideas from the regions.

All materials will be available in English, allowing students with little or no knowledge of the Spanish language to engage with the module.

This prepares you for the rest of the programme and so it is intended to support you in concurrent and subsequent modules by developing your skills with the statistical analyses that are used in quantitative approaches to research. You will develop your understanding of how to plan statistical analyses, how to carry out a bivariate statistical analysis, and how to use the results of a statistical analysis to advance scientific knowledge in a given area. You will use all of this knowledge to complete a partially written research report by carrying out a statistical analysis of a real-world educational dataset and then writing-up the results of this analysis in a critical manner.

This prepares you for the rest of the programme and so it is intended to support you in concurrent and subsequent modules by developing your skills with the statistical analyses that are used in quantitative approaches to research. You will develop your understanding of how to plan statistical analyses, how to carry out a bivariate statistical analysis, and how to use the results of a statistical analysis to advance scientific knowledge in a given area. You will use all of this knowledge to complete a partially written research report by carrying out a statistical analysis of a real-world educational dataset and then writing-up the results of this analysis in a critical manner.

The theory and methods of Statistics play an important role in all walks of life, society, medicine and industry. They enable important understanding to be gained and informed decisions to be made, about a population by examining only a small random sample of the members of that population. For example, to decide whether a new drug improves the symptoms of a disease in all those diagnosed as having the condition (the population), a clinical trial might be undertaken in which a sample of people who receive the new drug is compared with a sample receiving no active treatment. Such statistical inferences about a population are subject to uncertainty - what we observe in our particular sample (or samples) may not hold for the whole population. Probability theory and statistical distributions are needed to quantify this uncertainty, and assess the accuracy of our inference about the population. This module aims to lay foundations in probability and distribution theory, data analysis and the use of a statistical software package, which will be built upon in later modules.

The module begins by introducing statistical data analysis by using the freely available R package, https://cran.r-project.org/. Statistical analysis and report writing are discussed along with the use of the R software package for summarising and interpreting data.

It then formally defines probability and studies the key properties. The concepts of random variables as outcomes of random experiments are introduced and the key properties of the commonly used standard univariate random variables are studied. Emphasis is placed on learning the theories by proving key properties of each distribution.

Basic ideas of statistical inference, including techniques of point and interval estimation and hypothesis testing, are introduced and illustrated with practical examples.

The theory and methods of Statistics play an important role in all walks of life, society, medicine and industry. They enable important understanding to be gained and informed decisions to be made, about a population by examining only a small random sample of the members of that population. For example, to decide whether a new drug improves the symptoms of a disease in all those diagnosed as having the condition (the population), a clinical trial might be undertaken in which a sample of people who receive the new drug is compared with a sample receiving no active treatment. Such statistical inferences about a population are subject to uncertainty - what we observe in our particular sample (or samples) may not hold for the whole population. Probability theory and statistical distributions are needed to quantify this uncertainty, and assess the accuracy of our inference about the population. This module aims to lay foundations in probability and distribution theory, data analysis and the use of a statistical software package, which will be built upon in later modules.

The module begins by introducing statistical data analysis by using the freely available R package, https://cran.r-project.org/. Statistical analysis and report writing are discussed along with the use of the R software package for summarising and interpreting data.

It then formally defines probability and studies the key properties. The concepts of random variables as outcomes of random experiments are introduced and the key properties of the commonly used standard univariate random variables are studied. Emphasis is placed on learning the theories by proving key properties of each distribution.

Basic ideas of statistical inference, including techniques of point and interval estimation and hypothesis testing, are introduced and illustrated with practical examples.

This module will develop your critical knowledge and understanding of aspects of the work and lives of teachers in schools and relate this to your own experience. You will develop an understanding of key themes relating to teachers, teaching and classroom practice. Themes might include recent research on effective teaching and teacher development, education policies which affect teachers and portrayals of teachers in popular culture.