Joint Schools' Social Sciences course timetable

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

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Introducing students to methods of data analysis that are relevant to spatial data. Discussing nature of Geographic Information Science (GISc), describing how space is conceptualised and represented in a GIS.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is concerned with greater knowledge of regression, through extension of the simple linear model; enabling students to assess the models they use, testing for problems such as collinearity, outliers/leverage, and heteroskdasticity.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research

Introducing students to methods of data analysis that are relevant to spatial data. Discussing nature of Geographic Information Science (GISc), describing how space is conceptualised and represented in a GIS.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is concerned with greater knowledge of regression, through extension of the simple linear model; enabling students to assess the models they use, testing for problems such as collinearity, outliers/leverage, and heteroskdasticity.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research

Introducing students to methods of data analysis that are relevant to spatial data. Discussing nature of Geographic Information Science (GISc), describing how space is conceptualised and represented in a GIS.

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

This foundational course is for eligible graduate students who have no prior training in statistics.

It introduces students to the basic general concepts that underlie descriptive and inferential statistics. It is divided into 4 sessions:

• Session 1: Variables and Measurement
• Session 2: Describing a Variable
• Session 3: Populations and Samples
• Session 4: Statistical Models and Significance Tests

This module is concerned with greater knowledge of regression, through extension of the simple linear model; enabling students to assess the models they use, testing for problems such as collinearity, outliers/leverage, and heteroskdasticity.