Theme: Advanced Statistics

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.

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.

Module is designed to teach students how to analyse different types of data using SPSS; including outputs, conducting diagnostic tests, calculating effect sizes and make predictions.

Introduction to statistical techniques of Exploratory and Confirmation Factor Analyss. EFA is used to uncover the latent structure of a set of variables. CFA examines whether collected date correspond to a model of what the data are meant to measure. AMOS will be introduced as a powerful tool to conduct confirmatory factor analysis.

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 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.

The module introduces time series techniques relevant to forecasting in social science research and computer implementation of the methods.

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