The course’s main objective is to introduce students to social science research. To do so we will focus on how we use facts and observations to make and evaluate statements about the world and the forces which appear to account for human interactions. What will become quickly evident is that academic discourse and scientific debate is more involved and cumbersome than everyday reasoning.
This module introduces students to quantitative methods in the social sciences. The module covers descriptive statistics (central tendency and variation), data visualisation, data access, probability, sampling, hypothesis testing, inferential statistics and ends with an introduction to simple linear regression. Students will be introduced to the R statistical software and work with real-world data.
This module aims to build skills in applied statistics using a variety of methods, including regression, spatial analysis and quantitative text analysis. It starts with an introduction to multiple regression, advanced survey methods and missing data before going to look at a host of spatial analysis methods. They will then be introduced to a wider range of regression techniques, including models for binary dependent variables, panel data, multilevel models, and multilevel modelling and poststratification.
The module's main objective is to provide students with an introduction to the rapidly growing field of causal inference. Increasingly, social scientists are no longer willing to establish correlations and merely assert that these patterns are causal. Instead, there is a new focus on design-based inference, designing research studies in advance so that they yield causal effects. This module discusses the nature of causation in the social sciences, and goes on to look at some of the most popular research designs in causal analysis.
This module focuses on advanced measurement techniques that are routinely used in industry and academic research. The module covers theories of quantitative measurement as well as practical measurement strategies involving data reduction techniques and latent variable modelling. Examples are taken from the wide variety of social science fields that use these methods. The module provides hands-on training in the application of these measurement strategies in real-world data analysis projects.