There are no pre-requisites for this module to be added

The module introduces you to key elements of the quantitative methodologies. It provides you with an applied understanding of how quantitative research and data function in the real-world, across private, public and business facing organisations. The module is structured into two interlinking parts. In the 1st part you will acquire a working knowledge and understanding of quantitative research, while in the 2nd part you will apply skills of quantitative data collection and analysis using appropriate tools.

The first part will focus on:
• Theoretical and epistemological bases for using numbers in social research, and the premise of where, how, and why to use quantitative data for the understanding of social policy and contemporary issues.
• Benefits and pitfalls of using statistical tools to inform social policy and impact initiatives in: the third sector, public administration, art and culture, academic and research, and business sectors.
• Critical engagement with the application, interpretation, (mis)representation and ethical use of quantitative evidence across private and public sectors, and popular culture.
• The role of ‘Big Data’ and ‘data surveillance’ in the contemporary world and their associated strengths and weaknesses.

The second part will consist of:
• A focus on surveys as the most popular quantitative method in social research (including, for example developing valid and reliable survey questions, piloting, managing non-response rate, engagement, administration).
• Sampling and the logic of ‘sample representation’ of general populations, the challenges of sampling, sampling frames, etc.
• Basic functionality of relevant statistical software (e.g. SPSS or similar) used for generating and analysing quantitative data.
• Key concepts, analytical strategies, and visualisation techniques such as: commonly used techniques for summarising data, and consideration of ‘typical’ and ‘outlier’ data/cases; tables and graphs; variables, mean, medians, value; cross-tabulation, single & bi-variate analysis.
• Exploring the relationships between variables: deciding if measures are linked and how (correlation or causation), introducing elaboration techniques, and measures of association (e.g. the Chi-square and Cramer’s V).

On completion of this module, you will be able to:
1. Understand and evaluate the links between theory and methodology, incl. benefits and limitations of quantitative evidence.
2. Demonstrate the ability to construct a valid and reliable research tool to produce quantitative data.
3. Use and perform basic operations with relevant statistical and online survey software to execute collection and interpretation of data.
4. Critically reflect on the uses and applications of data science, incl. ethical implications when using statistical evidence, or when doing quantitative research.