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Undergraduate Module Descriptor

SSI2005: Data Analysis in Social Science 2

This module descriptor refers to the 2019/0 academic year.

Module Aims

You will learn the strengths and weaknesses of the OLS regression model from a classical statistics perspective. Using a combination of lectures, practical demonstrations and practical assignments, this module aims at developing your skills in the analysis and presentation of quantitative data. Specifically, you will learn how to construct data sets from individual and aggregate level data, how to analyse these data using the appropriate statistical tools – ranging from simple t tests for the comparison of means to more complex multivariate regression analysis - and how to best display summary statistics and estimation results using relevant techniques for the visual – e.g., graphical - display of data. The module will adopt a “hands on” approach, with particular emphasis on applied data analysis and on computational aspects of quantitative social science research

Intended Learning Outcomes (ILOs)

This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.

On successfully completing the programme you will be able to:
Module-Specific Skills1. Recognise and evaluate in writing the diversity of specialised techniques and approaches involved in analysing quantitative data in political science, sociology and criminology
2. Use statistical analysis to test research hypothesis
3. Present and summarise analysed data in a coherent and effective manner
4. Demonstrate acquired skills, confidence and competence in a computer package for statistical analysis (e.g. Excel and R)
Discipline-Specific Skills5. Understand and use the tools and techniques of quantitative research for the analysis of political and social data
6. Use statistical evidence to empirically evaluate the (relative) validity of political, sociological and criminological theories and hypothesis
7. Construct well thought out and rigorous data analysis, tables and reports for both written and oral presentation
8. Examine relationships between theoretical concepts with real world empirical data
Personal and Key Skills9. Study independently
10. Use IT – and, in particular, statistical software packages - for the retrieval, analysis and presentation of information
11. Work independently, within a limited time frame, and without access to external sources, to complete a specified task.